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://intersect.org.au/training/course/spss102
https://dresa.org.au/materials/exploring-chi-square-and-correlation-in-spss-d38c2067-302a-4194-80a2-71f2311f8756
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](https://intersect.org.au/training/course/spss101/) workshop.
**For more information, please click [here](https://intersect.org.au/training/course/spss102).**
training@intersect.org.au
Data Analysis, SPSS
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://intersect.org.au/training/course/excel201
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
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://intersect.org.au/training/course/spss102
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](https://intersect.org.au/training/course/spss101/) workshop.
**For more information, please click [here](https://intersect.org.au/training/course/spss102).**
training@intersect.org.au
Data Analysis, SPSS
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://intersect.org.au/training/course/rdmt001
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
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://intersect.org.au/training/course/refine101
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
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://intersect.org.au/training/course/webdata201
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](https://intersect.org.au/training/course/python101/) and [Python for Research](https://intersect.org.au/training/course/python110/) courses to come up to speed beforehand.
**For more information, please click [here](https://intersect.org.au/training/course/webdata201).**
training@intersect.org.au
Data Management, Python
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...
Data Visualisation in R
https://intersect.org.au/training/course/r202
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](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.
We also strongly recommend attending the [Data Manipulation in R](https://intersect.org.au/training/course/r201/) 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...
Data Manipulation and Visualisation in R
https://intersect.org.au/training/course/r203
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](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).**
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...
Introduction to Machine Learning using R: Introduction & Linear Regression
https://intersect.org.au/training/course/r205
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](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).**
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...
Introduction to Machine Learning using R: Classification
https://intersect.org.au/training/course/r206
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](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).**
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...
Introduction to Machine Learning using R: SVM & Unsupervised Learning
https://intersect.org.au/training/course/r207
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](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).**
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...
Exploring Chi-square and correlation in R
https://intersect.org.au/training/course/r210
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](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).**
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...
Traversing t tests in R
https://intersect.org.au/training/course/r211
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](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).**
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...
Exploring ANOVAs in R
https://intersect.org.au/training/course/r212
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](https://intersect.org.au/training/course/r101/), [Data Manipulation and Visualisation in R](https://intersect.org.au/training/course/r203/) workshops first.
**For more information, please click [here](https://intersect.org.au/training/course/r212).**
training@intersect.org.au
Programming, R
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://intersect.org.au/training/course/redcap101
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://intersect.org.au/training/course/redcap201
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 [Data Capture and Surveys with REDCap](https://intersect.org.au/training/course/redcap101/) workshop.
**For more information, please click [here](https://intersect.org.au/training/course/redcap201).**
training@intersect.org.au
Data Management, REDCap
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://intersect.org.au/training/course/regex101
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:
Keywords: Data Analysis, Regular Expressions
Regular Expressions on the Command Line
https://intersect.org.au/training/course/regex201
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://intersect.org.au/training/course/spss101
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
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://intersect.org.au/training/course/sql101
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://intersect.org.au/training/course/tableau101
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://intersect.org.au/training/course/unix101
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
Getting Started with Excel
We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors.
This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool,...
Keywords: Data Analysis, Excel
Getting Started with Excel
https://intersect.org.au/training/course/excel001
https://dresa.org.au/materials/getting-started-with-excel
We rarely receive the research data in an appropriate form. Often data is messy. Sometimes it is incomplete. And sometimes there's too much of it. Frequently, it has errors.
This webinar targets beginners and presents a quick demonstration of using the most widespread data wrangling tool, Microsoft Excel, to sort, filter, copy, protect, transform, aggregate, summarise, and visualise research data.
#### You'll learn:
- Introduction to Microsoft Excel user interface
- Interpret data using sorting, filtering, and conditional formatting
- Summarise data using functions
- Analyse data using pivot tables
- Manipulate and visualise data
- Handy tips to speed up your work
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/excel001).**
training@intersect.org.au
Data Analysis, Excel
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...
Thinking like a computer: The Fundamentals of Programming
https://intersect.org.au/training/course/coding003
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
From PC to Cloud or High Performance Computing
Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.
We may refer to both types as “large scale computing” - but what...
Keywords: Research Computing
From PC to Cloud or High Performance Computing
https://intersect.org.au/training/course/compute001
https://dresa.org.au/materials/from-pc-to-cloud-or-high-performance-computing
Most of you would have heard of Cloud and High Performance Computing (HPC), or you may already be using it. HPC is not the same as cloud computing. Both technologies differ in a number of ways, and have some similarities as well.
We may refer to both types as “large scale computing” - but what is the difference? Both systems target scalability of computing, but in different ways.
This webinar will give a good overview to the researchers thinking to make a move from their local computer to Cloud of High Performance Computing Cluster.
#### You'll learn:
- Introduction
- HPC vs Cloud computing
- When to use HPC
- When to use the Cloud
- The Cloud – Pros and Cons
- HPC – Pros and Cons
#### Prerequisites:
The webinar has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/compute001).**
training@intersect.org.au
Research Computing
Excel for Researchers
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,...
Keywords: Data Analysis, Excel
Excel for Researchers
https://intersect.org.au/training/course/excel101
https://dresa.org.au/materials/excel-for-researchers
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).**
training@intersect.org.au
Data Analysis, Excel
Version Control with Git
Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there...
Keywords: Data Management, Git
Version Control with Git
https://intersect.org.au/training/course/git101
https://dresa.org.au/materials/version-control-with-git
Have you mistakenly overwritten programs or data and want to learn techniques to avoid repeating the loss? Version control systems are one of the most powerful tools available for avoiding data loss and enabling reproducible research. While the learning curve can be steep, our trainers are there to answer all your questions while you gain hands on experience in using Git, one of the most popular version control systems available.
Join us for this workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.
#### You'll learn:
- keep versions of data, scripts, and other files
- examine commit logs to find which files were changed when
- restore earlier versions of files
- compare changes between versions of a file
- push your versioned files to a remote location, for backup and to facilitate collaboration
#### Prerequisites:
The course has no prerequisites.
**For more information, please click [here](https://intersect.org.au/training/course/git101).**
training@intersect.org.au
Data Management, Git
Getting started with HPC using PBS Pro
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis...
Keywords: Research Computing, HPC
Getting started with HPC using PBS Pro
https://intersect.org.au/training/course/hpc201
https://dresa.org.au/materials/getting-started-with-hpc-using-pbs-pro
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?
High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using PBS Pro.
#### You'll learn:
- Connect to an HPC cluster
- Use the Unix command line to operate a remote computer and create job scripts
- Submit and manage jobs on a cluster using a scheduler
- Transfer files to and from a remote computer
- Use software through environment modules
- Use parallelisation to speed up data analysis
- Access the facilities available to you as a researcher
- This is the PBS Pro version of the Getting Started with HPC course.
#### Prerequisites:
This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our [Unix Shell and Command Line Basics](https://intersect.org.au/training/course/unix101/) course.
**For more information, please click [here](https://intersect.org.au/training/course/hpc201).**
training@intersect.org.au
Research Computing, HPC
Getting started with HPC using Slurm
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis...
Keywords: Research Computing, HPC
Getting started with HPC using Slurm
https://intersect.org.au/training/course/hpc202
https://dresa.org.au/materials/getting-started-with-hpc-using-slurm
Is your computer's limited power throttling your research ambitions? Are your analysis scripts pushing your laptop's processor to its limits? Is your software crashing because you’ve run out of memory? Would you like to unleash to power of the Unix command line to automate and run your analysis on supercomputers that you can access for free?
High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This course provides a hands on introduction to running software on HPC infrastructure using Slurm.
#### You'll learn:
- Connect to an HPC cluster
- Use the Unix command line to operate a remote computer and create job scripts
- Submit and manage jobs on a cluster using a scheduler
- Transfer files to and from a remote computer
- Use software through environment modules
- Use parallelisation to speed up data analysis
- Access the facilities available to you as a researcher
- This is the Slurm version of the Getting Started with HPC course.
#### Prerequisites:
This course assumes basic familiarity with the Bash command line environment found on GNU/Linux and other Unix-like environments. To come up to speed, consider taking our [Unix Shell and Command Line Basics](https://intersect.org.au/training/course/unix101/) course.
**For more information, please click [here](https://intersect.org.au/training/course/hpc202).**
training@intersect.org.au
Research Computing, HPC
Parallel Programming for HPC
You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you've submitted batch jobs.
Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance...
Keywords: Research Computing, HPC
Parallel Programming for HPC
https://intersect.org.au/training/course/hpc301
https://dresa.org.au/materials/parallel-programming-for-hpc
You have written, compiled and run functioning programs in C and/or Fortran. You know how HPC works and you've submitted batch jobs.
Now you want to move from writing single-threaded programs into the parallel programming paradigm, so you can truly harness the full power of High Performance Computing.
#### You'll learn:
- OpenMP (Open Multi-Processing): a widespread method for shared memory programming
- MPI (Message Passing Interface): a leading distributed memory programming model
#### Prerequisites:
To do this course you need to have:
A good working knowledge of HPC. Consider taking our
Getting Started with HPC using PBS Pro course to come up to speed beforehand.
Prior experience of writing programs in either C or Fortran.
**For more information, please click [here](https://intersect.org.au/training/course/hpc301).**
training@intersect.org.au
Research Computing, HPC