Online Short Courses

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

  • Introductory Statistics for Researchers

  • Study Design

  • Introduction to Regression Modelling in R

  • Introduction to Python for Data Science

  • Sample Size and Power Calculations

 

**Due to the Coronavirus (COVID-19) situation, we are now offering online course with heavily discounted rates (half of normal fees).**   

Note: The course should be attended remotely, and will be delivered using online collaborative teaching tools, as a mix of live video-assisted lectures and computer-based tutorials, with a chat session and multiple support staff to answer questions.

 

Introduction to R

4, 6 and 7 May, 2020

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 access to the course.

Duration: 10.00am to 12.30pm - each day

Location: Online

 

Online Course Fees (heavily discounted rates with half of normal fees)

UNSW Student: $62.50

UNSW Staff: $125

External Student: $225

External: $300

You will receive a certificate of completion for the course.

Introductory Statistics for Researchers

11 - 14 May, 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?"

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.

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.

Instructions on how to carry out particular statistical analyses will be provided, with an emphasis on interpreting the computer output (most packages produce similar output). There will be plenty of practical work throughout the course.

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

It would be very helpful if you have some basic knowledge of R. We recommend our one-day course, Introduction to R, which runs just prior to this course. However, previous experience with statistical packages like SAS, SPSS or Stata or some basic programming skills are also helpful.

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

Course Outline

This course will cover topics including:

Introduction

Types of experiments, scales of measurement, which method to use.

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-tests, Wilcoxon).

Comparing many groups (with one treatment factor) - one-way ANOVA or Kruskal-Wallis - 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 a computer with access to the course.

Duration: 9.00am to 1.00pm - each day

Location: Online

 

Online Course Fees (heavily discounted rates with half of normal fees)

UNSW Student: $100

UNSW Staff: $200

External Student: $375

External: $500

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

 

Online Course Fees (heavily discounted rates with half of normal fees)

UNSW Student: $40

UNSW Staff: $80

External Student: $150

External: $200

You will receive a certificate of completion for the course.

Introduction to Python for Data Science

26 and 27 May, 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 one-day 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. The course is self-paced and focussed on developing practical skills.

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 Requirement: You will need a computer with access to the course.

Duration: 10.00am to 1.00pm - each day

Location: Online

 

Online Course Fees (heavily discounted rates with half of normal fees)

UNSW Student: $62.50

UNSW Staff: $125

External Student: $225

External: $300

You will receive a certificate of completion for the course.

Hierarchical Modelling of Species Communities with the R-package Hmsc

18 - 19 June, 2020

Course Overview

Hierarchical Modelling of Species Communities (HMSC) is a joint species distribution modelling approach that enables one to integrate data on species abundances, environmental covariates, species traits, phylogenetic relationships, and the spatio-temporal context in which the data have been acquired (Ovaskainen et al. 2017Ovaskainen and Abrego 2020). This course is aimed for students and researchers who are interested in analysing data on community ecology in a way that allows placing their results in the context of modern theory. The course covers a comprehensive treatment of HMSC, including both the technical detail of the statistical methods, as well as the ecological interpretation of the results. With the help of worked out examples, the participants learn how to conduct and interpret statistical analyses in practice with the R-package Hmsc (Tikhonov et al. 2020), providing a fast starting point for applying HMSC to their own data. The participants are also encouraged to bring also their own data so that they can get hands-on support on HMSC-analyses of their own projects.

Course Outline

This course will cover topics including:

  • Introduction and motivation

  • How HMSC relates to ecological theory?

  • The syntax and typical workflow of the R-package Hmsc

  • Types of data that can be incorporated to Hmsc

  • Types of questions that can be addressed by Hmsc

  • Worked out case studies on plants, fungi and birds

  • Break-up groups to analyse your own data

 

Presenter and Expertise: Otso Ovaskainen (Professor, University of Helsinki) and Jari Oksanen (Emeritus Professor, University of Helsinki)

Course Requirement: You will need to bring your own laptop to the course, with preferably both R and the R-package Hmsc (found from CRAN) installed

Duration: 9.00am to 5.00pm for both days

Location: Morven Brown Building (K-C20-G3) Room G3 | Gate 8, High Street | UNSW Sydney | UNSW Kensington Campus

Course fee: Free

Refreshment: Morning and afternoon teas

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
 

Introduction to Regression Modelling in R

26 - 28 August, 2019

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 three-day short course 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.  If you need to revise introductory statistics material, you should attend the Introductory Statistics for Researchers course (see below) earlier in August 2019, and then take this regression course later, as the material in the Introductory Statistics for Researchers course is taken as assumed knowledge for this regression course.

Course outline

An outline of topics included in this regression course is below (click the "+" on the right).

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
 
Wiggly models
Spline smoothers, including diagnostics and interactions
 
Generalised linear models (GLMs)
Examples of GLMs
Fitting GLMs and checking assumptions, including mean-variance relationship
Inference
Extensions - offsets, zero-inflated models

Requirements: You will need to bring your own laptop! We will sort out internet access for you.
Duration: 3 days, 9.00 am to 5.00 pm daily
Location: UNSW Business School (E12), Level 1, Room 119, UNSW Kensington Campus