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Programming and tidy data analysis in R

A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps...

Keywords: R, Tidyverse, Programming

Resource type: tutorial

Programming and tidy data analysis in R https://dresa.org.au/materials/programming-and-tidy-data-analysis-in-r A workshop to expand the skill-set of someone who has basic familiarity with R. Covers programming constructs such as functions and for-loops, and working with data frames using the dplyr and tidyr packages. Explains the importance of a "tidy" data representation, and goes through common steps needed to load data and convert it into a tidy form. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R, Tidyverse, Programming phd ecr researcher
Linear models in R

A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the...

Keywords: R statistics

Resource type: tutorial

Linear models in R https://dresa.org.au/materials/linear-models-in-r A workshop on linear models in R. Learning to use linear models provides a foundation for modelling, estimation, prediction, and statistical testing in R. Many commonly used statistical tests can be performed using linear models. Ideas introduced using linear models are applicable to many of the more complicated statistical and machine learning models available in R. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R statistics phd ecr researcher
Introduction to R

An introduction to R, for people with zero coding experience.

To be taught as a hands on workshop, typically as two half-days.

Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and...

Keywords: R

Resource type: tutorial

Introduction to R https://dresa.org.au/materials/introduction-to-r An introduction to R, for people with zero coding experience. To be taught as a hands on workshop, typically as two half-days. Developed by the Monash Bioinformatics Platform and taught as part of the Data Fluency program at Monash University. License is CC-BY-4. You are free to share and adapt the material so long as attribution is given. Paul Harrison paul.harrison@monash.edu R phd ecr researcher
Introduction to Jupyter Notebooks

This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data.

You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new,...

Keywords: jupyter, Introductory, training material, CloudStor, markdown, Python, R

Resource type: tutorial

Introduction to Jupyter Notebooks https://dresa.org.au/materials/introduction-to-jupyter-notebooks This workshop will introduce you to Jupyter Notebooks, a digital tool that has exploded in popularity in recent years for those working with data. You will learn what they are, what they do and why you might like to use them. It is an introductory set of lessons for those who are brand new, have little or no knowledge of coding and computational methods in research. This workshop is targeted at those who are absolute beginners or ‘tech-curious’. It includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming. sara.king@aarnet.edu.au Mason, Ingrid jupyter, Introductory, training material, CloudStor, markdown, Python, R
WORKSHOP: R: fundamental skills for biologists

This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022.

Event description

Biologists need data analysis skills to be able to...

Keywords: Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation

WORKSHOP: R: fundamental skills for biologists https://dresa.org.au/materials/workshop-r-fundamental-skills-for-biologists This record includes training materials associated with the Australian BioCommons workshop ‘R: fundamental skills for biologists’. This workshop took place over four, three-hour sessions on 1, 8, 15 and 22 June 2022. **Event description** Biologists need data analysis skills to be able to interpret, visualise and communicate their research results. While Excel can cover some data analysis needs, there is a better choice, particularly for large and complex datasets.  R is a free, open-source software and programming language that enables data exploration, statistical analysis, visualisation and more. The large variety of R packages available for analysing biological data make it a robust and flexible option for data of all shapes and sizes.  Getting started can be a little daunting for those without a background in statistics and programming. In this workshop we will equip you with the foundations for getting the most out of R and RStudio, an interactive way of structuring and keeping track of your work in R. Using biological data from a model of influenza infection, you will learn how to efficiently and reproducibly organise, read, wrangle, analyse, visualise and generate reports from your data in R. Topics covered in this workshop include: - Spreadsheets, organising data and first steps with R - Manipulating and analysing data with dplyr - Data visualisation - Summarized experiments and getting started with Bioconductor This workshop is presented by the Australian BioCommons and Saskia Freytag from WEHI  with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. **Files and materials included in this record:** - Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. - Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. - Schedule (PDF): A breakdown of the topics and timings for the workshop - Recommended resources (PDF): A list of resources recommended by trainers and participants - Q_and_A(PDF): Archive of questions and their answers from the workshop Slack Channel. **Materials shared elsewhere:** This workshop follows the tutorial ‘Introduction to data analysis with R and Bioconductor’ which is publicly available. https://saskiafreytag.github.io/biocommons-r-intro/ This is derived from material produced as part of The Carpentries Incubator project https://carpentries-incubator.github.io/bioc-intro/ Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Analysis, Statistics, R software, RStudio, Data visualisation
WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software

This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022.

Event description

bio.tools provides easy access to essential...

Keywords: Bioinformatics, Research software, EDAM, Workflows, FAIR

WEBINAR: bio.tools - making it easier to find, understand and cite biological tools and software https://dresa.org.au/materials/webinar-bio-tools-making-it-easier-to-find-understand-and-cite-biological-tools-and-software This record includes training materials associated with the Australian BioCommons webinar ‘bio.tools - making it easier to find, understand and cite biological tools and software’. This webinar took place on 21 June 2022. **Event description** bio.tools provides easy access to essential scientific and technical information about software, command-line tools, databases and services. It’s backed by ELIXIR, the European Infrastructure for Biological Information, and is being used in Australia to register software (e.g. Galaxy Australia, prokka). It underpins the information provided in the Australian BioCommons discovery service ToolFinder. Hans Ienasescu and Matúš Kalaš join us to explain how bio.tools uses a community driven, open science model to create this collection of resources and how it makes it easier to find, understand, utilise and cite them. They’ll delve into how bio.tools is using standard semantics (e.g. the EDAM ontology) and syntax (e.g. biotoolsSchema) to enrich the annotation and description of tools and resources. Finally, we’ll see how the community can contribute to bio.tools and take advantage of its key features to share and promote their own research software.   Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. **Files and materials included in this record:** - Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. - Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. - biotools_EDAM_slides (PDF): A PDF copy of the slides presented during the webinar. **Materials shared elsewhere:** A recording of this webinar is available on the Australian BioCommons YouTube Channel: https://youtu.be/K0J4_bAUG3Y Melissa Burke (melissa@biocommons.org.au) Bioinformatics, Research software, EDAM, Workflows, FAIR
Beyond Basics: Conditionals and Visualisation in Excel

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...

Keywords: Data Analysis, Excel

Beyond Basics: Conditionals and Visualisation in Excel https://dresa.org.au/materials/beyond-basics-conditionals-and-visualisation-in-excel 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).** training@intersect.org.au Data Analysis, Excel
Data Visualisation in R

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 explore different types of graphs and learn how to...

Keywords: Programming, R

Data Visualisation in R https://dresa.org.au/materials/data-visualisation-in-r 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 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: - 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 or Learn to Program: R and R for Research needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: R and R for Research courses to ensure that you are familiar with the knowledge needed for this course. We also strongly recommend attending the Data Manipulation in R course. **For more information, please click [here](https://intersect.org.au/training/course/r202).** training@intersect.org.au Programming, R
Data Manipulation and Visualisation in R

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...

Keywords: Programming, R

Data Manipulation and Visualisation in R https://dresa.org.au/materials/data-manipulation-and-visualisation-in-r 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 or Learn to Program: R and R for Research needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: R and R for Research 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).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: Introduction & Linear Regression

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...

Keywords: Programming, R

Introduction to Machine Learning using R: Introduction & Linear Regression https://dresa.org.au/materials/introduction-to-machine-learning-using-r-introduction-linear-regression 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 and Data Manipulation in R or Learn to Program: R and Data Manipulation and Visualisation in R needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: R, Data Manipulation in R and Data Manipulation and Visualisation in R courses 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).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: Classification

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...

Keywords: Programming, R

Introduction to Machine Learning using R: Classification https://dresa.org.au/materials/introduction-to-machine-learning-using-r-classification 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 and Data Manipulation in R or Learn to Program: R and Data Manipulation and Visualisation in R needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: R, Data Manipulation in R and Data Manipulation and Visualisation in R and Introduction to ML using R: Introduction & Linear Regression courses 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).** training@intersect.org.au Programming, R
Introduction to Machine Learning using R: SVM & Unsupervised Learning

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...

Keywords: Programming, R

Introduction to Machine Learning using R: SVM & Unsupervised Learning https://dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning 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 and Data Manipulation in R or Learn to Program: R and Data Manipulation and Visualisation in R needed to attend this course. If you already have experience with programming, please check the topics covered in the Learn to Program: R, Data Manipulation in R and Data Manipulation and Visualisation in R and Introduction to ML using R: Introduction & Linear Regression courses 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).** training@intersect.org.au Programming, R
Exploring Chi-square and correlation in R

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...

Keywords: Programming, R

Exploring Chi-square and correlation in R https://dresa.org.au/materials/exploring-chi-square-and-correlation-in-r 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 Data Manipulation and Visualisation in R **For more information, please click [here](https://intersect.org.au/training/course/r210).** training@intersect.org.au Programming, R
Traversing t tests in R

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...

Keywords: Programming, R

Traversing t tests in R https://dresa.org.au/materials/traversing-t-tests-in-r 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 Data Manipulation and Visualisation in R **For more information, please click [here](https://intersect.org.au/training/course/r211).** training@intersect.org.au Programming, R
Exploring ANOVAs in R

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.

This half-day course covers one and two-way Analyses of Variance (ANOVA) and their...

Keywords: Programming, R

Exploring ANOVAs in R https://dresa.org.au/materials/exploring-anovas-in-r 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. This half-day course covers one and two-way Analyses of Variance (ANOVA) and their non-parametric counterparts in R. To better understand the tests, assumptions and associated concepts, we will be using a dataset containing the Mathematics scores of secondary students. This dataset also includes information regarding their mother's and father's jobs and education levels, the number of hours dedicated to study, and time spent commuting to and from school. Lifestyle information about alcohol consumption habits, whether the students have quality relationships with their families and whether they have free time after school is included in this dataset. #### You'll learn: - Basic statistical theory behind ANOVAs - How to check that the data meets the assumptions - One-way ANOVA in R and post-hoc analysis - Two-way ANOVA plus interaction effects and post-hoc analysis - Non-parametric alternatives to one and two-way ANOVA #### Prerequisites: This course assumes an intermediate level of programming proficiency, plus familiarity with the syntax and functions of the dplyr and ggplot2 packages. Experience navigating the RStudio integrated development environment (IDE) is also required. If you’re new to programming in R, we strongly recommend you register for the Learn to Program: R, Data Manipulation in R, and Data Visualisation in R workshops first. Please see Intersect’s training schedule to find the next upcoming courses. **For more information, please click [here](https://intersect.org.au/training/course/r212).** training@intersect.org.au Programming, R
Research Data Management Techniques

Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don't know how?

This workshop is ideal for researchers who want to know how research data management can support project...

Keywords: Data Management, Data Management

Research Data Management Techniques https://dresa.org.au/materials/research-data-management-techniques Are you drowning in research data? Do you want to know where you should be storing your data? Are you required to comply with funding body data management requirements, but don't know how? This workshop is ideal for researchers who want to know how research data management can support project success and are interested in research data management services and support available at their institution. Combining slide-based background material, discussions, and case studies this workshop will equip participants with best practices for managing their valuable research data. #### You'll learn: - How to manage research data according to legal, statutory, ethical, funding body and university requirements - Approaches to planning, collecting, organising, managing, storing, backing up, preserving, and sharing your data - Services supporting research data at your institution #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/rdmt001).** training@intersect.org.au Data Management, Data Management
Data Capture and Surveys with REDCap

Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you.

This course will introduce you to REDCap, a rapidly evolving web tool developed by...

Keywords: Data Management, REDCap

Data Capture and Surveys with REDCap https://dresa.org.au/materials/data-capture-and-surveys-with-redcap Would you like to enable secure and reliable data collection forms and manage online surveys? Would your study benefit from web-based data entry? Research Electronic Data Capture (REDCap) might be for you. This course will introduce you to REDCap, a rapidly evolving web tool developed by researchers for researchers. REDCap features a high level of security, and a high degree of customisability for your forms and advanced user access control. It also features free, unlimited survey distribution functionality and a sophisticated export module with support for all standard statistical programs. #### You'll learn: - Get started with REDCap - Create and set up projects - Design forms and surveys using the online designer - Learn how to use branching logic, piping, and calculations - Enter data via forms and distribute surveys - Create, view and export data reports - Add collaborators and set their privileges #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/redcap101).** training@intersect.org.au Data Management, REDCap
Longitudinal Trials with REDCap

REDCap is a powerful and extensible application for managing and running longitiudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shephard your participants through a complex survey at various time points with...

Keywords: Data Management, REDCap

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

Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet?

Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring, normalising...

Keywords: Data Analysis, Open Refine

Cleaning Data with Open Refine https://dresa.org.au/materials/cleaning-data-with-open-refine Do you have messy data from multiple inconsistent sources, or open-responses to questionnaires? Do you want to improve the quality of your data by refining it and using the power of the internet? Open Refine is the perfect partner to Excel. It is a powerful, free tool for exploring, normalising and cleaning datasets, and extending data by accessing the internet through APIs. In this course we'll work through the various features of Refine, including importing data, faceting, clustering, and calling remote APIs, by working on a fictional but plausible humanities research project. #### You'll learn: - Download, install and run Open Refine - Import data from csv, text or online sources and create projects - Navigate data using the Open Refine interface - Explore data by using facets - Clean data using clustering - Parse data using GREL syntax - Extend data using Application Programming Interfaces (APIs) - Export project for use in other applications #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/refine101).** training@intersect.org.au Data Analysis, Open Refine
Mastering text with Regular Expressions

Have you ever wanted to extract phone numbers out of a block of unstructured text? Or email addresses. Or find all the words that start with “e” and end with “ed”, no matter their length? Or search through DNA sequences for a pattern? Or extract coordinates from GPS data?

Regular Expressions...

Keywords: Data Analysis, Regular Expressions

Mastering text with Regular Expressions https://dresa.org.au/materials/mastering-text-with-regular-expressions Have you ever wanted to extract phone numbers out of a block of unstructured text? Or email addresses. Or find all the words that start with “e” and end with “ed”, no matter their length? Or search through DNA sequences for a pattern? Or extract coordinates from GPS data? Regular Expressions (regexes) are a powerful way to handle a multitude of different types of data. They can be used to find patterns in text and make sophisticated replacements. Think of them as find and replace on steroids. Come along to this workshop to learn what they can do and how to apply them to your research. #### You'll learn: - Comprehend and apply the syntax of regular expressions - Use the http://regexr.com tool to test a regular expression against some text - Construct simple regular expressions to find capitalised words; all numbers; all words that start with a specific set of letters, etc. in a block of text - Craft and test a progressively more complex regular expression - Find helpful resources covering regular expressions on the web #### Prerequisites: Comprehend and apply the syntax of regular expressions Use the http://regexr.com tool to test a regular expression against some text Construct simple regular expressions to find capitalised words; all numbers; all words that start with a specific set of letters, etc. in a block of text Craft and test a progressively more complex regular expression Find helpful resources covering regular expressions on the web **For more information, please click [here](https://intersect.org.au/training/course/regex101).** training@intersect.org.au Data Analysis, Regular Expressions
Regular Expressions on the Command Line

Would you like to use regular expressions with the classic command line utilities find, grep, sed and awk? These venerable Unix utilities allow you to search, filter and transform large amounts of text (including many common data formats) efficiently and repeatably.

You'll learn:

  • find to...

Keywords: Data Analysis, Regular Expressions

Regular Expressions on the Command Line https://dresa.org.au/materials/regular-expressions-on-the-command-line Would you like to use regular expressions with the classic command line utilities find, grep, sed and awk? These venerable Unix utilities allow you to search, filter and transform large amounts of text (including many common data formats) efficiently and repeatably. #### You'll learn: - find to locate files and directories matching regexes. - grep to filter lines in files based on pattern matches. - sed to find and replace using regular expressions and captures. - awk to work with row- and column-oriented data. #### Prerequisites: This course assumes prior knowledge of the basic syntax of regular expressions. If you're new to regular expressions or would like a refresher, take our Mastering text with Regular Expressions course first. This course also assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. Take our Unix Shell and Command Line Basics course to get up to speed quickly. **For more information, please click [here](https://intersect.org.au/training/course/regex201).** training@intersect.org.au Data Analysis, Regular Expressions
Data Entry and Processing in SPSS

This hands-on training is designed to familiarize you with the interface and basic data processing functionalities in SPSS. We will examine several “must know” syntax commands that can help streamline data entry and processing. In addition, we will explore how to obtain descriptive statistics in...

Keywords: Data Analysis, SPSS

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

This hands-on training is designed to familiarize you further with the SPSS data analysis environment. In this session, we will traverse into the realm of inferential statistics, beginning with linear correlation and reliability. We will present a brief conceptual overview and the SPSS procedures...

Keywords: Data Analysis, SPSS

Exploring Chi-Square and correlation in SPSS https://dresa.org.au/materials/exploring-chi-square-and-correlation-in-spss This hands-on training is designed to familiarize you further with the SPSS data analysis environment. In this session, we will traverse into the realm of inferential statistics, beginning with linear correlation and reliability. We will present a brief conceptual overview and the SPSS procedures for computing Pearson's r and Spearman's Rho, followed by a short session on reliability . In the remainder of the session, we will explore the Chi-Square Goodness-of-Fit test and Chi-Square Test of Association for analysing categorical data. #### You'll learn: - Perform Pearson’s Correlation (r) Test - Perform Spearman’s Rho Correlation (⍴) Test - Carry out basic reliability analysis on survey items - Perform Chi-Square Goodness-of-Fit test - Perform Chi-Square Test of Association #### Prerequisites: In order to participate, attendees must have a licensed copy of SPSS installed on their computer. Speak to your local university IT or Research Office for assistance in obtaining a license and installing the software. This workshop is recommended for researchers and postgraduate students who have previously attended the Intersect’s Data Entry and Processing in SPSS training course. **For more information, please click [here](https://intersect.org.au/training/course/spss102).** training@intersect.org.au Data Analysis, SPSS
Databases and SQL

A relational database is an extremely efficient, fast and widespread means of storing structured data, and Structured Query Language (SQL) is the standard means for reading from and writing to databases. Databases use multiple tables, linked by well-defined relationships, to store large amounts...

Keywords: Data Management, SQL

Databases and SQL https://dresa.org.au/materials/databases-and-sql A relational database is an extremely efficient, fast and widespread means of storing structured data, and Structured Query Language (SQL) is the standard means for reading from and writing to databases. Databases use multiple tables, linked by well-defined relationships, to store large amounts of data without needless repetition while maintaining the integrity of your data. Moving from spreadsheets and text documents to a structured relational database can be a steep learning curve, but one that will reward you many times over in speed, efficiency and power. Developed using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - Understand and compose a query using SQL - Use the SQL syntax to select, sort and filter data - Calculate new values from existing data - Aggregate data into sums, averages, and other operations - Combine data from multiple tables - Design and build your own relational databases #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/sql101).** training@intersect.org.au Data Management, SQL
Getting Started with Tableau for Data Analysis and Visualisation

Tableau is a powerful data visualisation software that can help anyone see and understand their data. With the features to connect to almost any database, drag and drop to create visualizations, and share with a click, it definately makes thing easier.

This course is suitable for all researchers...

Keywords: Data Analysis, Tableau

Getting Started with Tableau for Data Analysis and Visualisation https://dresa.org.au/materials/getting-started-with-tableau-for-data-analysis-and-visualisation Tableau is a powerful data visualisation software that can help anyone see and understand their data. With the features to connect to almost any database, drag and drop to create visualizations, and share with a click, it definately makes thing easier. This course is suitable for all researchers and research students from any discipline. It provides step by step guides on how to visualise your research data on an interactive dashboard. #### You'll learn: - Import and combine data - Filter data - Create cross tabulation table - Create interactive plots including graph map - Create and design an interactive dashboard #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/tableau101).** training@intersect.org.au Data Analysis, Tableau
Unix Shell and Command Line Basics

The Unix environment is incredibly powerful but quite daunting to the newcomer. Command line confidence unlocks powerful computing resources beyond the desktop, including virtual machines and High Performance Computing. It enables repetitive tasks to be automated. And it comes with a swag of...

Keywords: Research Computing, Unix

Unix Shell and Command Line Basics https://dresa.org.au/materials/unix-shell-and-command-line-basics The Unix environment is incredibly powerful but quite daunting to the newcomer. Command line confidence unlocks powerful computing resources beyond the desktop, including virtual machines and High Performance Computing. It enables repetitive tasks to be automated. And it comes with a swag of handy tools that can be combined in powerful ways. Getting started is the hardest part, but our helpful instructors are there to demystify Unix as you get to work running programs and writing scripts on the command line. Every attendee is given a dedicated training environment for the duration of the workshop, with all software and data fully loaded and ready to run. We teach this course within a GNU/Linux environment. This is best characterised as a Unix-like environment. We teach how to run commands within the Bash Shell. The skills you'll learn at this course are generally transferable to other Unix environments. #### You'll learn: - Navigate and work with files and directories (folders) - Use a selection of essential tools - Combine data and tools to build a processing workflow - Automate repetitive analysis using the command line #### Prerequisites: The course has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/unix101).** training@intersect.org.au Research Computing, Unix
Collecting Web Data

Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated.

Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as...

Keywords: Data Management, Python

Collecting Web Data https://dresa.org.au/materials/collecting-web-data Web scraping is a technique for extracting information from websites. This can be done manually but it is usually faster, more efficient and less error-prone if it can be automated. Web scraping allows you to convert non-tabular or poorly structured data into a usable, structured format, such as a .csv file or spreadsheet. But scraping is about more than just acquiring data: it can help you track changes to data online, and help you archive data. In short, it's a skill worth learning. So join us for this web scraping workshop to learn web scraping, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. #### You'll learn: - The concept of structured data - The use of XPath queries on HTML document - How to scrape data using browser extensions - How to scrape using Python and Scrapy - How to automate the scraping of multiple web pages #### Prerequisites: A good knowledge of the basic concepts and techniques in Python. Consider taking our Learn to Program: Python and Python for Research courses to come up to speed beforehand. **For more information, please click [here](https://intersect.org.au/training/course/webdata201).** training@intersect.org.au Data Management, Python
Start Coding without Hesitation: Programming Languages Showdown

Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are...

Keywords: Programming, Python, R, MATLAB, Julia

Start Coding without Hesitation: Programming Languages Showdown https://dresa.org.au/materials/start-coding-without-hesitation-programming-languages-showdown Programming is becoming more and more popular, with many researchers using programming to perform data cleaning, data manipulation, data analytics, as well as creating publication quality plots. Programming can be really beneficial for automating processes and workflows. In this webinar, we are exploring four of the most popular programming languages that are widely used in academia, namely Python, R, MATLAB, and Julia. #### You'll learn: - Why use Programming - An overview of Python, R, MATLAB, and Julia - Code comparison of the four programming languages - Popularity and job opportunities - Intersect's comparison - General guidelines on how to choose the best programming language for your research #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/coding001).** training@intersect.org.au Programming, Python, R, MATLAB, Julia
A showcase of Data Analysis in Python and R: A case study using COVID-19 data

In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and...

Keywords: Programming, Python, R

A showcase of Data Analysis in Python and R: A case study using COVID-19 data https://dresa.org.au/materials/a-showcase-of-data-analysis-in-python-and-r-a-case-study-using-covid-19-data In all fields of research we are being confronted with a deluge of data; data that needs cleaning and transformation to be used in further analysis. This webinar demonstrates the effective use of programming tools for an initial analysis of COVID-19 datasets, with examples using both R and Python. #### You'll learn: - Cleaning up a dataset for analysis - Using Jupyter lab for interactive analysis - Making the most of the tidyverse (R) and pandas (python) - Simple data visualisation using ggplot (R) and seaborn (python) - Best practices for readable code #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/coding002).** training@intersect.org.au Programming, Python, R
Thinking like a computer: The Fundamentals of Programming

Human brains are extremely good at evaluating a small amount of information simultaneously, ignoring anomalies and coming up with an answer to a problem without much in the way of conscious thought. Computers on the other hand are extremely good at performing individual calculations, one at a...

Keywords: Programming

Thinking like a computer: The Fundamentals of Programming https://dresa.org.au/materials/thinking-like-a-computer-the-fundamentals-of-programming Human brains are extremely good at evaluating a small amount of information simultaneously, ignoring anomalies and coming up with an answer to a problem without much in the way of conscious thought. Computers on the other hand are extremely good at performing individual calculations, one at a time, and can keep the results in a large bank of short-term memory for quick recall. These two approaches are fundamentally different. Humans can only reasonably retain seven plus or minus two pieces of information in short-term memory, and new items push older items out, whereas a computer is hopeless when given multiple pieces of information simultaneously. Understanding this fact is key to being able to write instructions for computers - also known as programs – in a way that takes advantage of their strengths, and overcomes their drawbacks. Suitable for the programming novice, this webinar is good preparation for researchers wanting to learn how to program. #### You'll learn: - How a human solves tasks - How a computer solves tasks - Overview of programming concepts: - Variables - Loops - Conditionals - Functions - Data types #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/coding003).** training@intersect.org.au Programming