Centre for Health and Social Analytics (CHSA) is led by Professor Alberto Nettel-Aguirre, it contributes to the University’s Health and Wellbeing Strategy and the Data and Decision Science Framework.

Director: Professor Alberto Nettel-Aguirre

ORCID

Phone: +61 2 42215852
Emailalberton@uow.edu.au
Location: Building 39C Room 267

Expertise & research focus

The CHSA is being established as a centre within the National Institute for Applied Statistics Research Australia (NIASRA) with the aim of significantly increasing the University's capacity, capabilities, focus and performance in health, medical and social research. CHSA will have a focus on the acquisition, management, analysis, dissemination and interpretation of large and complex data sets. CHSA’s director has expertise in and focus on the use of Biostatistics and Data Science in health research.

CHSA’s portfolio will cover highly collaborative enterprises and projects in health research aligning with UOW’s  health and wellbeing strategy; including development of modern biostatistical/data science methods and capacity building short courses.

Aim

The aim of CHSA is to ensure that correct use of Biostatistical and data science methods in health research, while making substantial contributions to all steps of health research  methods, including data collection, measurement and analysis with a focus on translation.

Major research projects

  • Community Health And Rural/Regional Medicine Project (CHARM) (UOW)
  • CHild Active-transportation Safety and the Environment (CHASE)
  • CHILDNEPH- A national (Canadian) initiative to improve care and outcomes for patients with nephrotic syndrome.
  • SMIHS Research Advisory Group (UOW)
  • Trends In Oxygen Saturation In Healthy Term Infants In The First Few Days Of Life: The "TOST" Study

Short courses

This is a unique entry level short course specifically designed to teach the basics of data science and machine learning with applications in the health and social sciences.

Course outline

Data science and machine learning represent an overlap of statistics, computer science and domain expertise and are increasingly becoming integral in research applications based on health and social data. The combination of statistics and computer science, allows machine learning methodology to take advantage of the strengths of both areas yielding methods that have some advantages over statistical analysis and computational algorithms on their own. The course will cover the most in demand data science and machine learning methods for both supervised (regression, classification and regression trees, neural nets and support vector machines) and unsupervised learning (clustering). Participants will learn how to choose the appropriate method, and how to analyse and interpret the results using a free interface to the popular data science and statistical package R. 

Contact: karink@uow.edu.au to register or click the following button to book 

book and make a payment

 

Instructor

Professor Marijka Batterham, Professor Alberto Nettel-Aguirre, and Brad Wakefield

Dates

Tuesday and Wednesday, 19 & 20 July, 2022 

Time

10.00 am - 4.00 pm

Location

Room 40.122, UOW campus

Introductory cost

$220

Contact: marijka@uow.edu.au for additional course information