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156 events found

Country: Australia 

  • Keeping Archives Online Learning Series

    1 July 2016 - 31 December 2025

    Keeping Archives Online Learning Series https://dresa.org.au/events/keeping-archives-online-learning-series Our pioneering online learning program, based on our respected publication Keeping Archives, was launched in 2016. It provides a new level of learning in the archives and records profession, filling a gap between a tertiary course and on-the-job experience. These courses are ideal for: - People who are new to archives and need a grounding in archival principles; - Students who wish to enhance the archival component of their training; - Professional archivists who may require a refresher in new archival methods and theory – e.g. emergent web technologies and social media platforms; - Statutory organisations whose staff need records and archives knowledge as part of their responsibilities. - Organisations with volunteers who engage in archival work and need basic knowledge. 2016-07-01 09:00:00 UTC 2025-12-31 17:00:00 UTC Australian Society of Archivists Australia Australia Australian Society of Archivists office@archivists.org.au [] [] [] open_to_all ArchivesRecordsArvchivingRecordkeeping
  • Introduction to Machine Learning using R: Introduction & Linear Regression at Deakin Online

    16 - 17 July 2024

    Introduction to Machine Learning using R: Introduction & Linear Regression at Deakin Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-introduction-linear-regression-at-deakin-online-93f3b97d-d602-4653-bad8-d193841aa4f5 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).** 2024-07-16 09:30:00 UTC 2024-07-17 12:30:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at La Trobe Online

    16 - 17 July 2024

    Excel for Researchers at La Trobe Online https://dresa.org.au/events/excel-for-researchers-at-la-trobe-online-71ffde26-ee29-4724-8b7f-d6b4476d04ce 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).** 2024-07-16 10:00:00 UTC 2024-07-17 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Traversing t tests in R at UTS Online

    17 July 2024

    Traversing t tests in R at UTS Online https://dresa.org.au/events/traversing-t-tests-in-r-at-uts-online R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** 2024-07-17 09:30:00 UTC 2024-07-17 12:45:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Traversing t tests in R at ACU

    17 July 2024

    Traversing t tests in R at ACU https://dresa.org.au/events/traversing-t-tests-in-r-at-acu R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** 2024-07-17 09:30:00 UTC 2024-07-17 12:45:00 UTC Intersect Australia Australia Australia ACU training@intersect.org.au [] [] [] host_institution []
  • Traversing t tests in R at UC Online

    17 July 2024

    Traversing t tests in R at UC Online https://dresa.org.au/events/traversing-t-tests-in-r-at-uc-online-934e3088-8d6e-49c4-ae8c-54018e6f749f R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** 2024-07-17 09:30:00 UTC 2024-07-17 12:45:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: SVM & Unsupervised Learning at UniSA Online

    17 July 2024

    Introduction to Machine Learning using R: SVM & Unsupervised Learning at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-svm-unsupervised-learning-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 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 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 the 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/r207).** 2024-07-17 13:00:00 UTC 2024-07-17 16:00:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Learn to Program: R at UOA Online

    18 - 19 July 2024

    Learn to Program: R at UOA Online https://dresa.org.au/events/learn-to-program-r-at-uoa-online-d5893ab5-32ab-4bb0-998a-1ae3e06fc35f R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2024-07-18 09:30:00 UTC 2024-07-19 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Meta-Analysis: an introduction

    22 July 2024

    Meta-Analysis: an introduction https://dresa.org.au/events/meta-analysis-an-introduction-cfaae4e7-5130-455b-8201-6a213e4b1077 **Meta Analysis - An Introduction, run by the Sydney Informatics Hub.** This workshop provides research students with a theoretical and practical introduction to meta-analysis as part of a systematic review. In this workshop we will be examining the process of performing a meta-analysis in particular focusing on key statistical concepts such as heterogeneity and Fixed and Random effects modelling. The available choices of statistical software will be discussed and participants will be shown worked examples using the **metafor** package in R. A basic knowledge of R software is desirable, but not necessary since participants are not expected to produce and run their own code during the workshop. **Attendance options:** **In person:** Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online**: Choose the **_Online Zoom_** ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!! 2024-07-22 09:30:00 UTC 2024-07-22 11:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Linear Models 3

    22 July 2024

    Linear Models 3 https://dresa.org.au/events/linear-models-3-baf32e31-153c-4cca-83b1-f8ca38ddc1a5 **Linear Models 3: Advanced topics, tips and tricks** Discusses topics such as: - Reporting and Interpretation (estimated marginal means, confidence vs prediction intervals, applying and correcting for multiple comparisons, reporting variable \*Importance\*, plus other reporting and interpretation tricks) - Model Parametrisation using the Design Matrix (interpreting categorical predictor parameters, dummy coding, effects coding) - More on Mixed Models (introducing the random slope) This is the third of 3 workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic/binary and count (Poisson) regression. Each one builds on the preceding workshops and together they show how all these analyses can be performed using the same easy to understand Generalised Linear Mixed Model (GLMM) framework and workflow. As well as how they can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. There is also a 4th complementary workshop called Statistical Model Building which we recommend for those experienced with Linear Models or who have done at least the first 2 of our workshops. The material is organised around Statistical Workflows applicable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results. **Attendance options:** - **In person**: Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. - **Online**: Choose the _**Online Zoom**_ ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. **Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!!** 2024-07-22 11:30:00 UTC 2024-07-22 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Multivariate Statistical Analysis 1: Dimension Reduction

    22 July 2024

    Multivariate Statistical Analysis 1: Dimension Reduction https://dresa.org.au/events/multivariate-statistical-analysis-1-dimension-reduction-0fdb5651-0866-4d25-ace8-6f2fbe679b23 **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!!!** 2024-07-22 14:00:00 UTC 2024-07-22 15:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Data Manipulation and Visualisation in R at UNE Online

    23 - 24 July 2024

    Data Manipulation and Visualisation in R at UNE Online https://dresa.org.au/events/data-manipulation-and-visualisation-in-r-at-une-online-63764a57-2d00-46b9-9000-1806b688d1ac R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: - DataFrame Manipulation using the dplyr package - DataFrame Transformation using the tidyr package - Using the Grammar of Graphics to convert data into figures using the ggplot2 package - Configuring plot elements within ggplot2 - Exploring different types of plots using ggplot2 #### Prerequisites: Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/r203).** 2024-07-23 09:30:00 UTC 2024-07-24 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 UNSW Online

    23 - 24 July 2024

    Introduction to Machine Learning using Python: Introduction & Linear Regression at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-introduction-linear-regression-at-unsw-online-684062b5-8dc0-40d9-9308-20ed4f2e9150 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).** 2024-07-23 09:30:00 UTC 2024-07-24 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at La Trobe Online

    23 July 2024

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at La Trobe Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-la-trobe-online-71a259f5-22a1-4843-8cc3-59b94270f5f0 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).** 2024-07-23 10:00:00 UTC 2024-07-23 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Learn to Program: Python at UOA Online

    23 - 24 July 2024

    Learn to Program: Python at UOA Online https://dresa.org.au/events/learn-to-program-python-at-uoa-online-6895b0cc-6c8c-487b-b754-005423a8efeb Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Python - How to load external data into Python - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/python101).** 2024-07-23 13:30:00 UTC 2024-07-24 16:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: Classification at Deakin Online

    24 - 25 July 2024

    Introduction to Machine Learning using R: Classification at Deakin Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-classification-at-deakin-online-233ed57b-c65f-4483-8e7a-0346940fe360 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).** 2024-07-24 09:30:00 UTC 2024-07-25 12:30:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []
  • Beyond Basics: Conditionals and Visualisation in Excel at La Trobe Online

    24 July 2024

    Beyond Basics: Conditionals and Visualisation in Excel at La Trobe Online https://dresa.org.au/events/beyond-basics-conditionals-and-visualisation-in-excel-at-la-trobe-online-88cd6920-7802-4ea2-b542-6fac4f484218 After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel's diverse functionality and apply it to your research project. #### You'll learn: - Cell syntax and conditional formatting - IF functions - Pivot Table summaries - Nesting multiple AND/IF/OR calculations - Combining nested calculations with conditional formatting to bring out important elements of the dataset - MINIFS function - Box plot creation and outlier identification - Trendline and error bar chart enhancements #### Prerequisites: Familiarity with the content of Excel for Researchers, specifically: the general Office/Excel interface (menus, ribbons/toolbars, etc.) workbooks and worksheets absolute and relative references, e.g. $A$1, A1. simple ranges, e.g. A1:B5 **For more information, please click [here](https://intersect.org.au/training/course/excel201).** 2024-07-24 10:00:00 UTC 2024-07-24 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at Western Sydney: Online

    25 July 2024

    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-4681913d-457a-43ed-9563-88496f58924a 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 14 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).** 2024-07-25 09:30:00 UTC 2024-07-25 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UNE Online

    30 - 31 July 2024

    Excel for Researchers at UNE Online https://dresa.org.au/events/excel-for-researchers-at-une-online-52594cb5-e398-459f-b9d6-4c61503a84de 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).** 2024-07-30 09:30:00 UTC 2024-07-31 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: Classification at UNSW Online

    30 - 31 July 2024

    Introduction to Machine Learning using Python: Classification at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-unsw-online-d4c53122-4eb5-4dc1-89e9-bcc4c2534d7a 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).** 2024-07-30 09:30:00 UTC 2024-07-31 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []
  • Data Manipulation in Python at La Trobe Online

    30 - 31 July 2024

    Data Manipulation in Python at La Trobe Online https://dresa.org.au/events/data-manipulation-in-python-at-la-trobe-online Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Working with pandas DataFrames - Indexing, slicing and subsetting in pandas DataFrames - Missing data values - Combine multiple pandas DataFrames #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python201).** 2024-07-30 10:00:00 UTC 2024-07-31 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UOA Online

    31 July 2024

    Getting started with NVivo for Windows at UOA Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-uoa-online-ecaa62ac-1eea-4705-9423-dc4496ea8d34 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 14 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).** 2024-07-31 09:30:00 UTC 2024-07-31 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Beyond Basics: Conditionals and Visualisation in Excel at UTS Online

    1 August 2024

    Beyond Basics: Conditionals and Visualisation in Excel at UTS Online https://dresa.org.au/events/beyond-basics-conditionals-and-visualisation-in-excel-at-uts-online-877d1985-4c49-400e-bc84-c59aa96a2a83 After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel's diverse functionality and apply it to your research project. #### You'll learn: - Cell syntax and conditional formatting - IF functions - Pivot Table summaries - Nesting multiple AND/IF/OR calculations - Combining nested calculations with conditional formatting to bring out important elements of the dataset - MINIFS function - Box plot creation and outlier identification - Trendline and error bar chart enhancements #### Prerequisites: Familiarity with the content of Excel for Researchers, specifically: the general Office/Excel interface (menus, ribbons/toolbars, etc.) workbooks and worksheets absolute and relative references, e.g. $A$1, A1. simple ranges, e.g. A1:B5 **For more information, please click [here](https://intersect.org.au/training/course/excel201).** 2024-08-01 09:30:00 UTC 2024-08-01 12:45:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UOA Online

    1 - 2 August 2024

    Excel for Researchers at UOA Online https://dresa.org.au/events/excel-for-researchers-at-uoa-online-cd5a6124-8f93-490b-9e93-c038c76fedc2 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).** 2024-08-01 13:30:00 UTC 2024-08-02 16:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Exploring Chi-square and correlation in R at UNE Online

    6 August 2024

    Exploring Chi-square and correlation in R at UNE Online https://dresa.org.au/events/exploring-chi-square-and-correlation-in-r-at-une-online-fc58daf6-786c-49a7-bf5d-36cddd231e3d This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson's r, Spearman's Rho and Kendall’s tau) in real world datasets. #### You'll learn: - Obtain inferential statistics and assess data normality - Manipulate data and create graphs - Perform Chi-Square tests (Goodness of Fit test and Test of Independence) - Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r210).** 2024-08-06 09:30:00 UTC 2024-08-06 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UNSW Online

    6 August 2024

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-unsw-online-7417bd83-0a26-411d-84e7-c9c5d92f6ef0 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).** 2024-08-06 09:30:00 UTC 2024-08-06 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []
  • Data Manipulation and Visualisation in Python at UOA Online

    6 - 7 August 2024

    Data Manipulation and Visualisation in Python at UOA Online https://dresa.org.au/events/data-manipulation-and-visualisation-in-python-at-uoa-online-1ea20eb5-79fe-441e-b0aa-006f3f0b79a3 Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation). We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Working with pandas DataFrames - Indexing, slicing and subsetting in pandas DataFrames - Missing data values - Combine multiple pandas DataFrames - Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries - Configuring plot elements within seaborn and matplotlib - Exploring different types of plots using seaborn #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python203).** 2024-08-06 09:30:00 UTC 2024-08-07 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Learn to Program: R at La Trobe Online

    6 - 7 August 2024

    Learn to Program: R at La Trobe Online https://dresa.org.au/events/learn-to-program-r-at-la-trobe-online-2e80cda4-90ab-4ea4-abad-74f49dd032ae R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2024-08-06 10:00:00 UTC 2024-08-07 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Exploring Chi-square and correlation in R at UNE Online

    6 August 2024

    Exploring Chi-square and correlation in R at UNE Online https://dresa.org.au/events/exploring-chi-square-and-correlation-in-r-at-une-online-7cd7561f-20bb-4fac-8e78-140ab5513288 This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson's r, Spearman's Rho and Kendall’s tau) in real world datasets. #### You'll learn: - Obtain inferential statistics and assess data normality - Manipulate data and create graphs - Perform Chi-Square tests (Goodness of Fit test and Test of Independence) - Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r210).** 2024-08-06 13:00:00 UTC 2024-08-06 16:00:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UNE Online

    7 August 2024

    Getting started with NVivo for Windows at UNE Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-une-online-604597fb-f3f2-4214-8b91-b8b2d4b65cfc 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 14 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).** 2024-08-07 09:30:00 UTC 2024-08-07 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Keeping Archives Online Learning Series

    1 July 2016 - 31 December 2025

    Keeping Archives Online Learning Series https://dresa.org.au/events/keeping-archives-online-learning-series Our pioneering online learning program, based on our respected publication Keeping Archives, was launched in 2016. It provides a new level of learning in the archives and records profession, filling a gap between a tertiary course and on-the-job experience. These courses are ideal for: - People who are new to archives and need a grounding in archival principles; - Students who wish to enhance the archival component of their training; - Professional archivists who may require a refresher in new archival methods and theory – e.g. emergent web technologies and social media platforms; - Statutory organisations whose staff need records and archives knowledge as part of their responsibilities. - Organisations with volunteers who engage in archival work and need basic knowledge. 2016-07-01 09:00:00 UTC 2025-12-31 17:00:00 UTC Australian Society of Archivists Australia Australia Australian Society of Archivists office@archivists.org.au [] [] [] open_to_all ArchivesRecordsArvchivingRecordkeeping
  • Introduction to Machine Learning using R: Introduction & Linear Regression at Deakin Online

    16 - 17 July 2024

    Introduction to Machine Learning using R: Introduction & Linear Regression at Deakin Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-introduction-linear-regression-at-deakin-online-93f3b97d-d602-4653-bad8-d193841aa4f5 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).** 2024-07-16 09:30:00 UTC 2024-07-17 12:30:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at La Trobe Online

    16 - 17 July 2024

    Excel for Researchers at La Trobe Online https://dresa.org.au/events/excel-for-researchers-at-la-trobe-online-71ffde26-ee29-4724-8b7f-d6b4476d04ce 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).** 2024-07-16 10:00:00 UTC 2024-07-17 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Traversing t tests in R at UTS Online

    17 July 2024

    Traversing t tests in R at UTS Online https://dresa.org.au/events/traversing-t-tests-in-r-at-uts-online R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** 2024-07-17 09:30:00 UTC 2024-07-17 12:45:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Traversing t tests in R at ACU

    17 July 2024

    Traversing t tests in R at ACU https://dresa.org.au/events/traversing-t-tests-in-r-at-acu R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** 2024-07-17 09:30:00 UTC 2024-07-17 12:45:00 UTC Intersect Australia Australia Australia ACU training@intersect.org.au [] [] [] host_institution []
  • Traversing t tests in R at UC Online

    17 July 2024

    Traversing t tests in R at UC Online https://dresa.org.au/events/traversing-t-tests-in-r-at-uc-online-934e3088-8d6e-49c4-ae8c-54018e6f749f R has become a popular programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. The primary goal of this workshop is to familiarise you with basic statistical concepts in R from reading in and manipulating data, checking assumptions, statistical tests and visualisations. This is not an advanced statistics course, but is instead designed to gently introduce you to statistical comparisons and hypothesis testing in R. #### You'll learn: - Read in and manipulate data - Check assumptions of t tests - Perform one-sample t tests - Perform two-sample t tests (Independent-samples, Paired-samples) - Perform nonparametric t tests (One-sample Wilcoxon Signed Rank test, Independent-samples Mann-Whitney U test) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts. Please consider attending Intersect's following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r211).** 2024-07-17 09:30:00 UTC 2024-07-17 12:45:00 UTC Intersect Australia Australia Australia UC training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: SVM & Unsupervised Learning at UniSA Online

    17 July 2024

    Introduction to Machine Learning using R: SVM & Unsupervised Learning at UniSA Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-svm-unsupervised-learning-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 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 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 the 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/r207).** 2024-07-17 13:00:00 UTC 2024-07-17 16:00:00 UTC Intersect Australia Australia Australia UniSA training@intersect.org.au [] [] [] host_institution []
  • Learn to Program: R at UOA Online

    18 - 19 July 2024

    Learn to Program: R at UOA Online https://dresa.org.au/events/learn-to-program-r-at-uoa-online-d5893ab5-32ab-4bb0-998a-1ae3e06fc35f R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2024-07-18 09:30:00 UTC 2024-07-19 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Meta-Analysis: an introduction

    22 July 2024

    Meta-Analysis: an introduction https://dresa.org.au/events/meta-analysis-an-introduction-cfaae4e7-5130-455b-8201-6a213e4b1077 **Meta Analysis - An Introduction, run by the Sydney Informatics Hub.** This workshop provides research students with a theoretical and practical introduction to meta-analysis as part of a systematic review. In this workshop we will be examining the process of performing a meta-analysis in particular focusing on key statistical concepts such as heterogeneity and Fixed and Random effects modelling. The available choices of statistical software will be discussed and participants will be shown worked examples using the **metafor** package in R. A basic knowledge of R software is desirable, but not necessary since participants are not expected to produce and run their own code during the workshop. **Attendance options:** **In person:** Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. **Online**: Choose the **_Online Zoom_** ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!! 2024-07-22 09:30:00 UTC 2024-07-22 11:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Linear Models 3

    22 July 2024

    Linear Models 3 https://dresa.org.au/events/linear-models-3-baf32e31-153c-4cca-83b1-f8ca38ddc1a5 **Linear Models 3: Advanced topics, tips and tricks** Discusses topics such as: - Reporting and Interpretation (estimated marginal means, confidence vs prediction intervals, applying and correcting for multiple comparisons, reporting variable \*Importance\*, plus other reporting and interpretation tricks) - Model Parametrisation using the Design Matrix (interpreting categorical predictor parameters, dummy coding, effects coding) - More on Mixed Models (introducing the random slope) This is the third of 3 workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic/binary and count (Poisson) regression. Each one builds on the preceding workshops and together they show how all these analyses can be performed using the same easy to understand Generalised Linear Mixed Model (GLMM) framework and workflow. As well as how they can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. There is also a 4th complementary workshop called Statistical Model Building which we recommend for those experienced with Linear Models or who have done at least the first 2 of our workshops. The material is organised around Statistical Workflows applicable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results. **Attendance options:** - **In person**: Choose the **_In person_** ticket option. University rules governing COVID related requirements will apply in the venue. In the event that in person delivery is not possible, you will be provided with the Zoom link for remote attendance. - **Online**: Choose the _**Online Zoom**_ ticket option. It is recommended to have a dual monitor setup to view and participate remotely in the session. Registered attendees will receive a link to the Zoom event closer to the date. Sydney Informatics Hub - This workshop is provided at no cost to researchers studying at, employed by or affiliated with the University of Sydney. This includes Honours and higher degree by research students, academic staff, and professional staff who do research. - Please use your University of Sydney email address to register i.e. @sydney.edu.au, @uni.sydney.edu.au, etc - If you do not have a UniKey, please contact us to confirm your position after registration. **Registrations from open web email clients (@gmail.com, @hotmail.com etc) will AUTOMATICALLY be cancelled, without us contacting you!!!** 2024-07-22 11:30:00 UTC 2024-07-22 13:00:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Multivariate Statistical Analysis 1: Dimension Reduction

    22 July 2024

    Multivariate Statistical Analysis 1: Dimension Reduction https://dresa.org.au/events/multivariate-statistical-analysis-1-dimension-reduction-0fdb5651-0866-4d25-ace8-6f2fbe679b23 **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!!!** 2024-07-22 14:00:00 UTC 2024-07-22 15:30:00 UTC Sydney Informatics Hub Sydney, Australia Sydney Australia University of Sydney sih.training@sydney.edu.au [] [] [] host_institution []
  • Data Manipulation and Visualisation in R at UNE Online

    23 - 24 July 2024

    Data Manipulation and Visualisation in R at UNE Online https://dresa.org.au/events/data-manipulation-and-visualisation-in-r-at-une-online-63764a57-2d00-46b9-9000-1806b688d1ac R is quickly gaining popularity as a programming language for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio and the Shiny web application framework. In this workshop, you will learn how to manipulate, explore and get insights from your data (Data Manipulation using the dplyr package), as well as how to convert your data from one format to another (Data Transformation using the tidyr package). You will also explore different types of graphs and learn how to customise them using one of the most popular plotting packages in R, ggplot2 (Data Visualisation). We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from Intersect and the highly regarded Software Carpentry Foundation. #### You'll learn: - DataFrame Manipulation using the dplyr package - DataFrame Transformation using the tidyr package - Using the Grammar of Graphics to convert data into figures using the ggplot2 package - Configuring plot elements within ggplot2 - Exploring different types of plots using ggplot2 #### Prerequisites: Either [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: R](https://intersect.org.au/training/course/r101/) and [R for Research](https://intersect.org.au/training/course/r110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/r203).** 2024-07-23 09:30:00 UTC 2024-07-24 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 UNSW Online

    23 - 24 July 2024

    Introduction to Machine Learning using Python: Introduction & Linear Regression at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-introduction-linear-regression-at-unsw-online-684062b5-8dc0-40d9-9308-20ed4f2e9150 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).** 2024-07-23 09:30:00 UTC 2024-07-24 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at La Trobe Online

    23 July 2024

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at La Trobe Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-la-trobe-online-71a259f5-22a1-4843-8cc3-59b94270f5f0 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).** 2024-07-23 10:00:00 UTC 2024-07-23 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Learn to Program: Python at UOA Online

    23 - 24 July 2024

    Learn to Program: Python at UOA Online https://dresa.org.au/events/learn-to-program-python-at-uoa-online-6895b0cc-6c8c-487b-b754-005423a8efeb Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Python - How to load external data into Python - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in Python #### Prerequisites: No prior experience with programming is needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/python101).** 2024-07-23 13:30:00 UTC 2024-07-24 16:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using R: Classification at Deakin Online

    24 - 25 July 2024

    Introduction to Machine Learning using R: Classification at Deakin Online https://dresa.org.au/events/introduction-to-machine-learning-using-r-classification-at-deakin-online-233ed57b-c65f-4483-8e7a-0346940fe360 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).** 2024-07-24 09:30:00 UTC 2024-07-25 12:30:00 UTC Intersect Australia Australia Australia Deakin training@intersect.org.au [] [] [] host_institution []
  • Beyond Basics: Conditionals and Visualisation in Excel at La Trobe Online

    24 July 2024

    Beyond Basics: Conditionals and Visualisation in Excel at La Trobe Online https://dresa.org.au/events/beyond-basics-conditionals-and-visualisation-in-excel-at-la-trobe-online-88cd6920-7802-4ea2-b542-6fac4f484218 After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel's diverse functionality and apply it to your research project. #### You'll learn: - Cell syntax and conditional formatting - IF functions - Pivot Table summaries - Nesting multiple AND/IF/OR calculations - Combining nested calculations with conditional formatting to bring out important elements of the dataset - MINIFS function - Box plot creation and outlier identification - Trendline and error bar chart enhancements #### Prerequisites: Familiarity with the content of Excel for Researchers, specifically: the general Office/Excel interface (menus, ribbons/toolbars, etc.) workbooks and worksheets absolute and relative references, e.g. $A$1, A1. simple ranges, e.g. A1:B5 **For more information, please click [here](https://intersect.org.au/training/course/excel201).** 2024-07-24 10:00:00 UTC 2024-07-24 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at Western Sydney: Online

    25 July 2024

    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-4681913d-457a-43ed-9563-88496f58924a 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 14 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).** 2024-07-25 09:30:00 UTC 2024-07-25 12:30:00 UTC Intersect Australia Australia Australia WSU training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UNE Online

    30 - 31 July 2024

    Excel for Researchers at UNE Online https://dresa.org.au/events/excel-for-researchers-at-une-online-52594cb5-e398-459f-b9d6-4c61503a84de 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).** 2024-07-30 09:30:00 UTC 2024-07-31 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: Classification at UNSW Online

    30 - 31 July 2024

    Introduction to Machine Learning using Python: Classification at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-classification-at-unsw-online-d4c53122-4eb5-4dc1-89e9-bcc4c2534d7a 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).** 2024-07-30 09:30:00 UTC 2024-07-31 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []
  • Data Manipulation in Python at La Trobe Online

    30 - 31 July 2024

    Data Manipulation in Python at La Trobe Online https://dresa.org.au/events/data-manipulation-in-python-at-la-trobe-online Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Working with pandas DataFrames - Indexing, slicing and subsetting in pandas DataFrames - Missing data values - Combine multiple pandas DataFrames #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python201).** 2024-07-30 10:00:00 UTC 2024-07-31 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UOA Online

    31 July 2024

    Getting started with NVivo for Windows at UOA Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-uoa-online-ecaa62ac-1eea-4705-9423-dc4496ea8d34 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 14 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).** 2024-07-31 09:30:00 UTC 2024-07-31 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Beyond Basics: Conditionals and Visualisation in Excel at UTS Online

    1 August 2024

    Beyond Basics: Conditionals and Visualisation in Excel at UTS Online https://dresa.org.au/events/beyond-basics-conditionals-and-visualisation-in-excel-at-uts-online-877d1985-4c49-400e-bc84-c59aa96a2a83 After cleaning your database, you may need to apply some conditional analysis to glean greater insights from your data. You may also want to enhance your charts for inclusion into a manuscript, thesis or report by adding some statistical elements. This course will cover conditional syntax, nested functions, statistical charting and outlier identification. Armed with the tips and tricks from our introductory Excel for Researchers course, you will be able to tap into even more of Excel's diverse functionality and apply it to your research project. #### You'll learn: - Cell syntax and conditional formatting - IF functions - Pivot Table summaries - Nesting multiple AND/IF/OR calculations - Combining nested calculations with conditional formatting to bring out important elements of the dataset - MINIFS function - Box plot creation and outlier identification - Trendline and error bar chart enhancements #### Prerequisites: Familiarity with the content of Excel for Researchers, specifically: the general Office/Excel interface (menus, ribbons/toolbars, etc.) workbooks and worksheets absolute and relative references, e.g. $A$1, A1. simple ranges, e.g. A1:B5 **For more information, please click [here](https://intersect.org.au/training/course/excel201).** 2024-08-01 09:30:00 UTC 2024-08-01 12:45:00 UTC Intersect Australia Australia Australia UTS training@intersect.org.au [] [] [] host_institution []
  • Excel for Researchers at UOA Online

    1 - 2 August 2024

    Excel for Researchers at UOA Online https://dresa.org.au/events/excel-for-researchers-at-uoa-online-cd5a6124-8f93-490b-9e93-c038c76fedc2 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).** 2024-08-01 13:30:00 UTC 2024-08-02 16:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Exploring Chi-square and correlation in R at UNE Online

    6 August 2024

    Exploring Chi-square and correlation in R at UNE Online https://dresa.org.au/events/exploring-chi-square-and-correlation-in-r-at-une-online-fc58daf6-786c-49a7-bf5d-36cddd231e3d This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson's r, Spearman's Rho and Kendall’s tau) in real world datasets. #### You'll learn: - Obtain inferential statistics and assess data normality - Manipulate data and create graphs - Perform Chi-Square tests (Goodness of Fit test and Test of Independence) - Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r210).** 2024-08-06 09:30:00 UTC 2024-08-06 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UNSW Online

    6 August 2024

    Introduction to Machine Learning using Python: SVM & Unsupervised Learning at UNSW Online https://dresa.org.au/events/introduction-to-machine-learning-using-python-svm-unsupervised-learning-at-unsw-online-7417bd83-0a26-411d-84e7-c9c5d92f6ef0 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).** 2024-08-06 09:30:00 UTC 2024-08-06 12:30:00 UTC Intersect Australia Australia Australia UNSW training@intersect.org.au [] [] [] host_institution []
  • Data Manipulation and Visualisation in Python at UOA Online

    6 - 7 August 2024

    Data Manipulation and Visualisation in Python at UOA Online https://dresa.org.au/events/data-manipulation-and-visualisation-in-python-at-uoa-online-1ea20eb5-79fe-441e-b0aa-006f3f0b79a3 Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you'd expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation). We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Working with pandas DataFrames - Indexing, slicing and subsetting in pandas DataFrames - Missing data values - Combine multiple pandas DataFrames - Using the Grammar of Graphics to convert data into figures using the seaborn and matplotlib libraries - Configuring plot elements within seaborn and matplotlib - Exploring different types of plots using seaborn #### Prerequisites: Either [Learn to Program: Python](https://intersect.org.au/training/course/python101/) or [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) needed to attend this course. If you already have experience with programming, please check the topics covered in the [Learn to Program: Python](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to ensure that you are familiar with the knowledge needed for this course. **For more information, please click [here](https://intersect.org.au/training/course/python203).** 2024-08-06 09:30:00 UTC 2024-08-07 12:30:00 UTC Intersect Australia Australia Australia UOA training@intersect.org.au [] [] [] host_institution []
  • Learn to Program: R at La Trobe Online

    6 - 7 August 2024

    Learn to Program: R at La Trobe Online https://dresa.org.au/events/learn-to-program-r-at-la-trobe-online-2e80cda4-90ab-4ea4-abad-74f49dd032ae R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework. But getting started with R can be challenging, particularly if you've never programmed before. That's where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Introduction to the RStudio interface for programming - Basic syntax and data types in R - How to load external data into R - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data in R #### Prerequisites: No prior experience with programming needed to attend this course. We strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/r101).** 2024-08-06 10:00:00 UTC 2024-08-07 13:00:00 UTC Intersect Australia Australia Australia LTU training@intersect.org.au [] [] [] host_institution []
  • Exploring Chi-square and correlation in R at UNE Online

    6 August 2024

    Exploring Chi-square and correlation in R at UNE Online https://dresa.org.au/events/exploring-chi-square-and-correlation-in-r-at-une-online-7cd7561f-20bb-4fac-8e78-140ab5513288 This hands-on training is designed to familiarise you with the data analysis environment of the R programming. In this session, we will traverse into the realm of inferential statistics, beginning with correlation and reliability. We will present a brief conceptual overview and the R procedures for computing reliability and correlation (Pearson's r, Spearman's Rho and Kendall’s tau) in real world datasets. #### You'll learn: - Obtain inferential statistics and assess data normality - Manipulate data and create graphs - Perform Chi-Square tests (Goodness of Fit test and Test of Independence) - Perform correlations on continuous and categorical data (Pearson’s r, Spearman’s Rho and Kendall’s tau) #### Prerequisites: This course assumes familiarity with R and RStudio. You should have a good understanding of R syntax and basic programming concepts, as well as familiarity with data manipulation (dplyr) and visualisation (ggplot2 package). Please consider attending Intersect’s following courses to get up to speed: [Learn to Program: R](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) **For more information, please click [here](https://intersect.org.au/training/course/r210).** 2024-08-06 13:00:00 UTC 2024-08-06 16:00:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []
  • Getting started with NVivo for Windows at UNE Online

    7 August 2024

    Getting started with NVivo for Windows at UNE Online https://dresa.org.au/events/getting-started-with-nvivo-for-windows-at-une-online-604597fb-f3f2-4214-8b91-b8b2d4b65cfc 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 14 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).** 2024-08-07 09:30:00 UTC 2024-08-07 12:30:00 UTC Intersect Australia Australia Australia UNE training@intersect.org.au [] [] [] host_institution []

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