Register event
49 event found

Organiser: ARDC  or Sydney Informatics Hub  or Intersect Australia 

  • Introduction to Machine Learning using Python: Classification at UOA Online

    4 - 5 October 2022

    Introduction to Machine Learning using Python: Classification at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-uoa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2022-10-04 09:30:00 UTC 2022-10-05 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Longitudinal Trials with REDCap at LTU Online

    4 October 2022

    Longitudinal Trials with REDCap at LTU Online https://dresa.org.au/events/longitudinal-trials-with-redcap-at-ltu-online-fab74bbb-894b-4e69-8157-2498a9138206 REDCap is a powerful and extensible application for managing and running longitiudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shephard your participants through a complex survey at various time points with very little configuration. This course will introduce some of REDCap's more advanced features for running longitudinal studies, and builds on the foundational material taught in REDCAP101 - Managing Data Capture and Surveys with REDCap. #### You'll learn: - Build a longitudinal project - Manage participants throughout multiple events - Configure and use Automated Survey Invitations - Use Smart Variables to add powerful features to your logic - Take advantage of high-granularity permissions for your collaborators - Understand the data structure of a longitudinal project #### Prerequisites: This course requires the participant to have a fairly good basic knowledge of REDCap. To come up to speed, consider taking our [Data Capture and Surveys with REDCap](https://intersect.org.au/training/course/redcap101/) workshop. **For more information, please click [here](https://intersect.org.au/training/course/redcap201).** 2022-10-04 10:00:00 UTC 2022-10-04 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Data Entry and Processing in SPSS at UC Online

    4 - 5 October 2022

    Data Entry and Processing in SPSS at UC Online https://dresa.org.au/events/data-entry-and-processing-in-spss-at-uc-online-2ad49709-9422-4151-b2b5-47e7e0ba5976 This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in SPSS and perform visualization. This workshop is recommended for researchers and postgraduate students who are new to SPSS or Statistics; or those simply looking for a refresher course before taking a deep dive into using SPSS, either to apply it to their research or to add it to their arsenal of eResearch skills. #### You'll learn: - Navigate the SPSS working environment - Prepare data files and define variables - Enter data in SPSS and Import data from Excel - Perform data screening - Compose SPSS Syntax for data processing - Obtain descriptive statistics, create graphs & assess normality - Manipulate and transform variables #### Prerequisites: In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/spss101).** 2022-10-04 13:30:00 UTC 2022-10-05 16:30:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Design and Analysis of Surveys 1: Best Practice

    5 October 2022

    Design and Analysis of Surveys 1: Best Practice https://dresa.org.au/events/design-and-analysis-of-surveys-1-best-practice This workshop is full of practical tips and guidelines on how to design, field and analyse standard surveys. Some of the topics considered will be: how to design a survey, line vs discrete scales, the effect of colour, optimal discrete/LIKERT scales, pros and cons of common analyses (e.g. linear and logistic regression) and optimal ways to export data. The material is software agnostic and can be applied in any software. This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person:** Choose the "In person" ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-05 09:30:00 UTC 2022-10-05 11:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Design and Analysis of Surveys 1: Advanced Topics

    5 October 2022

    Design and Analysis of Surveys 1: Advanced Topics https://dresa.org.au/events/design-and-analysis-of-surveys-1-advanced-topics This workshop builds on the material in Surveys 1. It explores questionnaire validation and index creation using methods such as: Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) using Structural Equation Modelling (SEM), and Conjoint models such as Choice modelling. The material is software agnostic and can be applied in any software. This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person:** Choose the "In person" ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-05 11:30:00 UTC 2022-10-05 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Design and Analysis of Surveys 2: Advanced Topics

    5 October 2022

    Design and Analysis of Surveys 2: Advanced Topics https://dresa.org.au/events/design-and-analysis-of-surveys-2-advanced-topics This workshop builds on the material in Surveys 1. It explores questionnaire validation and index creation using methods such as: Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) using Structural Equation Modelling (SEM), and Conjoint models such as Choice modelling. The material is software agnostic and can be applied in any software. This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person:** Choose the "In person" ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-05 11:30:00 UTC 2022-10-05 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Multivariate Statistical Analysis 1: Dimension Reduction

    5 October 2022

    Multivariate Statistical Analysis 1: Dimension Reduction https://dresa.org.au/events/multivariate-statistical-analysis-1-dimension-reduction-5209a7a6-36ba-4a48-923d-58f236456361 **Multivariate Statistical Analysis 1: Dimension Reduction** In multivariate statistics we simultaneously model and estimate variability in more than one variable often in order to examine the relationship between variables. This workshop examines the key aspects of moving from univariate to multivariate analysis, and the situations and scenarios where multivariate analysis is typically applied. The focus will be on practical application of concepts through examples. **Topics covered will include:** - Motivations for undertaking multivariate analysis - Statistical principles for multivariate analysis - Dimension reduction techniques including principal components analysis (PCA), factor analysis (FA), correspondence analysis (CA) and non-metric multidimensional scaling (nMDS) This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Pre-requisites** Prior experience with statistical modelling is assumed, as the basics of regression modelling will not be covered. Please consider attending Linear Models 1 and/or Linear Models 2 workshops to come up to speed beforehand. Note that this workshop does not require knowledge of or use of specific statistics software. The analysis methods may be performed using a wide range of commonly available software. **Attendance options** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person**: Choose the _**In person**_ ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online**: Choose the _**Online Zoom**_ ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - Open to: Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey) - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. **Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!!** 2022-10-05 14:00:00 UTC 2022-10-05 15:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: Introduction & Linear Regression at UniSA Online

    6 - 7 October 2022

    Introduction to Machine Learning using R: Introduction & Linear Regression at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-introduction-linear-regression-at-unisa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use R and and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r205).** 2022-10-06 09:30:00 UTC 2022-10-07 12:30:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Intro to REDCap (webinar)

    6 October 2022

    Intro to REDCap (webinar) https://dresa.org.au/events/intro-to-redcap-webinar Research Electronic Data Capture (REDCap) is a secure web-based database application maintained by the University of Sydney. It is ideal for collecting and managing participant data and administering online surveys, with features supporting longitudinal data collection, complex team workflows and exports to a range of statistical analysis programs. **In "Intro to REDCap", we will cover:** - How to build a simple data entry project - How to collect data - How to add users/collaborators - How to export data Please note we will **not cover surveys**, as these are covered separately in our "Surveys in REDCap" training session. **Target audience:** Students and staff at the University of Sydney who need to collect electronic data or responses from participants into a structured database. **Open to:** Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey). If you do not have a UniKey, please contact us to confirm your position after registration. Please use your University of Sydney email address to register (e.g. first.last@sydney.edu.au, abcd1234@uni.sydney.edu.au) **Accessing the webinar:** Your confirmation **email from Eventbrite** will contain a button which will provide the details to access the Zoom webinar **For more information:** digital.research@sydney.edu.au Sydney Informatics Hub offers a broad range of training to researchers at the University. Please see our [training site](https://sydney.edu.au/research/facilities/sydney-informatics-hub/workshops-and-training.html)for more information. 2022-10-06 10:00:00 UTC 2022-10-06 12:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Excel for Researchers at Deakin Online

    6 - 7 October 2022

    Excel for Researchers at Deakin Online https://dresa.org.au/events/excel-for-researchers-at-deakin-online-bab9bfd3-9f50-4b1a-bd93-612966314ddd Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-06 13:00:00 UTC 2022-10-07 16:00:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UTS Online

    6 October 2022

    Getting started with NVivo for Windows at UTS Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-uts-online-b28faf2f-9fd5-4807-b81e-314757189cfc Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2022-10-06 13:30:00 UTC 2022-10-06 16:30:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Version Control with Git at UC Online

    6 October 2022

    Version Control with Git at UC Online https://dresa.org.au/events/version-control-with-git-at-uc-online Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available. Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - keep versions of data, scripts, and other files - examine commit logs to find which files were changed when - restore earlier versions of files - compare changes between versions of a file - push your versioned files to a remote location, for backup and to facilitate collaboration #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/git101).** 2022-10-06 13:30:00 UTC 2022-10-06 16:30:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at Western Sydney: Online

    7 October 2022

    Getting started with NVivo for Windows at Western Sydney: Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-western-sydney-online-553d211b-e146-4d09-9cd0-e8deed4614fb Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2022-10-07 09:30:00 UTC 2022-10-07 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UTS Online

    7 October 2022

    Getting started with NVivo for Windows at UTS Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-uts-online-3b35237e-2329-460c-b21d-edb3e4005185 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2022-10-07 13:30:00 UTC 2022-10-07 16:30:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    11 October 2022

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2022-10-11 09:30:00 UTC 2022-10-11 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UNE Online

    11 October 2022

    Excel for Researchers at UNE Online https://dresa.org.au/events/excel-for-researchers-at-une-online Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-11 09:30:00 UTC 2022-10-11 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: Introduction & Linear Regression at UniSA Online

    11 - 12 October 2022

    Introduction to Machine Learning using Python: Introduction & Linear Regression at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-introduction-linear-regression-at-unisa-online-fbdbdb9f-7f87-4cef-94dd-ed271edf03b7 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python205).** 2022-10-11 13:00:00 UTC 2022-10-12 16:00:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UNE Online

    12 October 2022

    Excel for Researchers at UNE Online https://dresa.org.au/events/excel-for-researchers-at-une-online-c1d0c270-e1dd-4b1a-b283-8a4a1dadbfd0 Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-12 09:30:00 UTC 2022-10-12 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Collecting Web Data at UC Online

    12 - 13 October 2022

    Collecting Web Data at UC Online https://dresa.org.au/events/collecting-web-data-at-uc-online-1abf7b55-140d-4eea-a38c-22d682f9ebc5 Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated. Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it's a skill worth learning. So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - The concept of structured data - The use of XPath queries on HTML document - How to scrape data using browser extensions - How to scrape using Python and Scrapy - How to automate the scraping of multiple web pages #### Prerequisites: A good knowledge of the basic concepts and techniques in Python. Consider taking our [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to come up to speed beforehand. **For more information, please click [here](https://intersect.org.au/training/course/webdata201).** 2022-10-12 09:30:00 UTC 2022-10-13 16:30:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at LTU Online

    12 - 13 October 2022

    Excel for Researchers at LTU Online https://dresa.org.au/events/excel-for-researchers-at-ltu-online-2a214127-b203-4bfb-940d-09b19a3b34b6 Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-12 09:30:00 UTC 2022-10-13 12:30:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Introduction to Artemis HPC

    12 October 2022

    Introduction to Artemis HPC https://dresa.org.au/events/introduction-to-artemis-hpc ### **Sydney Informatics Hub Research Computing Training Series** ## Introduction to the Artemis High Performance Computer (HPC) **Please note: OWN LAPTOP REQUIRED** with a terminal client installed. **Essential pre-requisites**: Competency on the Unix/Linux command line. If you are interested in learning HPC but have no Unix/Linux command-line skills, you should first take an 'Introduction to Unix/Linux' course. We will go through the basics if you have no experience. **Synopsis:** Learn about the University of Sydney’s HPC ‘Artemis’, including directory structure, software, and how to submit and monitor compute jobs using the PBS Pro scheduling software. Also learn about the national HPC facilities and cloud services available.  Artemis is available at no cost to University of Sydney staff and students. **Target audience:** Students and staff who would like to learn how to run compute jobs on Artemis HPC. Participants must have a valid USYD unikey. **Follow-on courses:** It is recommended to register for the Data transfer and RDS for HPC course to learn how to move data to Artemis. **Course notes and additional courses:** [https://sydney-informatics-hub.github.io/training.artemis/](https://sydney-informatics-hub.github.io/training.artemis/) **For more information:** Email sih.training@sydney.edu.au  **Sign up for email alerts about training:** [https://mailman.sydney.edu.au/mailman/listinfo/computing\_training](https://mailman.sydney.edu.au/mailman/listinfo/computing_training) 2022-10-12 10:00:00 UTC 2022-10-12 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: Classification at UniSA Online

    13 - 14 October 2022

    Introduction to Machine Learning using R: Classification at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-classification-at-unisa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r206).** 2022-10-13 09:30:00 UTC 2022-10-14 09:30:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    13 October 2022

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online-c4738fe2-2523-4a1f-baae-cdfb09e36ac2 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2022-10-13 09:30:00 UTC 2022-10-13 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Surveys in REDCap (webinar)

    13 October 2022

    Surveys in REDCap (webinar) https://dresa.org.au/events/surveys-in-redcap-webinar Research Electronic Data Capture (REDCap) is a secure web-based database application maintained by the University of Sydney. It is ideal for collecting and managing participant data and administering online surveys, with features supporting longitudinal data collection, complex team workflows and exports to a range of statistical analysis programs. **In "Surveys in REDCap", we will cover:** - How to turn a data collection instrument into a survey - How to distribute surveys - How to work with multiple surveys within a project (e.g. Survey Queue, Automated Survey Invitation features) **Essential pre-requisites:** You must know how to build a basic data entry project in REDCap. Please attend our "Intro to REDCap" webinar to cover this. **Target audience:** Students and staff at the University of Sydney who need to collect electronic data or responses from participants into a structured database. **Open to:** Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey). If you do not have a UniKey, please contact us to confirm your position after registration. Please use your University of Sydney email address to register (e.g. first.last@sydney.edu.au, abcd1234@uni.sydney.edu.au) **Accessing the webinar:** Your confirmation **email from Eventbrite** will contain a button which will provide the details to access the Zoom webinar **For more information:** digital.research@sydney.edu.au Sydney Informatics Hub offers a broad range of training to researchers at the University. Please see our [training site](https://sydney.edu.au/research/facilities/sydney-informatics-hub/workshops-and-training.html)for more information. 2022-10-13 10:00:00 UTC 2022-10-13 12:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • From PC to Cloud or High Performance Computing at UC Webinar

    14 October 2022

    From PC to Cloud or High Performance Computing at UC Webinar https://dresa.org.au/events/from-pc-to-cloud-or-high-performance-computing-at-uc-webinar Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well. We may refer to both types as “large scale computing” - but what is the difference? Both systems target scalability of computing, but in different ways. This webinar will give a good overview to the researchers thinking to make a move from their local computer to Cloud of High Performance Computing Cluster. #### You'll learn: - Introduction - HPC vs Cloud computing - When to use HPC - When to use the Cloud - The Cloud – Pros and Cons - HPC – Pros and Cons #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/compute001).** 2022-10-14 10:00:00 UTC 2022-10-14 11:30:00 UTC Intersect Australia Australia Australia Webinars training@intersect.org.au [] [] [] open_to_all []
  • Introduction to Machine Learning using Python: Classification at UniSA Online

    18 - 19 October 2022

    Introduction to Machine Learning using Python: Classification at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-unisa-online-27cb5a67-71b5-46bd-beaa-a132c09400db Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2022-10-18 09:30:00 UTC 2022-10-19 12:30:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Meta-Analysis – An Introduction

    19 October 2022

    Meta-Analysis – An Introduction https://dresa.org.au/events/meta-analysis-an-introduction-ebfd3995-3fc5-4e57-bde6-0c113baec414 **Meta Analysis - An Introduction, run by the Sydney Informatics Hub.** This workshop provides research students with a theoretical and practical introduction to meta-analysis as part of a systematic review. In this workshop we will be examining the process of performing a meta-analysis in particular focusing on key statistical concepts such as heterogeneity and Fixed and Random effects modelling. The available choices of statistical software will be discussed and participants will be shown worked examples using the **metafor** package in R. A basic knowledge of R software is desirable, but not necessary since participants are not expected to produce and run their own code during the workshop. **Attendance options:** **In person:** Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online**: Choose the **_Online Zoom_** ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!! 2022-10-19 09:30:00 UTC 2022-10-19 11:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Linear Models 3: Advanced topics, tips and tricks

    19 October 2022

    Linear Models 3: Advanced topics, tips and tricks https://dresa.org.au/events/linear-models-3-advanced-topics-tips-and-tricks-255bce7c-f07c-41f5-ab50-de1dbf538b86 **Linear Models 3: Advanced topics, tips and tricks** Discusses topics such as: - Reporting and Interpretation (estimated marginal means, confidence vs prediction intervals, applying and correcting for multiple comparisons, reporting variable \*Importance\*, plus other reporting and interpretation tricks) - Model Parametrisation using the Design Matrix (interpreting categorical predictor parameters, dummy coding, effects coding) - More on Mixed Models (introducing the random slope) This is the third of 3 workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic/binary and count (Poisson) regression. Each one builds on the preceding workshops and together they show how all these analyses can be performed using the same easy to understand Generalised Linear Mixed Model (GLMM) framework and workflow. As well as how they can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. There is also a 4th complementary workshop called Statistical Model Building which we recommend for those experienced with Linear Models or who have done at least the first 2 of our workshops. The material is organised around Statistical Workflows applicable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results. **Attendance options:** - **In person**: Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. - **Online**: Choose the _**Online Zoom**_ ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. **Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!!** 2022-10-19 11:30:00 UTC 2022-10-19 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Statistical Model Building

    19 October 2022

    Statistical Model Building https://dresa.org.au/events/statistical-model-building-a41d5acd-9e17-4478-a400-b3a6954f19c9 This workshop examines and illustrates the key aspects and strategies of statistical model building to help you answer your research question, avoid common pitfalls, erroneous models and incorrect conclusions. Appropriate statistical model building will help you to gain knowledge as opposed to simply getting the best prediction (although that can be a goal as well). We will focus on concepts such as variable selection, multi-collinearity, interactions, selecting a model building strategy, comparing models and evaluating models. In general, these concepts are useful for any statistical model building. This workshop will provide generalised linear regression model examples. The focus will be on practical application of concepts so mathematical descriptions will be kept to a minimum. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person @Camperdown:** Choose the "In person" ticket option. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Open to: Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey) - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-19 14:00:00 UTC 2022-10-19 15:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • AgReFed Data Harvester Workshop

    25 October 2022

    AgReFed Data Harvester Workshop https://dresa.org.au/events/agrefed-data-harvester-workshop ### Sydney Informatics Hub presents: ## AgReFed Data-Harvester Workshop for R users The AgReFed Data-Harvester enables reusable workflows for data downloads, feature extraction and spatial-temporal processing of ecological and environmental data into ready-made datasets for machine learning and geospatial insight. ### In this hands-on workshop, we will showcase: 1. Automatic download and extraction of: - Landsat/Sentinel data via Google Earth Engine (e.g., seasonal NDVI) - National calibrated Landsat/Sentinel data via Digital Earth Australia (DEA) Geoscience Earth Observations - Soil and Landscape Grid of Australia (SLGA) - SILO Climate Database (e.g., temperature, rainfall) - National Digital Elevation Model (DEM) 1 arcsec - Radiometric Data 2. Spatial and temporal extraction and processing from obtained datasets 3. Visualisation of data layers and satellite images from Google Earth Engine 4. Cloud-coverage masking for satellite image processing 5. Wrangling points of interest into a machine learning-friendly format ### ### Target audience: This workshop will be of interest to researchers and students in Agriculture, Environmental Science, Geosciences, as well as others interested in working with geo-spatial datasets, especially via spatio-temporal processing of open environmental and satellite-derived data. Additionally, anyone interested in joining an early-phase open-source project are encouraged to come and help develop! ### ### Workshop format: 3 hours online (with breaks). Registered attendees will receive a Zoom link closer to the date. ### ### Pre-requisites: A basic knowledge of R and RStudio is highly recommended; familiarity with tidyverse libraries will be advantageous. An interactive online RStudio environment will be provided for all users for the workshop (with installation instructions provided for those wishing to run things locally). **Note:** The AgReFed Data-Harvester is accessible via both _R_ and _Python_. If you would like to attend a Python workshop instead of (or in addition to) the R stream, please let us know of your interest by entering your email here: [https://sydney.au1.qualtrics.com/jfe/form/SV\_9TUKiEjaNEFPifk](https://sydney.au1.qualtrics.com/jfe/form/SV_9TUKiEjaNEFPifk) ### ### For more information:  sih.training@sydney.edu.au  2022-10-25 09:30:00 UTC 2022-10-25 12:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: Classification at UOA Online

    4 - 5 October 2022

    Introduction to Machine Learning using Python: Classification at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-uoa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2022-10-04 09:30:00 UTC 2022-10-05 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Longitudinal Trials with REDCap at LTU Online

    4 October 2022

    Longitudinal Trials with REDCap at LTU Online https://dresa.org.au/events/longitudinal-trials-with-redcap-at-ltu-online-fab74bbb-894b-4e69-8157-2498a9138206 REDCap is a powerful and extensible application for managing and running longitiudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shephard your participants through a complex survey at various time points with very little configuration. This course will introduce some of REDCap's more advanced features for running longitudinal studies, and builds on the foundational material taught in REDCAP101 - Managing Data Capture and Surveys with REDCap. #### You'll learn: - Build a longitudinal project - Manage participants throughout multiple events - Configure and use Automated Survey Invitations - Use Smart Variables to add powerful features to your logic - Take advantage of high-granularity permissions for your collaborators - Understand the data structure of a longitudinal project #### Prerequisites: This course requires the participant to have a fairly good basic knowledge of REDCap. To come up to speed, consider taking our [Data Capture and Surveys with REDCap](https://intersect.org.au/training/course/redcap101/) workshop. **For more information, please click [here](https://intersect.org.au/training/course/redcap201).** 2022-10-04 10:00:00 UTC 2022-10-04 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Data Entry and Processing in SPSS at UC Online

    4 - 5 October 2022

    Data Entry and Processing in SPSS at UC Online https://dresa.org.au/events/data-entry-and-processing-in-spss-at-uc-online-2ad49709-9422-4151-b2b5-47e7e0ba5976 This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in SPSS and perform visualization. This workshop is recommended for researchers and postgraduate students who are new to SPSS or Statistics; or those simply looking for a refresher course before taking a deep dive into using SPSS, either to apply it to their research or to add it to their arsenal of eResearch skills. #### You'll learn: - Navigate the SPSS working environment - Prepare data files and define variables - Enter data in SPSS and Import data from Excel - Perform data screening - Compose SPSS Syntax for data processing - Obtain descriptive statistics, create graphs & assess normality - Manipulate and transform variables #### Prerequisites: In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/spss101).** 2022-10-04 13:30:00 UTC 2022-10-05 16:30:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Design and Analysis of Surveys 1: Best Practice

    5 October 2022

    Design and Analysis of Surveys 1: Best Practice https://dresa.org.au/events/design-and-analysis-of-surveys-1-best-practice This workshop is full of practical tips and guidelines on how to design, field and analyse standard surveys. Some of the topics considered will be: how to design a survey, line vs discrete scales, the effect of colour, optimal discrete/LIKERT scales, pros and cons of common analyses (e.g. linear and logistic regression) and optimal ways to export data. The material is software agnostic and can be applied in any software. This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person:** Choose the "In person" ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-05 09:30:00 UTC 2022-10-05 11:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Design and Analysis of Surveys 1: Advanced Topics

    5 October 2022

    Design and Analysis of Surveys 1: Advanced Topics https://dresa.org.au/events/design-and-analysis-of-surveys-1-advanced-topics This workshop builds on the material in Surveys 1. It explores questionnaire validation and index creation using methods such as: Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) using Structural Equation Modelling (SEM), and Conjoint models such as Choice modelling. The material is software agnostic and can be applied in any software. This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person:** Choose the "In person" ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-05 11:30:00 UTC 2022-10-05 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Design and Analysis of Surveys 2: Advanced Topics

    5 October 2022

    Design and Analysis of Surveys 2: Advanced Topics https://dresa.org.au/events/design-and-analysis-of-surveys-2-advanced-topics This workshop builds on the material in Surveys 1. It explores questionnaire validation and index creation using methods such as: Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) using Structural Equation Modelling (SEM), and Conjoint models such as Choice modelling. The material is software agnostic and can be applied in any software. This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person:** Choose the "In person" ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-05 11:30:00 UTC 2022-10-05 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Multivariate Statistical Analysis 1: Dimension Reduction

    5 October 2022

    Multivariate Statistical Analysis 1: Dimension Reduction https://dresa.org.au/events/multivariate-statistical-analysis-1-dimension-reduction-5209a7a6-36ba-4a48-923d-58f236456361 **Multivariate Statistical Analysis 1: Dimension Reduction** In multivariate statistics we simultaneously model and estimate variability in more than one variable often in order to examine the relationship between variables. This workshop examines the key aspects of moving from univariate to multivariate analysis, and the situations and scenarios where multivariate analysis is typically applied. The focus will be on practical application of concepts through examples. **Topics covered will include:** - Motivations for undertaking multivariate analysis - Statistical principles for multivariate analysis - Dimension reduction techniques including principal components analysis (PCA), factor analysis (FA), correspondence analysis (CA) and non-metric multidimensional scaling (nMDS) This is just one workshop from a modular training programme made up of 1.5 hour workshops, each focusing on a single statistical method offered by Statistical Consulting within the Sydney Informatics Hub. Statistical Workflows giving practical step-by-step instructions applicable in any software are used and include experimental design, exploratory analysis, modelling, assumption testing, model interpretation and presentation of results. Researchers are encouraged to design a custom programme tailored to their research needs. **Pre-requisites** Prior experience with statistical modelling is assumed, as the basics of regression modelling will not be covered. Please consider attending Linear Models 1 and/or Linear Models 2 workshops to come up to speed beforehand. Note that this workshop does not require knowledge of or use of specific statistics software. The analysis methods may be performed using a wide range of commonly available software. **Attendance options** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person**: Choose the _**In person**_ ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online**: Choose the _**Online Zoom**_ ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - Open to: Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey) - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. **Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!!** 2022-10-05 14:00:00 UTC 2022-10-05 15:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: Introduction & Linear Regression at UniSA Online

    6 - 7 October 2022

    Introduction to Machine Learning using R: Introduction & Linear Regression at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-introduction-linear-regression-at-unisa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use R and and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts and familiarity with dplyr, tidyr and ggplot2 packages. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r205).** 2022-10-06 09:30:00 UTC 2022-10-07 12:30:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Intro to REDCap (webinar)

    6 October 2022

    Intro to REDCap (webinar) https://dresa.org.au/events/intro-to-redcap-webinar Research Electronic Data Capture (REDCap) is a secure web-based database application maintained by the University of Sydney. It is ideal for collecting and managing participant data and administering online surveys, with features supporting longitudinal data collection, complex team workflows and exports to a range of statistical analysis programs. **In "Intro to REDCap", we will cover:** - How to build a simple data entry project - How to collect data - How to add users/collaborators - How to export data Please note we will **not cover surveys**, as these are covered separately in our "Surveys in REDCap" training session. **Target audience:** Students and staff at the University of Sydney who need to collect electronic data or responses from participants into a structured database. **Open to:** Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey). If you do not have a UniKey, please contact us to confirm your position after registration. Please use your University of Sydney email address to register (e.g. first.last@sydney.edu.au, abcd1234@uni.sydney.edu.au) **Accessing the webinar:** Your confirmation **email from Eventbrite** will contain a button which will provide the details to access the Zoom webinar **For more information:** digital.research@sydney.edu.au Sydney Informatics Hub offers a broad range of training to researchers at the University. Please see our [training site](https://sydney.edu.au/research/facilities/sydney-informatics-hub/workshops-and-training.html)for more information. 2022-10-06 10:00:00 UTC 2022-10-06 12:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Excel for Researchers at Deakin Online

    6 - 7 October 2022

    Excel for Researchers at Deakin Online https://dresa.org.au/events/excel-for-researchers-at-deakin-online-bab9bfd3-9f50-4b1a-bd93-612966314ddd Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-06 13:00:00 UTC 2022-10-07 16:00:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UTS Online

    6 October 2022

    Getting started with NVivo for Windows at UTS Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-uts-online-b28faf2f-9fd5-4807-b81e-314757189cfc Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2022-10-06 13:30:00 UTC 2022-10-06 16:30:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Version Control with Git at UC Online

    6 October 2022

    Version Control with Git at UC Online https://dresa.org.au/events/version-control-with-git-at-uc-online Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available. Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - keep versions of data, scripts, and other files - examine commit logs to find which files were changed when - restore earlier versions of files - compare changes between versions of a file - push your versioned files to a remote location, for backup and to facilitate collaboration #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/git101).** 2022-10-06 13:30:00 UTC 2022-10-06 16:30:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at Western Sydney: Online

    7 October 2022

    Getting started with NVivo for Windows at Western Sydney: Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-western-sydney-online-553d211b-e146-4d09-9cd0-e8deed4614fb Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2022-10-07 09:30:00 UTC 2022-10-07 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UTS Online

    7 October 2022

    Getting started with NVivo for Windows at UTS Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-uts-online-3b35237e-2329-460c-b21d-edb3e4005185 Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers. NVivo allows researchers to simply organise and manage data from a variety of sources including surveys, interviews, articles, video, email, social media and web content, PDFs and images. Coding your data allows you to discover trends and compares themes as they emerge across different sources and data types. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner. #### You'll learn: - Create and organise a qualitative research project in NVivo - Import a range of data sources using NVivo's integrated tools - Code and classify your data - Format your data to take advantage of NVivo’s auto-coding ability - Use NVivo to discover new themes and trends in research - Visualise relationships and trends in your data #### Prerequisites: In order to participate, attendees must have a licensed copy of NVivo installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This course is taught using NVivo 12 Pro for Windows and is not suitable for NVivo for Mac users. **For more information, please click [here](https://intersect.org.au/training/course/nvivo101).** 2022-10-07 13:30:00 UTC 2022-10-07 16:30:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    11 October 2022

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2022-10-11 09:30:00 UTC 2022-10-11 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UNE Online

    11 October 2022

    Excel for Researchers at UNE Online https://dresa.org.au/events/excel-for-researchers-at-une-online Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-11 09:30:00 UTC 2022-10-11 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: Introduction & Linear Regression at UniSA Online

    11 - 12 October 2022

    Introduction to Machine Learning using Python: Introduction & Linear Regression at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-introduction-linear-regression-at-unisa-online-fbdbdb9f-7f87-4cef-94dd-ed271edf03b7 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Understand the difference between supervised and unsupervised Machine Learning. - Understand the fundamentals of Machine Learning. - Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax and basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python205).** 2022-10-11 13:00:00 UTC 2022-10-12 16:00:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UNE Online

    12 October 2022

    Excel for Researchers at UNE Online https://dresa.org.au/events/excel-for-researchers-at-une-online-c1d0c270-e1dd-4b1a-b283-8a4a1dadbfd0 Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-12 09:30:00 UTC 2022-10-12 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Collecting Web Data at UC Online

    12 - 13 October 2022

    Collecting Web Data at UC Online https://dresa.org.au/events/collecting-web-data-at-uc-online-1abf7b55-140d-4eea-a38c-22d682f9ebc5 Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated. Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it's a skill worth learning. So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - The concept of structured data - The use of XPath queries on HTML document - How to scrape data using browser extensions - How to scrape using Python and Scrapy - How to automate the scraping of multiple web pages #### Prerequisites: A good knowledge of the basic concepts and techniques in Python. Consider taking our [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to come up to speed beforehand. **For more information, please click [here](https://intersect.org.au/training/course/webdata201).** 2022-10-12 09:30:00 UTC 2022-10-13 16:30:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at LTU Online

    12 - 13 October 2022

    Excel for Researchers at LTU Online https://dresa.org.au/events/excel-for-researchers-at-ltu-online-2a214127-b203-4bfb-940d-09b19a3b34b6 Data rarely comes in the form you require. Often it is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors. We'll use one of the most widespread data wrangling tools, Microsoft Excel, to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. While aimed at novice Excel users, most attendees will walk away with new tricks to work more efficiently with their research data. #### You'll learn: - 'Clean up’ messy research data - Organise, format and name your data - Interpret your data (SORTING, FILTERING, CONDITIONAL FORMATTING) - Perform calculations on your data using functions (MAX, MIN, AVERAGE) - Extract significant findings from your data (PIVOT TABLE, VLOOKUP) - Manipulate your data (convert data format, work with DATES and TIMES) - Create graphs and charts to visualise your data (CHARTS) - Handy tips to speed up your work #### Prerequisites: In order to participate, attendees must have a licensed copy of Microsoft Excel installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. **For more information, please click [here](https://intersect.org.au/training/course/excel101).** 2022-10-12 09:30:00 UTC 2022-10-13 12:30:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Introduction to Artemis HPC

    12 October 2022

    Introduction to Artemis HPC https://dresa.org.au/events/introduction-to-artemis-hpc ### **Sydney Informatics Hub Research Computing Training Series** ## Introduction to the Artemis High Performance Computer (HPC) **Please note: OWN LAPTOP REQUIRED** with a terminal client installed. **Essential pre-requisites**: Competency on the Unix/Linux command line. If you are interested in learning HPC but have no Unix/Linux command-line skills, you should first take an 'Introduction to Unix/Linux' course. We will go through the basics if you have no experience. **Synopsis:** Learn about the University of Sydney’s HPC ‘Artemis’, including directory structure, software, and how to submit and monitor compute jobs using the PBS Pro scheduling software. Also learn about the national HPC facilities and cloud services available.  Artemis is available at no cost to University of Sydney staff and students. **Target audience:** Students and staff who would like to learn how to run compute jobs on Artemis HPC. Participants must have a valid USYD unikey. **Follow-on courses:** It is recommended to register for the Data transfer and RDS for HPC course to learn how to move data to Artemis. **Course notes and additional courses:** [https://sydney-informatics-hub.github.io/training.artemis/](https://sydney-informatics-hub.github.io/training.artemis/) **For more information:** Email sih.training@sydney.edu.au  **Sign up for email alerts about training:** [https://mailman.sydney.edu.au/mailman/listinfo/computing\_training](https://mailman.sydney.edu.au/mailman/listinfo/computing_training) 2022-10-12 10:00:00 UTC 2022-10-12 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: Classification at UniSA Online

    13 - 14 October 2022

    Introduction to Machine Learning using R: Classification at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-classification-at-unisa-online Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use R and its relevant packages to process real datasets, train and apply Machine Learning models #### Prerequisites: - Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation in R](https://intersect.org.au/training/course/r201/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/)needed to attend this course. If you already have experience with programming, please check the topics covered in courses above and [Introduction to ML using R: Introduction & Linear Regression](https://intersect.org.au/training/course/r205/) to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training. - Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using R workshops: - Introduction to Machine Learning using R: Introduction & Linear Regression - Introduction to Machine Learning using R: Classification - Introduction to Machine Learning using R: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/r206).** 2022-10-13 09:30:00 UTC 2022-10-14 09:30:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online

    13 October 2022

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UOA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-uoa-online-c4738fe2-2523-4a1f-baae-cdfb09e36ac2 Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python207).** 2022-10-13 09:30:00 UTC 2022-10-13 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Surveys in REDCap (webinar)

    13 October 2022

    Surveys in REDCap (webinar) https://dresa.org.au/events/surveys-in-redcap-webinar Research Electronic Data Capture (REDCap) is a secure web-based database application maintained by the University of Sydney. It is ideal for collecting and managing participant data and administering online surveys, with features supporting longitudinal data collection, complex team workflows and exports to a range of statistical analysis programs. **In "Surveys in REDCap", we will cover:** - How to turn a data collection instrument into a survey - How to distribute surveys - How to work with multiple surveys within a project (e.g. Survey Queue, Automated Survey Invitation features) **Essential pre-requisites:** You must know how to build a basic data entry project in REDCap. Please attend our "Intro to REDCap" webinar to cover this. **Target audience:** Students and staff at the University of Sydney who need to collect electronic data or responses from participants into a structured database. **Open to:** Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey). If you do not have a UniKey, please contact us to confirm your position after registration. Please use your University of Sydney email address to register (e.g. first.last@sydney.edu.au, abcd1234@uni.sydney.edu.au) **Accessing the webinar:** Your confirmation **email from Eventbrite** will contain a button which will provide the details to access the Zoom webinar **For more information:** digital.research@sydney.edu.au Sydney Informatics Hub offers a broad range of training to researchers at the University. Please see our [training site](https://sydney.edu.au/research/facilities/sydney-informatics-hub/workshops-and-training.html)for more information. 2022-10-13 10:00:00 UTC 2022-10-13 12:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • From PC to Cloud or High Performance Computing at UC Webinar

    14 October 2022

    From PC to Cloud or High Performance Computing at UC Webinar https://dresa.org.au/events/from-pc-to-cloud-or-high-performance-computing-at-uc-webinar Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well. We may refer to both types as “large scale computing” - but what is the difference? Both systems target scalability of computing, but in different ways. This webinar will give a good overview to the researchers thinking to make a move from their local computer to Cloud of High Performance Computing Cluster. #### You'll learn: - Introduction - HPC vs Cloud computing - When to use HPC - When to use the Cloud - The Cloud – Pros and Cons - HPC – Pros and Cons #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/compute001).** 2022-10-14 10:00:00 UTC 2022-10-14 11:30:00 UTC Intersect Australia Australia Australia Webinars training@intersect.org.au [] [] [] open_to_all []
  • Introduction to Machine Learning using Python: Classification at UniSA Online

    18 - 19 October 2022

    Introduction to Machine Learning using Python: Classification at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-unisa-online-27cb5a67-71b5-46bd-beaa-a132c09400db Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. #### You'll learn: - Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning. - Know the differences between various core Machine Learning models. - Understand the Machine Learning modelling workflows. - Use Python and scikit-learn to process real datasets, train and apply Machine Learning models #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation in Python](https://intersect.org.au/training/course/python201/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Data Manipulation and Visualisation in Python](https://intersect.org.au/training/course/python203/) and [Introduction to ML using Python: Introduction & Linear Regression](https://intersect.org.au/training/course/python205/) needed to attend this course. If you already have experience with programming, please check the topics covered in courses above to ensure that you are familiar with the knowledge needed for this course, such as good understanding of Python syntax, basic programming concepts and familiarity with Pandas, Numpy and Seaborn libraries, and basic understanding of Machine Learning and Model Training. Maths knowledge is not required. However, there is a few Math formula covered in this course and the references will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them. #### Why do this course: - Useful for anyone who wants to learn about Machine Learning but are overwhelmed with the tremendous amount of resources. - It does not go in depth into mathematical concepts and formula, however formal intuitions and references are provided to guide the participants for further learning. - We do have applications on real datasets! - Machine Learning models are introduced in this course together with important feature engineering techniques that are guaranteed to be useful in your own projects. - Give you enough background to kickstart your own Machine Learning journey, or transition yourself into Deep Learning. For a better and more complete understanding of the most popular Machine Learning models and techniques please consider attending all three Introduction to Machine Learning using Python workshops: - Introduction to Machine Learning using Python: Introduction & Linear Regression - Introduction to Machine Learning using Python: Classification - Introduction to Machine Learning using Python: SVM & Unsupervised Learning **For more information, please click [here](https://intersect.org.au/training/course/python206).** 2022-10-18 09:30:00 UTC 2022-10-19 12:30:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Meta-Analysis – An Introduction

    19 October 2022

    Meta-Analysis – An Introduction https://dresa.org.au/events/meta-analysis-an-introduction-ebfd3995-3fc5-4e57-bde6-0c113baec414 **Meta Analysis - An Introduction, run by the Sydney Informatics Hub.** This workshop provides research students with a theoretical and practical introduction to meta-analysis as part of a systematic review. In this workshop we will be examining the process of performing a meta-analysis in particular focusing on key statistical concepts such as heterogeneity and Fixed and Random effects modelling. The available choices of statistical software will be discussed and participants will be shown worked examples using the **metafor** package in R. A basic knowledge of R software is desirable, but not necessary since participants are not expected to produce and run their own code during the workshop. **Attendance options:** **In person:** Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online**: Choose the **_Online Zoom_** ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!! 2022-10-19 09:30:00 UTC 2022-10-19 11:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Linear Models 3: Advanced topics, tips and tricks

    19 October 2022

    Linear Models 3: Advanced topics, tips and tricks https://dresa.org.au/events/linear-models-3-advanced-topics-tips-and-tricks-255bce7c-f07c-41f5-ab50-de1dbf538b86 **Linear Models 3: Advanced topics, tips and tricks** Discusses topics such as: - Reporting and Interpretation (estimated marginal means, confidence vs prediction intervals, applying and correcting for multiple comparisons, reporting variable \*Importance\*, plus other reporting and interpretation tricks) - Model Parametrisation using the Design Matrix (interpreting categorical predictor parameters, dummy coding, effects coding) - More on Mixed Models (introducing the random slope) This is the third of 3 workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic/binary and count (Poisson) regression. Each one builds on the preceding workshops and together they show how all these analyses can be performed using the same easy to understand Generalised Linear Mixed Model (GLMM) framework and workflow. As well as how they can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. There is also a 4th complementary workshop called Statistical Model Building which we recommend for those experienced with Linear Models or who have done at least the first 2 of our workshops. The material is organised around Statistical Workflows applicable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results. **Attendance options:** - **In person**: Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. - **Online**: Choose the _**Online Zoom**_ ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. **Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!!** 2022-10-19 11:30:00 UTC 2022-10-19 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Statistical Model Building

    19 October 2022

    Statistical Model Building https://dresa.org.au/events/statistical-model-building-a41d5acd-9e17-4478-a400-b3a6954f19c9 This workshop examines and illustrates the key aspects and strategies of statistical model building to help you answer your research question, avoid common pitfalls, erroneous models and incorrect conclusions. Appropriate statistical model building will help you to gain knowledge as opposed to simply getting the best prediction (although that can be a goal as well). We will focus on concepts such as variable selection, multi-collinearity, interactions, selecting a model building strategy, comparing models and evaluating models. In general, these concepts are useful for any statistical model building. This workshop will provide generalised linear regression model examples. The focus will be on practical application of concepts so mathematical descriptions will be kept to a minimum. **Attendance options:** You will get the most out of this session by attending in person. This way allows you to interact with the presenters, ask questions directly, and catch up after the session. Please only choose online if circumstances prevent you from attending campus. **In person @Camperdown:** Choose the "In person" ticket option. **Online:** Choose the "Online Zoom" ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date.  Sydney Informatics Hub - Open to: Academic and Professional Staff, research students, and affiliates of The University of Sydney (with a valid UniKey) - Please use your **University of Sydney email address** to register i.e. **@sydney.edu.au, @uni.sydney.edu.au,** etc - If you do not have a UniKey, please contact us to confirm your position after registration. - Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you! 2022-10-19 14:00:00 UTC 2022-10-19 15:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • AgReFed Data Harvester Workshop

    25 October 2022

    AgReFed Data Harvester Workshop https://dresa.org.au/events/agrefed-data-harvester-workshop ### Sydney Informatics Hub presents: ## AgReFed Data-Harvester Workshop for R users The AgReFed Data-Harvester enables reusable workflows for data downloads, feature extraction and spatial-temporal processing of ecological and environmental data into ready-made datasets for machine learning and geospatial insight. ### In this hands-on workshop, we will showcase: 1. Automatic download and extraction of: - Landsat/Sentinel data via Google Earth Engine (e.g., seasonal NDVI) - National calibrated Landsat/Sentinel data via Digital Earth Australia (DEA) Geoscience Earth Observations - Soil and Landscape Grid of Australia (SLGA) - SILO Climate Database (e.g., temperature, rainfall) - National Digital Elevation Model (DEM) 1 arcsec - Radiometric Data 2. Spatial and temporal extraction and processing from obtained datasets 3. Visualisation of data layers and satellite images from Google Earth Engine 4. Cloud-coverage masking for satellite image processing 5. Wrangling points of interest into a machine learning-friendly format ### ### Target audience: This workshop will be of interest to researchers and students in Agriculture, Environmental Science, Geosciences, as well as others interested in working with geo-spatial datasets, especially via spatio-temporal processing of open environmental and satellite-derived data. Additionally, anyone interested in joining an early-phase open-source project are encouraged to come and help develop! ### ### Workshop format: 3 hours online (with breaks). Registered attendees will receive a Zoom link closer to the date. ### ### Pre-requisites: A basic knowledge of R and RStudio is highly recommended; familiarity with tidyverse libraries will be advantageous. An interactive online RStudio environment will be provided for all users for the workshop (with installation instructions provided for those wishing to run things locally). **Note:** The AgReFed Data-Harvester is accessible via both _R_ and _Python_. If you would like to attend a Python workshop instead of (or in addition to) the R stream, please let us know of your interest by entering your email here: [https://sydney.au1.qualtrics.com/jfe/form/SV\_9TUKiEjaNEFPifk](https://sydney.au1.qualtrics.com/jfe/form/SV_9TUKiEjaNEFPifk) ### ### For more information:  sih.training@sydney.edu.au  2022-10-25 09:30:00 UTC 2022-10-25 12:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []

Note, this map only displays events that have geolocation information in DReSA.
For the complete list of events in DReSA, click the grid tab.