Professional Development in Health Data Science

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In today’s world where there is growing demand for data-driven solutions, our Health Data Science professional development courses provide a timely option for employers seeking to quickly upskill staff and for individuals wanting to gain new skills to meet immediate work demands or support future career aspirations. The Health Data Science professional development courses stem from our Master of Science in Health Data Science program at UNSW Sydney. Five of the courses are drawn directly from the Master of Science program, and two courses have been customised with Australian-focussed topics.

Our courses aim to build and equip graduates with essential competencies for which there is high demand in the health data scientist workforce. Teaching examples are all health-specific using relevant real-world Australian health system data where possible. Curriculum has been developed in partnership with cross-disciplinary UNSW experts from the Centre for Big Data Research in Health, School of Computer Science and Engineering, School of Mathematics and Statistics, Ingham Institute for Applied Medical Research and key staff of the Australian Government Department of Health.

Each course is organised into 10 chapters with each chapter designed to be completed in 1 week (fast-format integrated with UNSW academic calendar) or up to 2-weeks (slow-format). Content is delivered fully online using a combination of instructional videos, readings and interactive exercises that aims to build analytics skills, stimulate critical thinking and engage peer to peer learning.

For more information about the courses and prerequisites, please refer to the brochure given below.


Session Dates

Data in the Australian Health System

This course provides an overview of how data are generated and used in the Australian health system. It gives an introduction to measuring health outcomes and disparities in health. It describes major sources of Australian health data, including those relating to primary care, hospital stays and prescription medicines, and how these can be used by the health data scientist to create evidence for policy and research.

Activities are structured to foster a scientific, questioning attitude in the student. Students are encouraged to think critically about how health data are recorded, what this reveals about the underlying health delivery systems, and be creative in their use of health data sources to create or critically appraise evidence.

Enrol at:

2 July to 18 November 2018

(20 weeks)

Registration closes: 24 June 2018

Statistical Foundations for Health Data Science

Almost all aspects of health data science, from the most basic descriptive analyses through to the development of the most sophisticated deep learning models, are built on a set of foundation statistical concepts and principles, encompassing both frequentist and Bayesian paradigms.

The course will provide the student with a thorough understanding of

the Law of Large Numbers and the Central Limit Theorem, probability distributions, likelihood and likelihood estimation, Bayes theorem and Bayesian estimation, Monte Carlo methods and resampling methods such as the bootstrap, frequentist inference, and essential epidemiological and study design concepts. The approach is highly computational. Rather than relying on mathematical proofs and theorems, students investigate and verify these concepts through simulations which they construct themselves, while simultaneously gaining proficiency in the widely-used, open-source R statistical programming language. The end result is a sound knowledge of statistical computing and good programming practice, allied to a hands-on understanding of the statistical underpinnings of both regression modelling and machine learning.

Enrol at:

23 July to 9 December 2018

(20 weeks)

Registration closes: 22 July 2018

Management and Curation of Australian Health Data

This course is designed to equip students with the skills required to collect or obtain data, design data management strategies aligned with best practice, and appreciate the day to day practicalities of data curation for sound data management. Students will develop data wrangling skills required to assemble data suitable for analysis and research purposes. Data wrangling skills will focus on the key areas of data security, data exploration, documentation of data (for example data dictionaries) and data management, with the ultimate aim of creating analysis-ready datasets and ensuring reproducible results.

Enrol at:

20 August to 2 December 2018 (15 weeks)

Registration closes: 5 August 2018

Computing for Health Data Science

Computing now pervades nearly every aspect of modern life, including health care delivery and health services management. The objective of this course is to develop 'computational thinking' in health data science students, by providing them with a thorough and principled introduction to computer programming, algorithms, data structures and software engineering best practices. The ability to write clear, efficient and correct computer code is at the core of most data science practice, and is a foundation skill set.

In this course, students will learn to program in the Python language through tackling health-related problems. Topics include data types, functions, data processing, simulation, software development and program testing and debugging. Theoretical principles are reinforced with extensive ‘hands-on’ coding in Python, including the NumPy package.

Enrol at:

23 July to 28 October 2018 (14 weeks, integrated with UNSW academic calendar)

Registration closes: 22 July 2018

Visualisation and Communication of Health Data

Health Data Scientists present information to audiences across a range of backgrounds, spanning a spectrum from naïve or non-practitioners of a discipline to highly informed and expert audiences. Effective communication across different media types is essential. Appropriate data visualisation techniques can greatly increase the effectiveness of communication. An understanding of some basic simple techniques can ensure communication remains effective across diverse audiences. An understanding of the computation and presentation aspects of health data visualisation can increase not only the effectiveness of communication but also the efficiency of work effort.

This course takes a practical approach to creating appropriate, reproducible and transparent analyses and visualisations. Using R and RStudio, it develops useful data science analysis and visualisation techniques for different types of data visualisation and communication, including charts and graphs and written and oral communication forms. What makes a good map is discussed and the use of geospatial information is explored through the construction of an interactive Shiny application.

Enrol at:

23 July to 28 October 2018 

(14 weeks, integrated with UNSW academic calendar)

Registration closes: 15 July 2018

Statistical Modelling

This course provides a sound grounding in the theory and practice of fitting statistical regression models, with particular focus on the flexibility of generalised linear models (GLMs). Starting with linear regression, a major theme of the course is best practice in model fitting, including thorough exploratory data analysis, model assumption checking, data preparation and transformation, including the use of imputation, and careful attention to model adequacy and diagnostics. Emphasis is given to content-aware, purposive model building and the use of Directed Acyclic Graphs (DAGs) of causal relations to inform model parameter selection. Non-linear, logistic, binomial and Poisson models for count data are also covered. Effect modifications (interactions) and their meaning in a health context are explored. The presentation and visualisation of statistical models is considered, with emphasis on the explanatory insights that can be gained from well-constructed models. The final part of the course covers basic time-series models, survival analysis and other time-to-event models. The course is taught using the R programming language.

Enrol at:

30 July to 11 November 2018

(15 weeks)

Registration closes: 22 July 2018

Machine Learning and Data Mining

Machine learning and data mining bring together methods coming from statistics and computer science and apply these to databases both

large and small. They use a powerful and diverse set of techniques and algorithms to discover patterns and relationships in data with the final goal of creating knowledge from these data. These methods are increasingly being applied to the vast amounts of health data that are generated through sources including electronic medical records, medical and pharmaceutical claims, medical imaging, wearable and implantable devices and social media.


This course provides an introduction to data mining and machine learning, including both supervised and unsupervised techniques. You will learn about the underlying theory, as well as gain the practical know-how required to effectively apply these techniques to real-world health datasets to answer new health data science problems. The widely-used, open-source Python programming language is used to teach the course.

Enrol at:

30 July to 28 October (13 weeks, integrated with UNSW academic calendar)

Registration closes: 22 July 2018

Event date
Monday, 2 July 2018 - 9:00am to Sunday, 9 December 2018 - 5:00pm
Online Learning
$3,300 (including GST) per course
Booking deadline
Contact for bookings
Number of seats available
50 per course
Contact for inquiries
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