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Study postgraduate online through Open Universities Australia. Explore all degrees, subjects and short courses now.

The Graduate Certificate of Applied Statistics is designed for graduates in any discipline, especially for humanities, social sciences, health sciences, information systems and business graduates who have a professional interest in the use of statistics. It is also applicable to other graduates who have a need to use statistics in their work but have not had sufficient experience with applied statistics. It concentrates on practical skills and enables participants to broaden their theoretical and practical knowledge in the basic areas of statistics.

Learn quantitative skills in exploratory data analysis, estimation and hypothesis testing, factor and regression analysis, discriminant analysis and forecasting. Gain practical experience using statistical software packages such as SPSS and SAS

Graduate Certificate of Applied Statistics

The Graduate Certificate of Applied Statistics provides you with knowledge and skills to work as a statistician, analyst or data scientist in a range of industries. This degree is an entry pathway to the Master of Applied Statistics.

Learn how to collect quality data and interpret it. You’ll study statistical concepts and data analysis techniques. Become the critical thinker and problem solver that business, government and research bodies need. Work towards becoming a statistician.

Data and Analysis

Introduction to Psychological Design and Statistics

Statistics

Research Design

Statistics for Social Research

UO R for Data Analytics

Statistical Concepts for Science

Basic Statistical Computing Using R

Statistical Practice 1

Quantitative Techniques for Business

This subject teaches students to identify some of the methodologies used in survey research. It includes an overview of the procedures used in survey research, a descriptive approach to methods including questionnaire design, data collection, and an introduction to survey sampling. It covers ethics and laws in conducting research. It also covers topics related to incidence and prevalence, standardisation, different types of study designs including cohort and case-control studies, randomised control trials and clinical trials in health statistics and elsewhere. Case studies will be drawn from areas such as sociology, market research, psychology, medical sciences and other areas as appropriate.

Survey research methods and get critical about design. Describe approaches and observe techniques. Sample data collection methods. Scale and graph procedures and figure out incidence and prevalence. Draw on case studies and assess and present data

Understanding, using and interpreting statistics is crucial to health science research and practice, particularly in monitoring health outcomes and decision-making processes about interventions. This unit will develop students&#39; knowledge of fundamental statistical concepts, such as descriptive and inferential statistics, common statistical tests and statistical methods frequently used in health science research. This will include hypothesis testing, estimation, associations, modelling relationships and prediction using different methods such as regression analyses. Throughout the unit, students will consolidate their understanding of statistical theory through its application to practice. While there are some formulae and computational elements to the unit, the emphasis is on interpretation and concepts. Besides the theoretical material, this unit will also enable students to run basic analyses using common statistical software. Using this software, students will analyse simulated health science data sets and then interpret the results obtained. This unit aims to extend students&#39; statistical understanding and analytical expertise, which can then be applied to practice through critical appraisal statistical methods used in health science research.

Explore fundamental statistical concepts and use common statistics software. You’ll learn how statistics are used in health science research and practice. Gain skills to interpret health-related data. Make confident decisions based on evidence.

Biostatistics for Health Sciences

This subject extends the ideas developed in Statistical Practice 1 to include more advanced analyses, broaden the range of applications students are familiar with so that they will be able to carry out independent statistical investigations, and develop an awareness of the assumptions and limitations involved in the generalisation of results of such investigations.

Practice and advance your statistical knowledge and extend ideas. Produce an independent investigation. Get analytical and compare groups using parametric and non-parametric methods. Stop generalising, question assumptions and identify data limits. 

Statistical Practice 2

This subject introduces R, one of the popular open source statistical programming languages commonly used in applied statistics and data science disciplines. Students will learn key programming principles of R and develop competence in programming in R which are essential for a statistician or data scientist to perform different types of statistical analyses.

Upskill and package up your computer and statistical knowledge. Form questions. Use SPSS software to select, modify, merge and apply data visualisation techniques. Investigate missing values and manage complex data files. 

This subject develops students’ understanding of a range of statistical methods along with their assumptions and limitations of their application. It enables students to develop the capacity to carry out independent statistical analysis of data using a standard statistical software package and also aims to develop students’ abilities in effectively communicating the outcomes of statistical investigations. In addition, it will provide a foundation and motivation for exposure to statistical ideas subsequent to the Graduate Certificate (GC) degree.

Develop a number of statistical data techniques; assess theories and survey limitations. Increase analysis skills and translate investigation outcomes. Recognise statistical inference and sampling distributions. Transcribe interpretative summaries. 

The ability to make sound decisions in High Performance Sport is critical to maximizing performance outcomes. In order to do this, practitioners need specific knowledge and skills in data analysis techniques, in addition to the ability to present data in a meaningful way to a variety of audiences. This subject is based on contemporary data analysis techniques focusing on determining practically meaningful differences in athletic performance. A range of approaches will be explored to allow for analysis of both individual and group data. The aim of this subject is to provide students with the knowledge, understanding and skills to analyse and interpret data of relevance to sports science and athletic performance and effectively present the results.

Data Analysis and Interpretation for High Performance

Swinburne University of Technology

Macquarie University

Postgraduate Statistics Courses & Degrees | Open Universities Australia

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