Professional Development Courses in Health Data Science

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.

Transferring into UNSW Health Data Science postgraduate programs

Each of the Health Data Science professional development courses is equivalent to 6 Units of Credit (UoC) of a UNSW postgraduate course in Health Data Science and they can be used as recognition of prior learning towards a UNSW postgraduate qualification in Health Data Science. At the time of admission to UNSW, students can apply to have Health Data Science professional development courses that they have completed (must have scored at least 50%) recognised as advanced standing or credit transferred to the UNSW degree program. Up to 50% of the total UoC of the program can be transferred and students must then complete at least 50% of the remaining UoC as a UNSW enrolled student to be awarded a UNSW qualification. For example, for the Graduate Certificate (24 UoC, 4 courses) you can use up to two Graduate Certificate level courses from the professional development courses (12 UoC) and then complete 2 courses (12 UoC) as a UNSW enrolled student.

Teaching Approach

Each course is organised into 10 chapters and is scheduled to align with the UNSW term calendar.

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. The courses use a variety of assessment modalities including multiple choice question gamification, data management plans, algorithm challenges and reflective blogs.

The notional study time commitment is 10 hours per week.

Assumed Knowledge

Statistical Modelling 1 & 2, Machine Learning & Data Mining are Graduate Diploma level courses that build upon the Graduate Certificate level courses of Statistical Foundations for Health Data Science and Computing for Health Data Science. Future students are encouraged to contact cbdrh@unsw.edu.au to confirm sufficiency of knowledge base before enrolling in these courses, which require programming skills in R and/or Python. Visualisation and Communication of Health Data requires R programming skills. Future students can contact cbdrh@unsw.edu.au to learn how proficiency can be gained through pre-course R learning modules.

Course Information

Course

Session Dates

Context of Health Data Science

 

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.

 

 

Registration closed

Session 1:

18 February to 5 May 2019

 

Session 2:

3 June to 20 October 2019

 

Registration Opening Soon

Term 1:

17 February to 16 May 2020

 

 

 

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.

 

Registration closed

Session 1:

18 February to 5 May 2019

Session 2:

15 July to 1 December 2019

 

 

Registration Opening Soon

Term 1:

17 February to 16 May 2020

Term 2:

1 June to 29 August 2020

 

 

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, including data from linkage projects.

 

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.

 

Registration closed

Session 1:

14 January to 2 June 2019

Session 2:

3 June to 18 August 2019

 

 

Registration Opening Soon

Term 2:

1 June to 29 August 2020

Term 3:

14 September to 12 December 2020

 

 

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.

 

Registration closed

Session 2:

16 September to 1 December 2019

 

 

 

 

 

Registration Opening Soon

Term 1:

17 February to 16 May 2020

 

 

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.

 

Registration closed

Session 1:

14 January to 2 June 2019

Session 2:

3 June to 18 August 2019

 

 

 

 

 

Registration Opening Soon

Term 2:

1 June to 29 August 2020

 

 

 

 

 

 

 

 

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.

 

Registration closed

Session 1:

14 January to 2 June 2019

Session 2:

3 June to 18 August 2019

 

 

 

 

 

Registration Opening Soon

Term 2:

1 June to 29 August 2020

 

 

 

 

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.

 

Registration Closed

Session 2:

16 September to 1 December 2019

 

 

Registration Closed

Session 1:

29 April to 15 September 2019

 

 

Registration Opening Soon

Term 2:

1 June to 29 August 2020

Term 3:

14 September tp 12 December 2020

 

 

Statistical Modelling II

 

Sophisticated modelling techniques are essential for the analysis of real-world health data. Building on Statistical Modelling I, this course expands the statistical toolkit and broadens students' understanding of relevant statistical approaches for the analysis of realistically complex data structures and research questions. The course is aimed at those currently working or planning on working in health or a health-related field, and who are interested in applying advanced statistical methods to analyse complex data.

 

Topics covered in this course include multilevel models for hierarchical data; analysis of time series and longitudinal data; quasi-experimental approaches for drawing causal inferences from observational data; multiple imputation for missing values; and simulation approaches for study planning and model evaluation.

 

Content is delivered through a combination of online readings, expert lectures and interactive tutorials. Statistical concepts are illustrated with a variety of health examples, and students will learn how to implement methods using leading statistical software.

 

Registration Opening Soon

 

Term 3:

14 September tp 12 December 2020

 

There are no entry requirements to enrol into Health Data Science professional development courses, enrolment is open to Australian and international professionals and students.

Check you meet the assumed knowledge requirements for courses before enrolling. If in doubt, email cbdrh@unsw.edu.au to confirm.

Enrol into courses using the link in the Course listing table. Registration for 2020 sessions will open soon.

Each course costs AU $3400 (including GST) in 2020. The cost includes access to all course content online, online facilitation by UNSW faculty and course tutors, assessment marking and feedback, and dependent on successful completion, course certificates. No refunds can be issued for course withdrawals. However, requests for transfers to an alternative Health Data Science professional development course can be considered if submitted within the period of chapters 1 and 2 of the enrolled course. Send transfer requests to cbdrh@unsw.edu.au.

Contact for inquiries

Email: cbdrh@unsw.edu.au

Phone: +61 2 9385 9064

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