Online Short Courses



Over the course of the year Stats Central teaches short courses aimed at researchers across all disciplines. Offerings in 2020 include:

  • Introduction to R - Due to popular demand, we are offering 2 separate courses again

          Course 1: 11-12 August, 9.30am-1.00pm each day

          Course 2: 19-20 August, 9.30am-1.00pm each day

  •   Introductory Statistics for Researchers: 24-27 August, 9.00am-1.00pm each day

  • Introduction to Regression Modelling in R: 31 August - 4 September, 9.00am-12.30pm each day

  • Introduction to Python for Data Science: 1-2 September, 10.00am-1.00 pm each day

  • Text Analytics in Python (Advanced): New first time offer, 8-9 September, 10.00am-1.00 pm each day

  • Sample Size and Power Calculations

  • Study Design: May


**Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

The course will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.


Introduction to R

Course Overview

R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R. It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers.

This one-day introduction to R will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focused on developing practical skills.

Course Outline

This course will cover topics including:

  • Basics of interacting with R – calculations, saving variables so you can reuse them, data types and structures, organising R code in scripts
  • Tidyverse – a basic introduction to tidy R code
  • Data – reading in and organising data (from spreadsheets) with dplyr
  • Plotting – make beautiful figures with ggplot

Course Requirement: You will need a computer with course access (to install R before attending the course)


**Due to a high demand of the course, we are running two separated sessions with a limited capacity.**

Session 1: Tuesday 11 and Wednesday 12 August

Session 2: Wednesday 19 and Thursday 20 August

Duration: 9.30am-1.00pm each day

Location: Online

You will receive a certificate of completion for the course.


Introductory Statistics for Researchers

24 - 27 August, 2020

Course Overview

In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"

How does one decide which statistical procedure is the most appropriate? What do all the sections of the output mean?

This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed.

Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.

A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course. You will need basic R proficiency to carry out the practical work; we will teach only the additional R commands needed for these analyses.


Important Notes

This is not an introductory course in using the statistical software package, R. Basic coding will not be taught. To do this course successfully, you must have basic proficiency in using the R  package. All examples and exercises used in this course are done using R

1. If you do not have basic R skills, you need to do our course,Introduction to R, before this introductory statistics course. Introduction to R will be run twice: the first course is on 11-12 August and the second course is on 19-20 August. See the course details above to register for Introduction to R.

2. Anyone who is interested in registering for Introductory Statistics for Researchers and who has not registered for Introduction to R in August 2020  must do a short task in R  before registering for the introductory statistics course. This is to ensure you will not be disappointed if you register for the course and find you do not have the basic R skills necessary!

Do you need to have previous experience using the program R?

It is essential to have basic proficiency in using the R statistical package. Please read "Important Notes" above.

In this course, the statistical software package used is R exclusively. We do not use or demonstrate SPSS.


Course Outline

Summarising and Graphing Data

Ways of presenting data (histograms, boxplots), measures of centre and spread, analysing tables, correlation, and confidence intervals.

Comparing Groups

Hypothesis testing concepts - power, significance, P-value. Comparing two groups (t and Wilcoxon tests).

Comparing many groups - with one treatment factor (one-way ANOVA or Kruskal-Wallis test; multiple comparison tests).

Finding Relationships

Correlation, predicting relationships (regression - simple).

Other topics covered

Non-Normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.


Course Requirement: You will need to use a computer during the course. You will also need administrator rights to install software needed for the course onto your computer. Basic proficiency in using the R statistical package is essential.


Date: Monday 24 to Thursday 27 August

Duration: 9.00am to 1.00pm each day

Location: Online

You will receive a certificate of completion for the course.


Introduction to Regression Modelling in R

31 August - 4 September, 2020

Course Overview

The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).

This short course, taking place over five half-days, is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, linear regression, analysis of variance and know something about orthogonal and nested designs. If you have not used R before, we strongly recommend you learn basic features of R and how to use the RStudio interface to R before this course.

An outline of topics included in this regression course is below.

Course Outline

Linear regression — Simple linear regression, including assumptions, influential observations and inference. Equivalence of two-sample t-test and linear regression.

Linear models with multiple predictor variables — Multiple regression. Analysis of variance (ANOVA), including multiple comparisons

More linear models — Paired and blocked designs. ANCOVA. Factorial experiments. Interactions in regression.

Model selection — Cross-validation. Information criteria (AIC, BIC). Penalised estimation (LASSO).

Mixed effects models — Random effects. Linear mixed effects models. Inference for mixed models, including likelihood ratio tests, parametric bootstrap, hypothesis tests, confidence intervals. Correlated random effects.

Extensions — Spline smoothers, including diagnostics and interactions. Generalised linear models (GLMs).

Course Requirements: You will need to use a computer during the course. Some familiarity with introductory statistics and R will be assumed.


Date: Monday 31 August to Friday 4 September 2020 (five morning sessions)

Duration: 9.00am - 12.30pm each day

Location: Online

You will receive a certificate of completion for the course.


Introduction to Python for Data Science

1 and 2 September, 2020

Course Overview

Python is a widely used programming language to manipulate, analyze, and visualize data. It is one of the most popular languages for Data Science, especially when dealing with complex, uncurated or text datasets.

This course assumes that you have never used Python before, but you have some basic programming knowledge. You will learn how to obtain and install Python, which is open-source software, and Jupyter Notebook, which is an interactive computational environment, in which you can combine code execution, rich text, mathematics, plots and rich media.

This two half-days introduction to Python will cover some useful features of Python for data science. It will discuss various online resources available to further develop your data science skills using Python.

Course Outline

This course will cover topics including:

• Python overview

• Jupyter Notebook

• Basic Python programming

• Typical process of data science

• Techniques to manipulate and analyze datasets

• Result visualization

• Selected statistical analysis and/or machine learning examples

Presenter and Expertise: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

Course Requirements: You will need to use a computer during the course.


Date: Tuesday 1st to Wednesday 2nd September 2020 (two morning sessions)

Duration: 10.00am - 1.00pm each day

Location: Online

You will receive a certificate of completion for the course.


Text Analytics in Python (Advanced)

8 and 9 September, 2020

Course Overview

More than 70% of the data on the internet is unstructured. Among them, text is the most common form that appears in almost all data sources. For example, text data such as emails, online reviews, tweets, news and reports hold valuable information and insight for most research and applications. Text analytics, usually involving techniques from text mining or natural language processing (NLP), can automatically uncover patterns and extract meaning/context from these unstructured texts.

This course assumes that you have basic Python programming knowledge, or have previously attended "Introduction to Python for Data Science" from Stats Central. This course will provide you the foundation to process and analyze text.

In this course, we will cover some useful Python features and libraries for text processing and analysis. We will touch on some advanced topics such as sentiment analysis, text classification, and/or topic extraction.


Course Outline

This course will cover topics including:

• Jupyter Notebook

• Basic text operations in Python

• Text analytics and NLP

• Tokenization, stopwords, lexicon normalization, POS tagging

• Sentiment analysis and text classification

Presenter and Expertise: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

Course Requirements: You will need to use a computer during the course.


Date: Tuesday 8 to Wednesday 9 September 2020 (two morning sessions)

Duration: 10.00am - 1.00pm each day

Location: Online

You will receive a certificate of completion for the course.


Study Design

19 May, 2020

Course Overview

Good study design is crucial for answering your research questions. No amount of post processing or statistical expertise can compensate for poor or inadequate study design. In this course you will lean the basic concepts of study design including how to;

  • Randomize, so that your sample can be used make inferences about the real world (the population).

  • Implement appropriate controls and manipulation to infer causation.

  • Determine adequate sample size.

We will then move on to advanced concepts, which will focus on how blocking and stratification can reduce variability and improve power (the ability to answer your research question) using a smaller sample size (hence less resources).

Practical Component

  • How to randomise – Random allocation of subjects to treatments and random sampling

  • Simple power analysis – How much data do we need to answer the question of interest?

  • We will use free online tools and the software packages, either Excel and G*Power; or R (optional).


Course Requirement: You will need a computer with Excel (or equivalent) and G*Power installed and access to the course.

Duration: 9.00am to 1.00pm

Location: Online

Sample Size and Power Calculations

26 November, 2019

Course Overview

Power calculations and sample size determination are essential parts of planning a scientific study. In this one-day course we will introduce the basic principles of precision-based and power-based sample size calculations. Using practical examples, we will demonstrate how to perform sample size calculations for common designs in single-sample studies and for comparing groups, as well as discussing related issues such as multiple comparisons. A variety of useful software for power calculations will be highlighted.

Course Outline

This course will cover topics including:

  • Introduction and motivation

  • Basic principles of power and sample size calculations

  • Single-sample studies

  • Comparing groups: Common study designs

  • Complex power calculations

  • Other issues

Presenter and Expertise: Mark Donoghoe, Statistical Consultant


Requirement: You will need to bring your own laptop to the workshop.

Duration: 9.00 am to 5.00 pm

Location: UNSW Business School (E12), Level 1, Room 115, UNSW Kensington Campus