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

UniSQ&#39;s Master of Science (Mathematics and Statistics) is ideal for graduates from any discipline who are looking to develop their mathematics/statistics knowledge and skills. Even if you haven’t studied in some time, this degree will provide you with an in-depth education in mathematical and/or statistical techniques and problem solving skills. Examine the fundamentals of theoretical and applied statistics, mathematical modelling and operations research, and computing and coding skills. This degree incorporates practical methods for undertaking analysis, modelling and evidence-based, data-driven decision-making.

Master of Science (Mathematics and Statistics)

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

Unlock the power of empirical methods in business data analysis and research with our foundational course.Gain a solid introduction to statistical analysis and techniques essential for cross-sectional and time-series modelling. Understand the rationale behind these techniques, along with their applications and limitations. Dive deep into regression modelling techniques, learning about their concepts, practical applications, interpretation, and constraints.Utilise appropriate computer software to enhance your analytical capabilities and explore alternative methods of reporting and using analytical results effectively.Join us to build a foundation in empirical methods, empowering you to analyse business data with confidence.UniSA Online’s 10-week short courses give you the flexibility to up-skill 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 second-year course from UniSA Online’s Business (Economics, Finance and Trade) degree. When studying this course as a &quot;single subject&quot;, prior knowledge from ECON 1010, MATH 1075 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.

Empirical Business Analysis

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 data sets relevant to your discipline, you will learn how to problem solve, generate evidence, and present solutions using data handling and statistical approaches.

This unit provides a broad introduction to statistical concepts and data analysis techniques, providing basic statistical knowledge. The unit dedicates itself to developing an understanding of statistical practice and illustrates this by studying the techniques most commonly employed in the sciences, social sciences, and humanities. Topics covered in this unit include data collection methods, data quality, data summarisation, basic probability, random variables, and statistical models like the normal distribution, followed by sampling distributions and statistical inferences about means and proportions. Also studied are analysis methods relating to comparisons, counted data and relationships, including regression and correlation. Excel is used for handling and analysing data and word processing to report the results.

Introductory Statistics

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

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.

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 up-skill 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.

In today&#39;s world, good decision-making relies on data and data analysis. This course helps students develop the understanding that they will need to make informed decisions using data and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for students in business, economics and similar disciplines, although it may have wider interest. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require students to write effectively and communicate their ideas with clarity. The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers. Students will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics.

Get ready to make confident business decisions backed by data. Dive into statistics concepts and tools, then test your skills on a real-world scenario. When you know how to use statistics, you’re empowered to communicate with clarity and persuasion.

Data Analytics I

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 up-skill 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.

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