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

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.

Graduate Certificate of Applied Statistics

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

Data and Analysis

Introduction to Psychological Design and Statistics

Data Analysis for Business

Statistics

Research Design

HR Analytics

Quantitative Methods for Social Research

UO R for Data Analytics

Statistical Concepts for Science

Basic Statistical Computing Using R

This unit provides students with an introduction to research design and statistical analysis. In this unit, students will learn about common research methods used in psychology and related disciplines, critically analyse these methods, and be able to conduct their own analyses. Both experimental and non-experimental research methods are covered, as well as a variety of statistical tests, including t-tests, correlation, and chi square analyses.Students will learn data management techniques and appropriate methods to summarise data, including both numeric and graphical summaries. Students will also gain hands-on experience using the statistical software Stata.

Learn how to design research and use statistics in psychology. You’ll get to know common statistical techniques and software. Conduct tests, summarise data, and communicate your findings. Think about the role of research in psychological science.

This subject was previously known as Statistics for Social Research.This subject provides a solid introduction to applied statistics for students in criminal justice and related fields. The subject covers both descriptive and inferential statistics, and students learn to use Microsoft Excel for statistical analysis.Students in this subject will be taught to identify when certain statistical techniques are appropriate, and how to perform, interpret and present these analyses. This will enable students to be more discerning consumers of the research literature and also prepare them to carry out their own research projects. Students will be taught to analyse data using Microsoft Excel. These skills are highly sought after by employers.Students should not enrol in this subject if they have completed 1003PSY Research Methods and Statistics and 2009CCJ / CCJ29 Statistics for Social Research.

This subject introduces students to modern data analytics, visualisation, and statistics. It equips students with the skills required to take advantage of powerful computing for the analysis and visualisation of complex data. These skills are used to solve problems in areas such as the biological sciences, medical sciences, agricultural sciences, nutrition, health sciences, education, and business. Students will become familiar with data visualisation and computing, descriptive statistics, statistical modelling, and data-based decision making. Using statistical computing packages is an integral part of this subject. This subject allows further study in second-year subjects in statistics and data science.

Making Sense of Data

In this subject, you are introduced to quantitative statistics, qualitative coding and the role of health data analytics in healthcare organisations and public health research. Key emphases include understanding the importance of healthcare data as an asset, identifying and applying the key elements of the data capture and analysis process in key research methodologies, along with effective presentation, communication and dissemination of findings beyond peer review publications. You will also investigate the role of health data analytics in quality and performance indicators. This subject includes synchronous sessions held during business hours. This means that you will attend sessions each week simultaneously with your facilitator and classmates. This subject is recommended for first-year undergraduate students.

Introduction to Data Analytics in Healthcare

HR analytics is the process of collecting and analysing HR data to improve an organisation’s workforce. In this course, you’ll learn how HR data can be used to measure how HR initiatives are contributing to the organisation’s goals and strategies. Learn the basic concepts, processes and tools that are used to identify commonly encountered HR issues. Gain the skills to evaluate information using basic statistical analysis. You’ll also explore ethical issues and future trends associated with HR analytics. UniSA Online’s 10-week short courses give you the flexibility to upskill in a certain area, stay current with developments in your field, diversify your knowledge, or even explore a new direction in your career – without having to commit to the time and cost of a full university degree.  This is a third-year course from UniSA Online’s HR degree. When studying this course as a &quot;single subject&quot;, prior knowledge from INFS 2038, or equivalent will support your success.﻿Delivered 100% online, you’ll be able to study where and when it suits you. Access online academic and student support seven days a week, fit study around work and life commitments, view learning resources 24/7, and log in to the interactive online environment anywhere, any time and on any device. Should your course have an exam it will be scheduled for Australian Central Standard Time or Australian Central Daylight Time, depending on the time of the year.

Harness the power of data for effective decision making in human resources. This specialised study within human resources management covers data analysis and statistical tools. You’ll also explore the ethics of using employee data.

TAS-BEA140 Data Analysis for Business is a foundation unit in business statistics and the mathematics of finance, and is a core subject in the Bachelor of Economics and Bachelor of Business degrees. It is also a nominated elective in a wide range of other programs throughout the University of Tasmania.This subject is intended to provide a background capacity in mathematics of finance and statistical description and analysis, appropriate for a business student. The subject also includes an introduction to the basic use of tools such as Excel as an aid to analysis and presentation. The focus of the subject is on the practical use of data in business and economic decision environments, especially in an environment of risk and uncertainty.The subject is an introduction to the exciting world of applying mathematics and statistics to business problems. By studying a variety of examples based on real world problems, the unit highlights the application of quantitative methodologies to assist managers and decision makers in dimensioning and communicating risks and opportunities.

A business statistics course that covers the mathematics of finance. You’ll learn about the statistical process and get hands on with basic tools. Use data practically to solve real-world problems in economics and business.

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.This subject was formerly known as STA60003 Basic Statistical Computing

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 provides a broad introduction to statistical concepts and data analysis techniques. You will develop an understanding of statistical practice through a study of those techniques most commonly used in the sciences, social sciences and humanities. Topics covered in this subject include data collection methods, data quality, data summarisation, and statistical models such as the normal distribution, followed by sampling distributions and statistical inferences about means and proportions. Also studied are methods of analysis relating to comparisons, counted data and relationships, including regression and correlation. Statistical computer packages are used for handling and analysing data. However, no prior computing knowledge is assumed. This subject introduces vital skills for tertiary learning and explores their relationship to success in future careers.

Gain vital statistics skills for your science degree and beyond. You’ll learn how to collect, organise, and summarise data. Explore statistical models. Use statistical software. Get set for science with a rigorous framework for analysing data.

In this course, you’ll be introduced to a powerful statistical computing software called R. R is a software with built-in functionalities to assist you in statistically analysing and visualising big sets of data with ease. As an open-source software, developers also continuously build packages and libraries that can be loaded into R to simplify complex statistical operations.  This course revisits basic concepts of statistics such as measures of centrality and distributions. You’ll also be introduced to different methods of hypothesis testing, as well as other statistical concepts. By the end of this course, you’ll leave with the skills to use R to analyse and solve statistical problems in various contexts. UniSA Online’s 10-week short courses give you the flexibility to upskill in a certain area, stay current with developments in your field, diversify your knowledge, or even explore a new direction in your career – without having to commit to the time and cost of a full university degree.  Delivered 100% online, you’ll be able to study where and when it suits you. Access online academic and student support seven days a week, fit study around work and life commitments, view learning resources 24/7, and log in to the interactive online environment anywhere, any time and on any device. This is a third-year course from UniSA Online’s Bachelor of Data Analytics degree. Please note: you are responsible for completing any relevant prerequisite courses before enrolling in this course.Should your course have an exam it will be scheduled for Australian Central Standard Time or Australian Central Daylight Time, depending on the time of the year.

In this subject, you will be introduced to mathematical and statistical methods for analysing scientific, business or technical data to inform and support decision-making. You will explore why data is needed and how it is used to make decisions, including how data are collected, analysed, interpreted and presented, and you will learn and apply a suite of common statistical and mathematical methods to generate evidence for decision-making. You will also be introduced to data management software and learn how to represent and communicate mathematical and statistical information effectively. Through case studies relevant to your discipline, you will learn how to problem solve, generate evidence, and present solutions using data handling and statistical approaches.This subject also builds on concepts of learning through practice from Year 1 by introducing you to more complex learning experiences including: - discipline-based skills and knowledge in dynamic practice situations. This will include authentic and purposeful, industry-related experiences- concepts of managing effective relationships and communicating with others- the development and use of adaptive leadership skills and how these skills relate to innovative and entrepreneurial practice - the nature of responsible, accountable, and reflective workplace skills, and creative and critical thinking relevant to para-professional practice. You will exercise self-awareness, initiative, and judgement to manage yourself and professional relationships effectively. The application of tacit knowledge and capabilities will be reflected in a Practice Manual.

Use data to make decisions in business, science, and technology. You’ll learn how to collect data, interpret it, and present it. Get to know statistical techniques and software. Communicate results clearly. Present solutions with confidence.

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