Events and training
La Trobe researchers and research students have access to a wide range of specialised courses, from beginner through to advanced levels in High-Performance Computing, Excel for research, data management and visualisation, cleaning and exploring data, and more. There is no cost to La Trobe researchers and research students for the majority of these courses. Many of these workshops are presented by our partners, Intersect Australia Limited.
Research Education and Development (RED) have an extensive workshop and seminar program listed on their website. There are also a range of workshops and training opportunities hosted by organisations external to La Trobe, which can be accessed by clicking here.
For Digital Research support, visit the online Digital Research Drop in session on the second Tuesday of each month, 1 - 2 pm
2024 Courses
Campus: M = Melbourne, B = Bendigo, AW = Albury Wodonga, MIL = Mildura, S = Shepparton, C = City, O = Online
| Date | Session title | Campus | Booking |
|---|---|---|---|
| Day 1: Thursday 11 April and Day 2: Friday 12 April 10:00am - 1:00pm | 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 the highly regarded Software Carpentry Foundation. | O | Expression of interest |
| Day 1: Tuesday 16 April and Day 2: Wednesday 17 April 10:00am - 1:00pm | Data Manipulation and Visualisation in Python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. In this workshop, you will explore DataFrames in depth (using the pandas library), learn how to manipulate, explore and get insights from your data (Data Manipulation), as well as how to deal with missing values and how to combine multiple datasets. You will also explore different types of graphs and learn how to customise them using two of the most popular plotting libraries in Python, matplotlib and seaborn (Data Visualisation). We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. | O | Expression of interest |
| Friday 19 April 10:00am - 1:00pm | 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. | O | Expression of interest |
| Tuesday 23 April | Data Capture and Surveys in 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. | O | Expression of interest |
| Friday 26 April 10:00am - 1:00pm | Longitudinal Trials in REDCap REDCap is a powerful and extensible application for managing and running longitudinal data collection activities. With powerful features such as organising data collections instruments into predefined events, you can shepherd 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. | O | Expression of interest |
| Day 1: Tuesday 30 April and Day 2: Wednesday 1 May 10:00am - 1:00pm | 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. | O | Expression of interest |
| Day 1: Tuesday 7 May and Day 2: Wednesday 8 May 10:00am - 1:00pm | Introduction to Machine Learning using Python: 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 Python programming language and its scientific computing packages. | O | Expression of interest |
| Day 1: Tuesday May 14 and Day 2: Wednesday 15 May 10:00am - 1:00pm | 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. | O | Expression of interest |
| Day 1: Tuesday 21 May and Day 2: Wednesday 22 May 10:00am - 1:00pm | Introduction to Machine Learning using Python: 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 Python programming language and its scientific computing packages. | O | Expression of interest |
| Day 1: Tuesday 28 May and Day 2: Wednesday 29 May 10:00am - 1:00pm | 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. | O | Expression of interest |
| Day 1: Monday 3 June and Day 2: Tuesday 4 June 9:00am - 3:30pm | Basic Statistics with R Learn a simple yet powerful way to design and carry out statistical analyses in R - like a real statistician! This two-day workshop introduces statistical concepts in a non-technical way and emphasises their practical application in R. The workshop will provide plenty of opportunities to gain hands-on experience and access support from our expert statisticians. This course is very popular so book early. | O & M | |
| Day 1: Tuesday 4 June and Day 2: Wednesday 5 June 10:00am - 1:00pm | Introduction to Machine Learning using Python: 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 Python programming language and its scientific computing packages. | O | Expression of interest |
| Day 1: Wednesday 5 June and Day 2: Thursday 6 June 9:00am - 3:30pm | Basic Statistics with Stata Learn a simple yet powerful way to design and carry out statistical analyses in Stata. This workshop introduces statistical concepts in a non-technical way and emphasises their practical application in Stata. Participants will have plenty of opportunities to gain hands-on experience and access in-class support. | O & M | |
| Friday 7 June 10:00am - 1:00pm | Getting started with Nvivo (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. | O | Expression of interest |
| Day 1: Tuesday 11 June and Day 2: Wednesday 12 June 10:00am - 1:00pm | 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. | O | Expression of interest |
| Day 1: Monday 24 June and Day 2: Tuesday 25 June 9:00am - 3:30pm | Intermediate Statistics with R: Regression Analysis The thrilling sequel to the extremely popular 'Basic Statistics with R' takes statistics to the next level! Amanda shakes it up in this two-day workshop introducing various types of regression models in a non-technical way and demonstrates their practical application in R. The workshop will provide plenty of opportunities to gain hands-on experience and access support from our expert statisticians. | O & M | |
| Wednesday 26 June 9:00am - 3:00pm | Sample Size Calculation Workshop | O & M |
More workshops - watch this space!
| Workshop title and description |
|---|
Good Clinical Practice (GCP): An Introduction Explores the crucial role of Good Clinical Practice in conducting great research. During this event, we will dive into the world of GCP and its impact on research outcomes. You will have the opportunity to learn from experts in the field, participate in interactive workshops, and network with like-minded individuals. Discover the latest advancements, best practices, and key strategies for ensuring GCP compliance in your research. |
Good Clinical Practice (GCP): A Refresher Are you looking to refresh your understanding of GCP? Is your current GCP certification due to expire? This event is perfect for you! This online session will cover topics including the principles of GCP, ethical considerations, regulatory requirements, and best practices in clinical trials. Our expert speakers will share their knowledge and experiences, ensuring an engaging and interactive session. Please note that prior to the event you will need to provide evidence of past GCP certification to be eligible for the refresher training session. |
| Learn to Program: Python Python has deservedly become a popular language for scientific computing. It has all the friendly features and conveniences you’d expect of a modern programming language, and also a rich set of libraries for working with data. We teach using Jupyter notebooks, which allow program code, results, visualisations and documentation to be blended seamlessly. Perfect for sharing insights with others while producing reproducible research. Join us for this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. |
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. |
| Advanced HPC: Parallel Programming - This intensive full-day course introduces different parallel programming methods: OpenMP as a widespread method for a shared memory programming model and MPI as the standard for a distributed memory programming model. It is targeted at C and Fortran programmers |
| Statistical Comparisons using R - This practical workshop will help participants to choose and use the appropriate standard statistical test for their data by introducing key concepts of inferential statistics in R. Participants will learn how to compute and interpret hypothesis tests for popular statistical models such as correlation, contingency tables, chi-square test, t-test and ANOVA. |
| Basic Statistics with R - Learn a simple yet powerful way to design and carry out statistical analyses in R - like a real statistician!. This three half day workshop introduces statistical concepts in a non-technical way and emphasises their practical application in R. The workshop will provide plenty of opportunities to gain hands-on experience and access support from our expert statisticians. This course is very popular so book early. |
Intermediate Statistics with R - The thrilling sequel to the extremely popular 'Basic Statistics with R' takes statistics to the next level!. Amanda shakes it up in this three half day workshop introducing various types of regression models in a non-technical way and demonstrates their practical application in R. The workshop will provide plenty of opportunities to gain hands-on experience and access support from our expert statisticians. |
| Cleaning and Exploring your 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 research 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. In this course you'll work through the various features of Refine by working on a fictional but plausible humanities research project. |
| Data visualisation with Google Fusion Tables - This course is ideal for researchers who work with large data sets and want to convey their research outcomes clearly and persuasively in a visual manner. By creating a heat map by merging geospatial data and crime statistics, participants will gain skills in visualisation that they can apply to their research. |
| Excel Fu for Researchers - Do you have large amounts of data that is messy, incomplete and contains errors? During this workshop you will learn how to use Excel to import, sort, filter, copy, protect, transform, summarise, merge, and visualise research data. Access the course outline here. |
| 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. |
| G*Power Workshop: Sample size analysis for researchers - G*Power is a free statistical software package for power and sample size analysis. It offers point-and-click functionality and covers a wide variety of statistical tests. Presented by the Statistics Consultancy Platform, this full day G*Power workshop includes concepts of statistical power and relevant statistical tests presented in a non-technical way. Designed to be hands-on, the workshop focuses on the practical application of statistical methods. |
| Introduction to Unix - Do you plan to use high performance computing for bioinformatics? Knowledge of the Unix operating system is fundamental to being productive on HPC systems. Command line confidence unlocks powerful computing resources beyond the desktop. It enables repetitive tasks to be automated and it comes with a swag of handy tools that can be combined in powerful ways. This workshop will introduce you to the fundamental Unix concepts and teach you to run programs and write scripts through a series of hands-on exercises. |
Introduction to Programming using Matlab - MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits optimized for solving engineering and scientific problems.Built-in graphics make it easy to visualize and gain insights from data and a vast library of prebuilt toolboxes lets you get started right away with algorithms essential to your domain. |
| Introduction to High Performance Computing (HPC) - HPC allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This 1-day course will introduce you to the Unix environment and show you how to transfer your data onto, and run software on HPC infrastructure. |
| Introductory Programming Workshop: Python, Unix and Git - Many research fields can benefit from automation and programmatic techniques, ranging from the humanities and social sciences through biomedical sciences and engineering. The tools and techniques taught in this workshop will be of use to anyone who currently uses a computer for their research. |
Introduction to Unix for HPC - High-Performance Computing (HPC) allows you to accomplish your analysis faster by using many parallel CPUs and huge amounts of memory simultaneously. This 2-day course will introduce you to the Unix environment and show you how to transfer your data onto, and run software on HPC infrastructure. |
| Managing Data Capture and surveys in 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. Access the course outline here. |
| Nectar Research Cloud - Find out what cloud computing is, how it works, how it can benefit your research and what types of service Nectar offers. This course will provide hands-on instructions on how to launch an instance on the Cloud, connect to it, configure it and set up storage so that it can be accessed from the instance and remotely from the office computer. |
| Office365 and One Drive, Delve and Sway - This session will help you to understand the capabilities of Office 365, how to access the apps and apply them to your research or work. |
| Powerful text searching and matching with Regexes - 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. |
Regular Expressions on Command - 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. |
| Software Carpentry: Introduction to Unix Shell - Do you want to unlock powerful computing resources beyond the desktop, including virtual machines and High Performance Computing? Unix can enable repetitive tasks to be automated and it comes with a swag of handy tools that can be combined in powerful ways to help with your research. |
Software Carpentry: Introduction to Matlab - MATLAB is an incredibly powerful programming environment with a rich set of analysis toolkits optimized for solving engineering and scientific problems. Built-in graphics make it easy to visualize and gain insights from data and a vast library of prebuilt toolboxes lets you get started right away with algorithms essential to your domain. |
| Software Carpentry: Introduction to programming with Python - This one day workshop is aimed at researchers and research students who would like to start learning to code in the Python programming language, a popular language for scientific computing. |
| Learn to Program: R - R is quickly gaining popularity as a programming language of choice for statisticians, data scientists and researchers. It has an excellent ecosystem including the powerful RStudio development environment and the Shiny web application framework, but getting started with R can be challenging, particularly if you’ve never programmed before. That’s where this introductory course comes in. We teach using RStudio, which allows program code, results, visualisations and documentation to be blended seamlessly. Join us for a live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation. |
| Software Carpentry: Introduction to 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. |
Using Databases and SQL - Do you need a better way to store your structured research data? 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 spread sheets and text documents to a structured relational database will reward you many times over in speed, efficiency and power. |
| Basic statistics with STATA - This workshop introduces statistical concepts in a non-technical way and emphasises their practical application in STATA. The workshop will provide plenty of opportunities to gain hands-on experience and to access in-class support. |