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131 event found
  • 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 []
  • Longitudinal and Mixed Model Analysis with R

    6 October 2022

    Longitudinal and Mixed Model Analysis with R https://dresa.org.au/events/longitudinal-and-mixed-model-analysis-with-r-2b3da451-2272-483f-892f-5ee4f72a32a2 This workshop will develop participants’ understanding of the principles, methods, and interpretation of statistical models for longitudinal data (i.e. repeated measures over time) using R. The course will cover the principles of Linear Mixed Models from simple models to more complex ones and includes practical sessions getting hands-on experience of longitudinal analysis in R. 2022-10-06 09:00:00 UTC 2022-10-06 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []
  • 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 []
  • QIIME2 analysis of 16S rRNA

    6 October 2022

    North Melbourne, Australia

    QIIME2 analysis of 16S rRNA https://dresa.org.au/events/qiime2-analysis-of-16s-rrna This workshop will give you an introduction to the QIIME2 analysis platform using 16S rRNA amplicon data from coral-associated bacteria. 2022-10-06 10:00:00 UTC 2022-10-06 14:00:00 UTC Melbourne Bioinformatics 21 Bedford Street, North Melbourne, Australia 21 Bedford Street North Melbourne Australia 3051 University of MelbourneMelbourne Bioinformatics bioinformatics-training@unimelb.edu.au [] researcherspeople with command line experience 20 workshop host_institution Qiime2Metabarcoding
  • 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 []
  • Introduction to Gadi

    6 October 2022

    Introduction to Gadi https://dresa.org.au/events/introduction-to-gadi-8b67728e-dcd2-4be6-9539-470d2532c96d Introduction to Gadi is designed for new users, or users that want a refresher on the basics of Gadi. 2022-10-06 14:00:00 UTC 2022-10-06 16:00:00 UTC NCI NCI training.nci@anu.edu.au [] [] [] open_to_all []
  • Databasing Clinic (Australia)

    7 October 2022

    Databasing Clinic (Australia) https://dresa.org.au/events/databasing-clinic-australia-1675d8ca-bcc2-416a-8b0e-227d3fe4b00c Join Dr Michael Falk, Heurist’s Community Technical Advisor, for a free and flexible training session. What we cover is entirely dependant on your needs. We can: Have an introduction to Heurist’s core features Import data into your Heurist database Workshop ideas for your Heurist website Troubleshoot specific problems with your database Discuss principles of data modelling To register for the clinic, click here. After registering, you will receive a confirmation email containing information about joining the meeting. 2022-10-07 04:30:00 UTC 2022-10-07 06:00:00 UTC Heurist Network Online Online Heurist Network info@heuristnetwork.org [] [] [] open_to_all AdvancedIntroductoryTraining
  • 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 []
  • Planet Research Data Commons Consultation Roundtables

    7 October 2022

    Planet Research Data Commons Consultation Roundtables https://dresa.org.au/events/planet-research-data-commons-consultation-roundtables Roundtable discussion on developing national-scale data infrastructure for environmental research and decision making. 2022-10-07 14:00:00 UTC 2022-10-07 16:00:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 100 [] open_to_all []
  • Trends, Challenges and the Art of Survey Writing for Research: Online

    10 - 11 October 2022

    Trends, Challenges and the Art of Survey Writing for Research: Online https://dresa.org.au/events/trends-challenges-and-the-art-of-survey-writing-for-research-online We are in the middle of a data revolution. Research and decision-making in the private, not for profit and public sectors are not immune from these trends, and have become increasingly data-driven. Data collection is a significant part of this, and the use of surveys has become increasingly important to gather information on public opinion and consumer behaviour. However, there have been some high profile misses in recent years: the 2016 US Presidential election and Brexit being the most significant of these. What caused these errors, and can good survey design save us from making the same mistakes? Taught by an instructor with real-world experience as a campaign consultant, survey researcher and data scientist, this masterclass will focus on teaching you about survey design. We will explore how surveys can be effectively used to collect data for research, target campaigns and messaging, or design policy. You will be shown how modern survey research works, and sometimes doesn’t, how technology and social trends are changing survey research, and the best ways to write effective survey questions. It will provide you with the knowledge and skills to develop the survey instruments needed for data-driven research, decision making and advice. 2022-10-10 10:00:00 UTC 2022-10-11 16:00:00 UTC ACSPRI ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all survey designsurvey researchsurvey instruments
  • Planet Research Data Commons Consultation Roundtables

    10 October 2022

    Planet Research Data Commons Consultation Roundtables https://dresa.org.au/events/planet-research-data-commons-consultation-roundtables-143d9da7-acfd-403a-b39c-0cf38a7810c0 Roundtable discussion on developing national-scale data infrastructure for environmental research and decision making. 2022-10-10 10:00:00 UTC 2022-10-10 12:00:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 100 [] open_to_all []
  • Introduction to Reproducible Research

    11 October 2022

    Introduction to Reproducible Research https://dresa.org.au/events/introduction-to-reproducible-research-40989f50-1f5c-43e7-826a-8fd45e2d6daf An Introduction to Reproducible Research is a 3-hour workshop aimed at introducing researchers to concepts, tools and practices to make their research workflows, data and results reproducible. The workshop challenges participants to use available tools and existing support to increase their research impact, quality and reproducibility through traditional and non-traditional outputs. 2022-10-11 09:00:00 UTC 2022-10-11 12:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop 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 []
  • Planet Research Data Commons Consultation Roundtables

    12 October 2022

    Planet Research Data Commons Consultation Roundtables https://dresa.org.au/events/planet-research-data-commons-consultation-roundtables-0dc0fae4-89ae-4fb7-93e4-472ca2788c9a Roundtable discussion on developing national-scale data infrastructure for environmental research and decision making. 2022-10-12 10:00:00 UTC 2022-10-12 12:00:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 100 [] open_to_all []
  • Book History with Heurist: the challenges and potential of a databasing platform

    13 October 2022

    Book History with Heurist: the challenges and potential of a databasing platform https://dresa.org.au/events/book-history-with-heurist-the-challenges-and-potential-of-a-databasing-platform Book History with Heurist: the challenges and potential of a databasing platform Simon Dagenais (Trier University) Michael Falk (University of Sydney) WHAT Join the Heurist Book Historians for a workshop on methodologies for digital book history. What is the best way to model books as data? What aspects of books’ lives can be captured in databases and online? What are the best practices for constructing and sharing book history data. Please consider presenting your project or a short theoretical paper of no more than 20 minutes. It is a workshop, so ideal for either early-stage or mature work. Confirmed keynote speakers: Prof Simon Burrows (Western Sydney University) Dr Dominique Stutzmann (IRHT-CNRS) Convenors: Prof Simon Dagenais (Universität Trier) Dr Michael Falk (Heurist Network / University of Sydney) WHERE AND WHEN Thursday October the 13th, 1000-1400AM CET The workshop will take place on Zoom. Details will be provided closer to the time. Please register here if you would like to submit a project/paper to the workshop or simply to attend. QUOI Rejoignez les Heurist Book Historians pour un atelier sur les méthodologies de l’histoire du livre numérique. Quelle est la meilleure façon de modéliser des livres en tant que données ? Quels aspects de la vie des livres peuvent être capturés dans les bases de données et en ligne ? Quelles sont les meilleures pratiques pour construire et partager des données sur l’historique des livres ? Pensez à présenter votre projet ou un court exposé théorique de 20 minutes maximum. C’est un atelier, donc idéal pour les travaux débutants ou matures. Conférenciers confirmés : Prof Simon Burrows (Western Sydney University) Dr Dominique Stutzmann (IRHT-CNRS) Convenors : Prof Simon Dagenais (Universität Trier) Dr Michael Falk (Heurist Network / U. Sydney) OÙ ET QUAND Jeudi 13 octobre 1000-1400 CET L’atelier aura lieu sur Zoom. Les détails seront fournis plus tard. Veuillez vous inscrire ici si vous souhaitez soumettre un projet/un article à l’atelier ou simplement y assister. 2022-10-13 08:00:00 UTC 2022-10-13 12:00:00 UTC Heurist Network Online Online Heurist Network info@heuristnetwork.org [] [] [] open_to_all AdvancedIntroductorySymposium
  • 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 []
  • Data Retention Project Phase 4: Information Sessions

    13 October 2022

    Data Retention Project Phase 4: Information Sessions https://dresa.org.au/events/data-retention-project-phase-4-information-sessions-ef55252c-a2ce-4fee-95d4-3835ca0e4dc5 Data Retention Programme Phase 4 : Information Session 2022-10-13 15:30:00 UTC 2022-10-13 16:30:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 50 [] open_to_all Science & Technology
  • 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 []
  • 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 []
  • Longitudinal and Mixed Model Analysis with R

    6 October 2022

    Longitudinal and Mixed Model Analysis with R https://dresa.org.au/events/longitudinal-and-mixed-model-analysis-with-r-2b3da451-2272-483f-892f-5ee4f72a32a2 This workshop will develop participants’ understanding of the principles, methods, and interpretation of statistical models for longitudinal data (i.e. repeated measures over time) using R. The course will cover the principles of Linear Mixed Models from simple models to more complex ones and includes practical sessions getting hands-on experience of longitudinal analysis in R. 2022-10-06 09:00:00 UTC 2022-10-06 17:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop open_to_all []
  • 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 []
  • QIIME2 analysis of 16S rRNA

    6 October 2022

    North Melbourne, Australia

    QIIME2 analysis of 16S rRNA https://dresa.org.au/events/qiime2-analysis-of-16s-rrna This workshop will give you an introduction to the QIIME2 analysis platform using 16S rRNA amplicon data from coral-associated bacteria. 2022-10-06 10:00:00 UTC 2022-10-06 14:00:00 UTC Melbourne Bioinformatics 21 Bedford Street, North Melbourne, Australia 21 Bedford Street North Melbourne Australia 3051 University of MelbourneMelbourne Bioinformatics bioinformatics-training@unimelb.edu.au [] researcherspeople with command line experience 20 workshop host_institution Qiime2Metabarcoding
  • 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 []
  • Introduction to Gadi

    6 October 2022

    Introduction to Gadi https://dresa.org.au/events/introduction-to-gadi-8b67728e-dcd2-4be6-9539-470d2532c96d Introduction to Gadi is designed for new users, or users that want a refresher on the basics of Gadi. 2022-10-06 14:00:00 UTC 2022-10-06 16:00:00 UTC NCI NCI training.nci@anu.edu.au [] [] [] open_to_all []
  • Databasing Clinic (Australia)

    7 October 2022

    Databasing Clinic (Australia) https://dresa.org.au/events/databasing-clinic-australia-1675d8ca-bcc2-416a-8b0e-227d3fe4b00c Join Dr Michael Falk, Heurist’s Community Technical Advisor, for a free and flexible training session. What we cover is entirely dependant on your needs. We can: Have an introduction to Heurist’s core features Import data into your Heurist database Workshop ideas for your Heurist website Troubleshoot specific problems with your database Discuss principles of data modelling To register for the clinic, click here. After registering, you will receive a confirmation email containing information about joining the meeting. 2022-10-07 04:30:00 UTC 2022-10-07 06:00:00 UTC Heurist Network Online Online Heurist Network info@heuristnetwork.org [] [] [] open_to_all AdvancedIntroductoryTraining
  • 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 []
  • Planet Research Data Commons Consultation Roundtables

    7 October 2022

    Planet Research Data Commons Consultation Roundtables https://dresa.org.au/events/planet-research-data-commons-consultation-roundtables Roundtable discussion on developing national-scale data infrastructure for environmental research and decision making. 2022-10-07 14:00:00 UTC 2022-10-07 16:00:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 100 [] open_to_all []
  • Trends, Challenges and the Art of Survey Writing for Research: Online

    10 - 11 October 2022

    Trends, Challenges and the Art of Survey Writing for Research: Online https://dresa.org.au/events/trends-challenges-and-the-art-of-survey-writing-for-research-online We are in the middle of a data revolution. Research and decision-making in the private, not for profit and public sectors are not immune from these trends, and have become increasingly data-driven. Data collection is a significant part of this, and the use of surveys has become increasingly important to gather information on public opinion and consumer behaviour. However, there have been some high profile misses in recent years: the 2016 US Presidential election and Brexit being the most significant of these. What caused these errors, and can good survey design save us from making the same mistakes? Taught by an instructor with real-world experience as a campaign consultant, survey researcher and data scientist, this masterclass will focus on teaching you about survey design. We will explore how surveys can be effectively used to collect data for research, target campaigns and messaging, or design policy. You will be shown how modern survey research works, and sometimes doesn’t, how technology and social trends are changing survey research, and the best ways to write effective survey questions. It will provide you with the knowledge and skills to develop the survey instruments needed for data-driven research, decision making and advice. 2022-10-10 10:00:00 UTC 2022-10-11 16:00:00 UTC ACSPRI ACSPRI info@acspri.org.au [] [] 12 workshop open_to_all survey designsurvey researchsurvey instruments
  • Planet Research Data Commons Consultation Roundtables

    10 October 2022

    Planet Research Data Commons Consultation Roundtables https://dresa.org.au/events/planet-research-data-commons-consultation-roundtables-143d9da7-acfd-403a-b39c-0cf38a7810c0 Roundtable discussion on developing national-scale data infrastructure for environmental research and decision making. 2022-10-10 10:00:00 UTC 2022-10-10 12:00:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 100 [] open_to_all []
  • Introduction to Reproducible Research

    11 October 2022

    Introduction to Reproducible Research https://dresa.org.au/events/introduction-to-reproducible-research-40989f50-1f5c-43e7-826a-8fd45e2d6daf An Introduction to Reproducible Research is a 3-hour workshop aimed at introducing researchers to concepts, tools and practices to make their research workflows, data and results reproducible. The workshop challenges participants to use available tools and existing support to increase their research impact, quality and reproducibility through traditional and non-traditional outputs. 2022-10-11 09:00:00 UTC 2022-10-11 12:00:00 UTC QCIF QCIF training@qcif.edu.au [] [] workshop 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 []
  • Planet Research Data Commons Consultation Roundtables

    12 October 2022

    Planet Research Data Commons Consultation Roundtables https://dresa.org.au/events/planet-research-data-commons-consultation-roundtables-0dc0fae4-89ae-4fb7-93e4-472ca2788c9a Roundtable discussion on developing national-scale data infrastructure for environmental research and decision making. 2022-10-12 10:00:00 UTC 2022-10-12 12:00:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 100 [] open_to_all []
  • Book History with Heurist: the challenges and potential of a databasing platform

    13 October 2022

    Book History with Heurist: the challenges and potential of a databasing platform https://dresa.org.au/events/book-history-with-heurist-the-challenges-and-potential-of-a-databasing-platform Book History with Heurist: the challenges and potential of a databasing platform Simon Dagenais (Trier University) Michael Falk (University of Sydney) WHAT Join the Heurist Book Historians for a workshop on methodologies for digital book history. What is the best way to model books as data? What aspects of books’ lives can be captured in databases and online? What are the best practices for constructing and sharing book history data. Please consider presenting your project or a short theoretical paper of no more than 20 minutes. It is a workshop, so ideal for either early-stage or mature work. Confirmed keynote speakers: Prof Simon Burrows (Western Sydney University) Dr Dominique Stutzmann (IRHT-CNRS) Convenors: Prof Simon Dagenais (Universität Trier) Dr Michael Falk (Heurist Network / University of Sydney) WHERE AND WHEN Thursday October the 13th, 1000-1400AM CET The workshop will take place on Zoom. Details will be provided closer to the time. Please register here if you would like to submit a project/paper to the workshop or simply to attend. QUOI Rejoignez les Heurist Book Historians pour un atelier sur les méthodologies de l’histoire du livre numérique. Quelle est la meilleure façon de modéliser des livres en tant que données ? Quels aspects de la vie des livres peuvent être capturés dans les bases de données et en ligne ? Quelles sont les meilleures pratiques pour construire et partager des données sur l’historique des livres ? Pensez à présenter votre projet ou un court exposé théorique de 20 minutes maximum. C’est un atelier, donc idéal pour les travaux débutants ou matures. Conférenciers confirmés : Prof Simon Burrows (Western Sydney University) Dr Dominique Stutzmann (IRHT-CNRS) Convenors : Prof Simon Dagenais (Universität Trier) Dr Michael Falk (Heurist Network / U. Sydney) OÙ ET QUAND Jeudi 13 octobre 1000-1400 CET L’atelier aura lieu sur Zoom. Les détails seront fournis plus tard. Veuillez vous inscrire ici si vous souhaitez soumettre un projet/un article à l’atelier ou simplement y assister. 2022-10-13 08:00:00 UTC 2022-10-13 12:00:00 UTC Heurist Network Online Online Heurist Network info@heuristnetwork.org [] [] [] open_to_all AdvancedIntroductorySymposium
  • 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 []
  • Data Retention Project Phase 4: Information Sessions

    13 October 2022

    Data Retention Project Phase 4: Information Sessions https://dresa.org.au/events/data-retention-project-phase-4-information-sessions-ef55252c-a2ce-4fee-95d4-3835ca0e4dc5 Data Retention Programme Phase 4 : Information Session 2022-10-13 15:30:00 UTC 2022-10-13 16:30:00 UTC Australian Research Data Commons Australian Research Data Commons (ARDC) contact@ardc.edu.au [] [] 50 [] open_to_all Science & Technology
  • 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 []

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