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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](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...

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](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...

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](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...

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](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...

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](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...

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](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...

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](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...

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](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://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 [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
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](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
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
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
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://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
Survey Tools in Research: REDCap and Qualtrics

Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your...

Keywords: Data Management, REDCap, Qualtrics

Survey Tools in Research: REDCap and Qualtrics https://dresa.org.au/materials/survey-tools-in-research-redcap-and-qualtrics Now more than ever researchers are needing to embrace electronic data capture methods to keep their research moving in the midst of social distancing restrictions and decreased access to survey participants. Using a research specific survey tool can not only solve this problem, but also set your research up for success through intuitive data collection and validation, scheduling and reporting. This webinar will introduce and compare two of the most popular research tools for the collection of survey data and patient records: REDCap and Qualtrics. #### You'll learn: - Electronic Data Capture: Surveys vs Forms - Confidential vs Anonymous data collection - Strengths and weaknesses of Qualtrics and REDCap - Real-life use cases for each tool - Using survey tools for longitudinal studies #### Prerequisites: The webinar has no prerequisites. **For more information, please click [here](https://intersect.org.au/training/course/surveys001).** training@intersect.org.au Data Management, REDCap, Qualtrics
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://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
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://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
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://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://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://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
Learn to Program: Julia

Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...

Keywords: Programming, Julia

Learn to Program: Julia https://dresa.org.au/materials/learn-to-program-julia Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages! This workshop expects that you are coming to Julia with some experience in the basic concepts of programming in another language. It is designed to help you migrate the basic concepts of programming that you already know to the Julia context. Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. #### You'll learn: - Introduction to the JupyterLab interface for programming - Basic syntax and data types in Julia - How to load external data into Julia - Creating functions (FUNCTIONS) - Repeating actions and analysing multiple data sets (LOOPS) - Making choices (IF STATEMENTS - CONDITIONALS) - Ways to visualise data using the Plots library in Julia #### Prerequisites: Some experience with the basic concepts of programming in another language needed to attend this course. It is an intensive course that is designed to help you migrate the basic concepts of programming that you already know to the Julia context in half a day instead of a full day. If you don't have any prior experience in programming, please consider attending one of the [Learn to Program: Python](https://intersect.org.au/training/course/python101/), [Learn to Program: R](https://intersect.org.au/training/course/r101/) or [Learn to Program: MATLAB](https://intersect.org.au/training/course/matlab101/) prior to this course. We also strongly recommend attending the Start Coding without Hesitation: Programming Languages Showdown and Thinking like a computer: The Fundamentals of Programming webinars. Recordings of previously delivered webinars can be found [here](https://intersect.org.au/training/webinars/). **For more information, please click [here](https://intersect.org.au/training/course/julia101).** training@intersect.org.au Programming, Julia
Beyond the Basics: Julia

Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance...

Keywords: Programming, Julia

Beyond the Basics: Julia https://dresa.org.au/materials/beyond-the-basics-julia Julia is a high-level, high-performance dynamic programming language with more than 4,000 external libraries available. Julia allows you to range from tight low-level loops and conditionals, up to a high-level programming style, with its performance approaching and often matching the performance of the fastest programming languages! This workshop explores the more advanced features of functions in Julia, introduces widely used tools within Julia, as well as demonstrates the speed of Julia by benchmarking functions and different styles of scripting within Julia. Join us for this live coding workshop where we write programs that produce results, using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. #### You'll learn: - Understand the role of Types within Julia - Create functions with complex arguments - Demonstrate programming patterns of list comprehension, pipes, and anonymous functions. - Benchmark Julia code and understand how to make it fast #### Prerequisites: If you already have experience with programming, please check the topics covered in the [Learn to Program: Julia](https://intersect.org.au/training/course/julia101/) 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/julia201).** training@intersect.org.au Programming, Julia
Getting started with NVivo for Windows

Does your research see you working through unstructured and non-numerical data? With the ability to collect, store and analyse different data types all in the one location makes, it's easy to see why NVivo is becoming the tool of choice for many researchers.

NVivo allows researchers to simply...

Keywords: Data Analysis, NVivo

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