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Workshops and training

Expand your data skills
We run over 80 free introductory to advanced training courses spanning data science, statistics, programming, bioinformatics, research computing, and research data management.

Many of our services are available free of charge to University researchers, research students, and affiliates. While workshops and training may be considered to customers external to the University, a fee-for-service arrangement may apply. Please contact us (sih.info@sydney.edu.au) for more information.

Topics


Statistics

Presents methods and concepts that help create world changing research worthy of top tier journals.

A research workflow from hypothesis generation all the way to publication will be presented. This workshop covers both the statistical and non-statistical methods and concepts that help create world changing research worthy of top tier journals. We will show some of the resources available from both Sydney Informatics Hub and across the University that help you along the way.

Statistical concepts will be covered in detail, some of which are: how experimental design fits into hypothesis generation and your final publication; specific tips on how to store data efficiently- which can save weeks of time; Exploratory Data Analysis (EDA) – which is how data cleaning and outliers are found along with developing a graphical model of your data which then shows you what numerical statistical method is most appropriate.

We will also showcase some of the more advanced analysis to give you an idea of what is possible.

Note that this workshop does not require knowledge of or use of specific statistics software.  The analysis methods may be performed using a wide range of commonly available software.

Open to University of Sydney staff, students and research affiliates
Prerequisites No previous knowledge of statistical methods is required.
Resources You must bring your own laptop.
Duration 90 minutes

This presentation and workshop focus on the key aspects of experimental design that researchers and students may need to apply.

The workshop will include the following topics

  • the research question and experimental design
  • experimental validity
  • randomisation and bias
  • blinding and bias
  • blocking and confounding
  • fixed and random effects
  • replication, experimental units
  • exercise

Open to University of Sydney staff, students and research affiliates
Prerequisites No previous knowledge is assumed.
Resources N/A
Duration 90 minutes

This workshop covers the theory and concepts of power analysis and includes worked examples using G*Power software.  Attendees will follow the examples on their own laptops.

Open to University of Sydney staff, students and research affiliates.
Prerequisites Knowledge of basic statistics is recommended.
Resources Bring your own laptop with G*Power software installed.
Duration 90 minutes

Linear Models 1: Linear regression, ANOVA, ANCOVA and repeated measures (a simple mixed model), focuses on Practical Data Analysis by presenting Statistical Workflows appliable in any software for 4 of the most common univariate analyses: linear regression, ANOVA, ANCOVA, and repeated measures (a simple mixed model). All assuming a normal (gaussian) residual. These workflows can be easily extended to more complex models. The R code used to create output is also included. ​

This is the first in a series of workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic and count (poisson) regression. We will show how all of these analyses can be performed using the same easy to understand Generalised Linear Mixed Model framework (GLMM). As well as how these methods can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. ​

The material is organised around Statistical Workflows appliable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results.

Open to University of Sydney staff, students and research affiliates.
Prerequisites Knowledge of basic statistics is recommended.
Resources Bring your own laptop. If specific software is required, this will be advised for each individual workshop.
Duration 90 minutes

Linear Models 2: Logistic and Poisson/count regression-an introduction to Generalised Linear Models (GLM), focuses on Practical data analysis by presenting statistical workflows appliable in any software for two of the more common GLMM’s: Logistic regression for binary data (using a Binomial distribution); and Poisson/count regression for count data (using a Poisson distribution). The GLM framework is also described in detail. The R code used to create output is also included. ​

This is the second in a series of workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic and count (Poisson) regression. We will show how all of these analyses can be performed using the same easy to understand Generalised Linear Model framework (GLM). As well as how these methods can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. ​

The material is organised around statistical workflows appliable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results.

Open to University of Sydney staff, students and research affiliates.
Prerequisites Knowledge of basic statistics is recommended.
Resources Bring your own laptop. If specific software is required, this will be advised for each individual workshop.
Duration 90 minutes

Discusses Advanced Topics such as:

  • Reporting and Interpretation (Estimated Marginal Means, Confidence Intervals, Multiple Comparisons)
  • Model Parametrisation using the Design Matrix (dummy coding, effects coding, indicator variable vs interaction models)
  • Complex Mixed Models

This is the third in a series of workshops for researchers interested in statistical methods such as linear regression, ANOVA, ANCOVA, mixed models, logistic and count (Poisson) regression. We will show how all  these analyses can be performed using the same easy to understand Generalised Linear Model framework (GLM). As well as how these methods can be used to analyse experimental designs such as Control vs Treatment, Randomised Control Trials (RCT’s), Before After Control Impact (BACI) analysis, repeated measures plus many more. ​

The material is organised around Statistical Workflows appliable in any software that give step by step instructions on how to do the analysis including assumption testing, model interpretation and presentation of results.

Open to University of Sydney staff, students and research affiliates.
Prerequisites It is recommended that attendees are familiar with concepts of Linear Models explained in Linear Models 1 and 2 workshops.
Resources Bring your own laptop. If specific software is required, this will be advised for each individual workshop.
Duration 90 minutes

A statistical model is used to describe the mathematical relationship between the dependent response variable and predictor variables while incorporating uncertainty and subject matter knowledge. This workshop examines and illustrates the key aspects and strategies of model building to help you avoid common pitfalls, erroneous models and incorrect conclusions. In general these strategies are useful for any statistical model building. This workshop will provide generalised linear regression model examples. The focus will be on practical application of concepts so mathematical descriptions will be kept to a minimum.

  • causal diagrams 
  • missing values 
  • multi-collinearity 
  • interactions 
  • polynomials 
  • splines 
  • selecting a strategy (all possible/best subset, forward, backward, stepwise) 
  • comparing models 
  • evaluate model reliability 
  • presenting the results
Open to University of Sydney staff, students and research affiliates
Prerequisites Prior experience with statistical modelling is assumed, as the basics of regression modelling will not be covered. Please consider attending Linear Models 1 and/or Linear Models 2 workshops to come up to speed beforehand. Note that this workshop does not require knowledge of or use of specific statistics software.  The analysis methods may be performed using a wide range of commonly available software.
Resources Bring your own laptop
Duration 90 minutes
   

 

     

Learn how to use SPSS to import and manipulate data, perform statistical analysis including Chi-Square and correlation, and visualise results.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more.

 

A systematic review answers a defined research question by examining all the available evidence that fit specified criteria.  A meta-analysis is a rigorous statistical approach to combining and analyzing the chosen evidence.

In this workshop we will be examining the process of performing a meta-analysis, focusing in particular on key statistical concepts such as heterogeneity and  Fixed and Random effects modelling.

Open to University of Sydney staff, students and research affiliates.
Prerequisites Knowledge of basic statistics is recommended. Basic knowledge of R (programming language) is desirable but not required.
Resources Bring your own laptop. If you want to practice the example duing the workshop you will need to have R and RStudio installed.
Duration 90 minutes

Survival analysis is used when you want to measure the time elapsed up to when a specified event occurs. It is commonly used in studies where subjects are followed until death occurs, hence the name.

In this workshop we will introduce some key concepts pertaining to survival analysis including censoring of cases, the survival function, and the hazard ratio estimator.  The Kaplan Meier survival curve will be explained through a worked example and the technique of Cox proportional hazards regression will be introduced using the same example dataset.  

Participants will be provided with software code in SPSS and R to reproduce the analysis presented in the workshop.

Open to: University of Sydney staff, students and research affiliates.
Pre-requisites: Knowledge of basic statistics is recommended.
Resources: Bring your own laptop. If you want to practice the example during the workshop you will need to be able to run SPSS syntax or R code.  This is optional.
Duration: 90 minutes

 

This workshop is full of practical tips and guidelines on how to design, field and analyse standard surveys. Some of the topics considered will be: how to design a survey, line vs discrete scales, the effect of colour, optimal discrete/LIKERT scales, pros and cons of common analyses (e.g. linear and logistic regression) and optimal ways to export data. The material is software agnostic and can be applied in any software.

Open to University of Sydney staff,  students and research affiliates​
Prerequisites No previous knowledge of statistical methods is required.
Resources N/A
Duration 90 minutes

This workshop builds on the material in Surveys 1. It explores topics including: questionnaire validation and index creation using methods such as Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), using Structural Equation Modelling (SEM), and Conjoint models such as Choice modelling.  The material is software agnostic and can be applied in any software.

Open to University of Sydney staff,  students and research affiliates​
Prerequisites No previous knowledge of statistical methods is required.
Resources N/A
Duration 90 minutes

In multivariate statistics we simultaneously model and estimate variability in more than one outcome variable often in order to examine the relationship between outcome variables. This workshop examines the key aspects of moving from univariate to multivariate analysis, and the situations and scenarios where multivariate analysis is typically applied. The focus will be on practical application of concepts through examples.

Topics covered will include:

  • Multivariate distributions
  • Multivariate hypothesis testing
  • Dimension reduction techniques including PCA and factor analysis
Open to University of Sydney staff, students and research affiliates
Prerequisites Prior experience with statistical modelling is assumed, as the basics of regression modelling will not be covered. Please consider attending Linear Models 1 and/or Linear Models 2 workshops to come up to speed beforehand. Note that this workshop does not require knowledge of or use of specific statistics software.  The analysis methods may be performed using a wide range of commonly available software
Resources Bring your own laptop
Duration 90 minutes

Programming

Seeking a friendly introduction to programming or the Unix command line? Already writing scripts but want to fill in the blanks in your knowledge of programming? New to R and would like to get a sense of its capabilities? Have you mistakenly overwritten programs or data and want to learn techniques to avoid doing it again?

This course is facilitated by Intersect Australia. As a member institution, Sydney University staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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In this live coding workshop we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

In this live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

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.

This course is facilitated by Intersect Australia.

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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.

This course is facilitated by Intersect Australia. Learn more

 

A live coding workshop where we write programs that produce results, using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

Learn about the fundamental concepts in programming using MATLAB and apply them to analyse a sample research dataset.

This course is facilitated by Intersect Australia. Learn more

Learn about the fundamental concepts in programming using R and apply them to analyse a sample research

This course is facilitated by Intersect Australia. Learn more

Learn about the fundamental concepts in programming using Python and apply them to analyse a sample research dataset. 

This course is facilitated by Intersect Australia. Learn more

Research computing

Learn about the University’s High Performance Computer (HPC) ‘Artemis’, including directory structure, software, and how to submit and monitor compute jobs using the PBS Pro scheduling software. Artemis is available at no cost to University of Sydney staff and students.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites Competency on the Unix/Linux command line. If you are interested in learning HPC but have no Unix/Linux command-line skills, you must first take an ‘Introduction to Unix/Linux’ course. 
Resources

You must bring your own laptop.

Related courses This course is designed as a 2-part session, with Introduction to the Artemis HPC in the morning and Introduction to data transfer and the research data store in the afternoon. We recommend you register for both, however you may take these courses on separate days. We also recommend following up with ‘Intermediate HPC (Automation)’.

Learn how to transfer data between your local computer, an external source, the Research Data Store (RDS) and the Artemis HPC. Learn how to back up Artemis HPC output onto the RDS.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites Competency on the Unix/Linux command line. If you are interested in learning HPC but have no Unix/Linux command-line skills, you must first take an ‘Introduction to Unix/Linux’ course.
Resources

You must bring your own laptop.

Related courses This course is designed as a 2-part session, with Introduction to the Artemis HPC in the morning and this course in the afternoon. We recommend you register for both, however you may take these courses on separate days. We also recommend following up with ‘Intermediate HPC (Automation)’.

This course introduces GPU computing, and running GPU jobs on Artemis and other HPC systems. The University of Sydney’s Artemis HPC hosts several NVIDIA V100 GPUs. This course will help you to understand basic concepts of GPU programming. 

  • Learn fundamentals of basic CUDA code, and write and run examples using C/CUDA, Matlab, and Python.
  • Undertake practical applications in Deep Learning using Python, Tensorflow, and Keras.
  • Learn how to set up suitable environments on Artemis for GPU-enabled applications to run, and how to run and submit jobs on the Artemis HPC GPU queue.
Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites Competency on the Unix/Linux command line. If you are interested in learning HPC but have no Unix/Linux command-line skills, you must first take an ‘Introduction to Unix/Linux’ course.
Resources

You must bring your own laptop.

Related courses This course is designed as a 2-part session, with Introduction to the Artemis HPC in the morning and this course in the afternoon. We recommend you register for both, however you may take these courses on separate days. We also recommend following up with ‘Intermediate HPC (Automation)’.

Learn how to submit jobs to Artemis from Matlab running on your own computer.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites A basic understanding of Matlab is assumed. We also highly recommend that you have taken the ‘Introduction to the Artemis HPC’ course, unless you already have experience using Artemis. These are scheduled regularly on campus.
Resources

You must have MATLAB R2017a installed if you wish to complete the training examples. No other version of MATLAB will work with the version of the MDCS currently installed on Artemis.

This course is designed to transition researchers from local Python development and execution to tailor code for High Performance Computing (traditional and cloud) using specific libraries, functions and common implementations.

  • gain experience with best practices for structuring code and testing modular structure and workflows
  • learn about the libraries, data structures, and functions used for Python multiprocessing
  • explore commonly used codes to solve common problems such as deep learning, parallel computing, multi-threaded applications
  • utilise advanced libraries that outperform (in speed/ability to handle large data/design)
Open to Staff, research students, and affiliates with a valid University of Sydney Unikey.
Prerequisites

Competency with high performance computing environments, submitting and running jobs, comfortable moving data between local and remote machines. Fundamental Python experience with basic grasp of functions, variables, syntax.

These prerequisites can be satisfied by attending these course regularly run on campus:

Resources

You must bring your own laptop. Contact us if you need to borrow one for the course. You must have a Python environment installed with the required modules. Please refer to the course notes for installation and versioning instructions.

This course is designed to transition researchers from local R development and execution to tailor code for High Performance Computing (traditional and cloud) using specific libraries, functions and common implementations.

  • gain experience with best practices for structuring code and testing modular structure and workflows
  • learn about the libraries, data structures, and functions used for R multiprocessing
  • explore commonly used codes to solve common problems such as deep learning, parallel computing, multi-threaded applications
  • utilise advanced libraries that outperform (in speed/ability to handle large data/design).
Open to Staff, research students, and affiliates with a valid University of Sydney Unikey.
Prerequisites

Competency with high performance computing environments, submitting and running jobs, comfortable moving data between local and remote machines. Fundamental Python experience with basic grasp of functions, variables, syntax.

These prerequisites can be satisfied by attending these course regularly run on campus:

Resources

You must bring your own laptop. Contact us if you need to borrow one for the course. You must have a Python environment installed with the required modules. Please refer to the course notes for installation and versioning instructions.

OpenFOAM is an opensource computational fluid dynamic found in engineering and science (chemistry) (For example heat transfer, turbulence, solid mechanics and it impact on design in manufacturing process etc..). The recent popularity of OpenFOAM can be attributed to it being free, having a versatile and easily implemented syntax and being adopted in both academic and commercial applications. ANSYS Fluent is a commercial package with similar capabilities.

In this course we will cover the basics of each software. Run through setting up code and using demos on your local machine before executing jobs in a High Performance Computing Environment. You will also learn about visualising results with Paraview.

Open to Staff, research students, and affiliates with a valid University of Sydney Unikey
Prerequisites Competency with high performance computing environments, submitting and running jobs, comfortable moving data between local and remote machines, will be beneficial but not mandatory as we will cover these fundamentals in the workshop. If you have no prior experience with these kind of environments then it is recommend to complete Intro to HPC.
Resources

You must bring your own laptop. Contact us if you need to borrow one for the course.

OpenFoam and ANSYS Fluent must be installed. Please refer to the course notes for installation and versioning instructions.

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.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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We teach how to run commands within the Bash Shell.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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This course provides a hands on introduction to running software on HPC infrastructure.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

Pre-requisites: Assumed knowledge is basic Unix/Linux and Artemis HPC or other HPC running PBS.

This course is for new users of the National Compute Infrastructure’s (NCI’s) high performance computer, Gadi. This will be a webinar/live-demo style course and users with access to Gadi are welcome to follow along. Learn about: 

  • How to get access and where to get help; 
  • Gadi’s hardware, queues and the filesystem; 
  • Running compute jobs; 
  • How to account compute resources; 
  • How to install software; 
  • Tips for optimising code. 

Researchers who were recently awarded or are interested in applying for time on Gadi through Schemes (e.g. National Compute Merit Allocation Scheme or NCMAS, SIH HPC Allocation Schemes) are encouraged to attend.     

Data science

A series of short 1-1.5 hour online training sessions that showcase the tools and techniques we use internally at SIH:

  • Publication-ready tables in R
  • How fast is your R code: an introduction to code profiling and benchmarking
  • Writing better, tidier R: keep calm and code functionally with purrr
  • More in development

This is two day  workshop series designed to provide an introduction to practical machine learning with R.

Day 1: Regression

Day 1 focuses on regression. We will provide an introduction to some basic principles of machine learning experimentation, describing how one selects a model to use, the concepts of cross-validation. We will demonstrate how these apply to several classical machine learning approaches in R, including supervised (classification and regression, such as K-nearest neighbour and linear regression) and unsupervised (clustering, such as hierarchical and k-means clustering, and dimensionality reduction, such as principal component analysis) methods. We recommend attending both the regression and classification workshops.

Day 2: Classification and unsupervised learning

Day 2 focuses on classification and unsupervised learning approaches. We will build on the first day’s activities to discuss how cross-validation applies in the context of classification problems. We will then demonstrate how these apply to several classical machine learning approaches in R, including supervised (classification and regression, such as K-nearest neighbour and linear regression) and unsupervised (clustering, such as hierarchical and k-means clustering, and dimensionality reduction, such as principal component analysis) methods. We recommend attending both the regression and classification workshops.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey 
Prerequisites Attendees are expected to have some R background (at least at the level of the “Introductory R” Intersect courses, including the tidyverse suite of packages and the use of R as a data processing tool). It is assumed that attendees have not had previous training in ML, for example as part of an undergraduate semester-long course.
Resources R, Rstudio and installation of several key packages will be required.

This is a two day workshop series designed to provide an introduction to practical machine learning with python.

Day 1: Regression

Day 1 focuses on regression. We will provide an introduction to some basic principles of machine learning experimentation, describing how one selects a model to use, the concepts of cross-validation. We will demonstrate how these apply to several classical machine learning approaches in python, including supervised (classification and regression, such as K-nearest neighbour and linear regression) and unsupervised (clustering, such as hierarchical and k-means clustering, and dimensionality reduction, such as principal component analysis) methods. We recommend attending both the regression and classification workshops, as the latter builds on the former. 

Day 2: Classification and Unsupervised Learning

Day 2 focuses on classification and unsupervised learning approaches. We will build on the first day’s activities to discuss how cross-validation applies in the context of classification problems. We will then demonstrate how these apply to several classical machine learning approaches in python, including supervised (classification and regression, such as K-nearest neighbour and linear regression) and unsupervised (clustering, such as hierarchical and k-means clustering, and dimensionality reduction, such as principal component analysis) methods. We recommend attending both the regression and classification workshops, as the latter builds on the former.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey 
Prequisites Attendees are expected to have some python background (at least at the level of the “Introductory python” Intersect courses). It is assumed that attendees have not had previous training in ML, for example as part of an undergraduate semester-long course.
Resources Anaconda python, jupyter notebooks and the scikit-learn library will be used in this course. 

This 2 day workshop follows the Data Carpentry R Geospatial curriculum, with additional details relating to working with geospatial data in Australia. It is designed to introduce learners comfortable with R to working with geospatial data, including raster and vector files. At the end of the workshops, learners will be able to load, manipulate and visualise these file types to make maps, and perform basic spatial calculations.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey 
Prerequisites Attendees are expected to have some R background (at least at the level of the “Introductory R” Intersect courses, including the tidyverse suite of packages and the use of R as a data processing tool).
Resources The lessons closely follow the Data Carpentry curriculum (opens new tab), and also include some Australian-specific information in “Introduction to Geospatial Concepts”.

This two day workshop will provide an introduction to natural language processing in python. It will provide an overview of NLP, followed by practical, hands-on coding sessions where learners will develop scripts to showcase the most prominent differences in word / n-gram frequency distributions between two corpora, carry out and visualise the output of topic modelling, perform text classification, sentiment analysis, and named entity recognition.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey 
Prerequisites Attendees are expected to have some python background (at least at the level of the “Introductory python” Intersect courses). It is assumed that attendees have not had previous training in ML, for example as part of an undergraduate semester-long course.
Resources Anaconda python, jupyter notebooks and the scikit-learn library will be used in this course. 

This half day workshop will provide an introduction to the RMarkdown family of tools, including RMarkdown, blogdown and bookdown. Learners will get hands-on experience showcasing their work using these tools, preparing reports for sharing online and off, writing a book using reproducible tools and presenting their work online through blogging.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey 
Prerequisites Attendees are expected to have some R background (at least at the level of the “Introductory R” Intersect courses, including the tidyverse suite of packages and the use of R as a data processing tool). Learners will need to set up an account on github.com prior to the training session.
Resources R, Rstudio and installation of several key packages will be required.

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.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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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.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

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.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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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. Using NVivo memos and visualisations combined with the ability to integrate with popular bibliographic tools you can get your research ready for publication sooner.

This course is facilitated by Intersect Australia. 

Learn more     

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 course is facilitated by Intersect Australia. 

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Bioinformatics

This workshop is comprised of three courses and aims to teach essential concepts for analysing next generation sequencing data on the University’s Artemis High Performance Computer (HPC).

Concepts learnt can be applied to analysing either whole genome, exome or transcriptomic data. Attendees will also learn how to automate their jobs using job arrays and how to visualise data on Artemis, omitting the need for a local desktop.

Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites Competency on the Unix/Linux command line and basic familiarity with Artemis. You must first take an ‘Introduction to Unix/Linux’ and ‘Introduction to Artemis’ course; these are regularly scheduled on campus. 
Resources

You must bring your laptop. If it has a Windows operating system, please ensure you have a terminal client installed.

This course will introduce you to:

  • Artemis HPC
  • single cell RNA sequencing with the 10X chromium system
  • 10X Genomics’ bioinformatics pipelines Cell Ranger
  • 10X Genomics’ Loupe Cell browser

By the end of the course you will be familiar with:

  • Artemis HPC
  • understand how single cell RNA sequencing works using the 10X system
  • know how to run an end-end QC and analysis pipeline using cellranger
  • know how to visualise results using Loupe Cell Browser
Open to Staff, research students and affiliates with a valid University of Sydney UniKey
Prerequisites Intro to RNA sequence analysis on Galaxy Australia
Resources You must bring your laptop. If it has a Windows operating system, please ensure you have a terminal client installed.

This course provides an introduction on how to carry out RNA-seq data analysis using the Artemis HPC and R. We will cover the processes of:

  • obtaining sequencing data (in fastq or other format)
  • generating a count table
  • generating a list of differentially expressed genes
  • pathway analysis basics
Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites

You must have:

  • completed Intro to RNA sequence analysis on Galaxy Australia
  • your own laptop, with R, Rstudio, Bioconductor and several other key libraries installed.
  • University of Sydney Unikey (to access the Artemis HPC).
  • a text editor: such as Sublime Text, Notepad ++ (Windows only), Visual studio code, Atom etc.
  • a terminal application, such as the built in terminal on a mac or linux machine, and gitbash for Windows.
Resources

You must bring your laptop. If it has a Windows operating system, please ensure you have a terminal client installed.

CLC Genomics Workbench is a comprehensive suite of bioinformatics tools packaged into a user-friendly graphical environment. You can perform a range of analyses on next generation sequencing data and create customisable workflows for studies in genomics, transcriptomics, epigenomics and metagenomics. CLC Genomics Workbench is linked to Artemis HPC, providing users with higher computational power and throughput, and better data security than ever before.

In this course, we will teach you how to submit bioinformatics analyses to be processed on Artemis HPC from the CLC Genomics application on your personal computer.

The course will cover:

  • data management, 
  • importing/exporting data 
  • launching jobs on Artemis
Open to Staff, research students, and affiliates with a valid University of Sydney UniKey
Prerequisites None
Resources

Own laptop is required, with CLC Genomics Workbench 10.1.1 installed. Download it for MacWindows, or Linux.

Purchase license subscription to CLC Genomics Workbench

This course is designed to introduce you to basic concepts in whole genome sequencing (WGS) analysis using Galaxy Australia https://usegalaxy.org.au/ a user-friendly web-based bioinformatics research platform.

In the DNA sequence analysis course, we will investigate a superbug outbreak in a hospital and learn how to:

  • assess read quality
  • de novo assemble a draft genome
  • annotate a draft genome
  • identify the pangenome
  • align reads to a reference genome
  • call variants
  • draw a phylogenetic tree
Open to All (University of Sydney staff, research students and affiliates given priority)
Prerequisites None
Resources

Own laptop is required.

This course is designed to introduce you to basic concepts RNA sequencing analysis pipelines on Galaxy Australia, a user-friendly web-based bioinformatics research platform.

In the RNA sequencing course, we will use RNA sequencing data to align, visualise and perform differential expression analysis.

Open to All (University of Sydney staff, research students and affiliates given priority)
Prerequisites None
Resources

Own laptop is required.

Galaxy Australia is a free web-based bioinformatics analysis and workflow platform. It contains thousands of bioinformatics tools to combine, analyse and interpret genomic (DNA), transcriptomic (RNA), proteomic (proteins) and metabolomic (small molecules) data. It provides a simple point-and-click graphical user interface. and aims to make applying bioinformatics approaches on powerful national computing infrastructure easier.

There are a number of bioinformatics courses using Galaxy Australia that SIH facilitate that cover: quality control, genome assembly, genome annotation, variant calling, antibiotic-resistant genes, strain subtyping, species identification, RNA-seq, metagenomics.

Galaxy Australia is supported by National Research Infrastructure for Australia, Bioplatforms Australia, the Australian Research Data Commons, UQ RCC, QCIF, Melbourne Bioinformatics.

Open to All (University of Sydney staff, research students and affiliates given priority)
Prerequisites None
Resources

Own laptop is required.

There are a number of bioinformatics courses using Galaxy Australia that SIH facilitate that cover:

  • quality control, 
  • genome assembly, 
  • genome annotation, 
  • variant calling, 
  • antibiotic-resistant genes, 
  • strain subtyping, 
  • species identification, 
  • RNA-seq, 
  • metagenomics.

Galaxy Australia is supported by National Collaborative Research Infrastructure for Australia, Bioplatforms Australia, the Australian Research Data Commons, UQ RCC, QCIF, Melbourne Bioinformatics.

Following the successful annual Sydney Summer School in Pathogen Genomics and Global Health programs in 2017 - 2020, we are pleased to open EOIs for 2021, to microbiologists, clinicians, epidemiologists and public health professionals that are interested in translational research in the field of public health pathogen genomics and communicable disease control. The program includes a mix of inspiring keynotes, master classes and practical demonstrations delivered by expert practitioners in a webinar series. We will teach the basics of genomics of bacteria, viruses, and fungi with epidemic potential and critically examine the approaches to the analysis of genomes in global health context. The webinar series will illustrate the power of genomics, functional genomics and metagenomics in answering important questions on the assessment of evolution, virulence, transmissibility and drug resistance as well as on detection of local and international outbreaks and deciphering of transmission pathways. The special focus this year will be on applications of genomics and tracking the evolution and community spread of SARS-CoV-2.

The number of participants is limited. Organisers will select participants based on their provided information about motivation, prior knowledge and interests.

Topics to be covered

  • What can the analysis of microbial genomes tell translational researchers clinicians? How to select sequencing and bioinformatics solutions for specific research questions? Genome-wide association studies and patient outcomes
  • Integration of genomic, clinical and epidemiological data: global and local perspectives and solutions
  • Integrated data models, data analytics for knowledge discovery and data visualisation (we will employ phylogenetics and phylodynamics as case studies)
  • Generation and analysis of SARS-CoV-2 genomic data in the context of current pandemic
  • Implementation of next generation sequencing technologies in diagnostic and public health laboratories
  • Effective and ethical data sharing and translation of genomics into precision medicine and public health
  • Modelling and evaluation of genomics-guided interventions in hospital and community settings; genomics knowledge network

Course Information:

Dates: 26th Feb, 5th, 12th, 19th March 2021

Time: 1pm – 5pm   Australian Eastern Standard Time (AEST)

EOI Submission Link (close date 10th Jan 2021):

https://redcap.sydney.edu.au/surveys/?s=TXCPLW99XR

Program: Detailed program for the 4 day webinar series to be released soon. Please refer to the CIDM-PH website for 2020 program.

Registration Fees (to be confirmed upon EOI):

$400 International and Australian participants

$250 University of Sydney students and academics

Enquiries:  WSLHD-CIDM-PH@health.nsw.gov.au

Research data management

Research Electronic Data Capture (REDCap) is a secure web-based database application maintained by the University. It is ideal for collecting and managing participant data and administering online surveys, with features supporting longitudinal data collection, complex team workflows and exports to a range of statistical analysis programs.

We will cover:

  • how to build a simple data entry project
  • how to choose the appropriate fields for your data collection
  • how to invite collaborators to a project

Open to

University of Sydney staff, students and affiliates

This training session is designed to address the needs of the University’s research community. It includes information specific to the University’s research data systems and platforms.

Resources Bring your own laptop
Related courses This training covers basic functions of REDCap. Many additional features are covered in Surveys in REDCap or Longitudinal Trials with REDCap training

In this training session, we will cover:

  • how to set up a survey
  • how to flow participants through surveys
  • how to distribute surveys

Open to

University of Sydney staff, students and affiliates

This training session is designed to address the needs of the University’s research community. It includes information specific to the University’s research data systems and platforms.

Prerequisites Experience in building REDCap projects using the basic functions
Resources You must bring your own laptop.

This course will be run by request only; charges apply. Contact digital.research@sydney.edu.au for more information

Learn how to run longitudinal data collection, such as surveys with post-intervention and follow-up questionnaires, using REDCap's powerful features.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

Learn how to monitor, track and extract information from the internet and generate structured datasets using Python.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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This course will introduce the technical components of Qualtrics from building a survey to analysing the results.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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A workshop where we cover the fundamentals of version control using the researcher-focused training modules from the highly regarded Software Carpentry Foundation.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more

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.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

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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.

This course is facilitated by Intersect Australia. As a member institution, University of Sydney staff and students can also attend any training hosted by Intersect, at any location, free of charge.

Learn more