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

This 100% online degree in Public Health will prepare you to tackle health on a large scale.  You’ll develop the skills to understand health policy and projects and create programs and services to influence health. Learn how to evaluate data to better inform your projects and develop a global perspective to address wide-reaching challenges.  With a strong connection to real-world learning, you’ll get to analyse and work upon existing programs and services and determine how you can make them better.  Benefit from flexible learning, led by expert academics and industry-connected researchers who work within the field. Graduate prepared to provide advocate, influence and promote better health for all.

Learn to improve health at community-scale. You’ll cover biology, epidemiology, and data analysis. Explore public health policy. Use media and advocacy. Consider social and environmental factors. Graduate ready to create change locally and globally.

Bachelor of Public Health

Data Analytics Concepts

Data Analytics for Cyber Security

Introduction to Construction Business Management

Professional Practice in Data Analytics

UO R for Data Analytics

Family Business Management

Mathematics Essentials in Data Analytics

Fundamentals of Mathematics for Data Analytics

Advanced Topics in Data Analytics

Data Analytics for Accounting

They say systematic desensitisation is the cure for all phobias. In this subject, you will be &#39;desensitised&#39; to statistics through a practical and down-to-earth approach that minimises the mathematics and maximises the applications. We will concentrate on what can be done with statistics to solve social problems and describe communities. To do so, the subject describes the construction, evaluation, administration, and analysis of survey-based data. This subject prepares you for many rewarding career paths: government policy, data analyst, applied researcher, customer insights and marketing professional, and community program developer/evaluator, just to name a few. Students will benefit from a wealth of learning resources, including: lectures, tutorials, online reference videos from practicing social scientists and statisticians, contemporary readings, and summary materials to review each week. The subject is organised around a problem or question-centred model, with a focus on how to use statistics to solve real-world issues and discover data-driven insights.

Social Data Analytics

Any modern business today will utilise some form of spreadsheet tool to review, manipulate and visualise data (in the form of charts for reporting). A spreadsheet tool also enables one to undertake advanced tasks on business data, such as performing scenario analyses and undertaking complex data analytics, for example creating a forecasting model. In this subject, you will learn how to use two technological tools to undertake data analysis. The first tool is Structure Query Language (SQL), which we will use to pull relevant data into Excel and facilitate further analysis. Next, various advanced Excel analyses including pivot tables, statistical analysis, and creation of basic forecasting models will be covered. Upon successful completion of this subject you will be proficient in basic data analysis using Excel and SQL, setting you up to become an analytical and data driven problem solver. With these skills, you will also be ready to identify insights allowing you to disrupt typical business operations systematically and with a higher chance of success.

Get up to speed with basic data analysis using SQL and Excel. You’ll delve into the data analytics concepts and processes used in business today. Find, present, and explain data trends. Solve complex business problems.

This subject was previously known as The Search for Everything: data Analytics and Storytelling in the 21st Century.Note: If you study this subject in Winter, the subject will be a shorter duration (5 weeks) and will require at least 20 hours of study per week. Summer also has a shorter duration (9 weeks).Data analytics is present in every moment of our lives. Data flows through smart phones, smart cars, smart fridges, streaming services, social media, loyalty programs, business operations, logistics, leisure and entertainment like a current shaping everything. At the heart of comprehensive data collection and analysis is a quest to understand ‘everything’, and this unit invites students into the multi-layered data-driven world where the real and the digital meet.MSP202 teaches students how to clean, manipulate, interpret, and transform data into creative visualisations using Microsoft Excel, to tell data-driven stories for invested stakeholders. Students examine the impact of data analytics in the context of public and private life; professional, political, and societal domains; and the reach of data across local, national, and global contexts. In hand with foundational data skills, this unit explores ethical issues relating to data surveillance, the implications of data mining on human behaviour, and the analytics machines central to the functioning of big tech platforms such as Google, Amazon, and Facebook.Please Note: All students studying at Murdoch University will need to complete the compulsory unit, Murdoch Academic Passport (MAP100), which only takes 2-3 hours to complete online. Find out more: <a href="http://goto.murdoch.edu.au/MurdochAcademicPassport" target="_blank">http://goto.murdoch.edu.au/MurdochAcademicPassport</a>

Work with data and see the impact you can have on everyday life. From collection to analysis, you’ll explore how data tells stories, informs decisions, and influences human behaviour. Get creative with visualisation. Get ethical with your approaches. 

The Search for Everything: Data Analytics and Storytelling

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

In this subject you will learn about three key areas of data analytics in cyber security. Using a series of tools to develop your cyber security analytical tool kit, you will begin by conducting basic data analysis using Excel and build an analytical dashboard in Excel which will allow you to dynamically update the dashboard based on the dataset entered. With these skills you will then transition to network data analytics for threat hunting and incident response, culminating in an assessment on post-infection threat analysis. Finally, you will undertake active network monitoring and analysis using cutting edge industry tools, that will form your final assessment. The progression of practical skill development in this subject will add progressive complexity to your understanding of data analytics scoped to cyber security and align with a Security Operations Centre (SOC) analyst&#39;s role.

Learn how to collect, analyse, and present data for cybersecurity. You’ll explore analytical techniques and tools that identify patterns, anomalies, and potential security breaches. Harness the power of data to protect organisations from cyber threats. 

In today’s data economy, businesses are blessed and cursed with an abundance of data. This proliferation of data is creating new professions and changing existing professions. One profession that is witnessing a rapid change due to the explosion of data is accounting. While there exists a plethora of data available to businesses, accounting data remains a source of data that is reliable, relevant and accessible for all organisations. This subject will introduce students to the very recent changes ushered in by the big data revolution to the accounting profession, introduce students to how data analytics is being applied to accounting data, develop their skills in analysing and presenting accounting data to management and also develop their abilities to critically evaluate the underlying accounting systems and processes related to both the collection, creation, processing and production of accounting data within organisations.

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.

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. Adelaide University&#39;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 Adelaide University&#39;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.

R for Data Analytics

Gain a solid foundation in mathematical and statistical concepts essential for mastering data analytics.This course covers key topics such as linear algebra concepts, vector and matrix operations, and the fundamentals of eigenvalues and eigenvectors. Explore dynamical systems and optimisation techniques, including gradient descent. Dive into linear regression, probability concepts—such as probability laws, Bayes&#39; rule, and independence—and selected probability distributions. Learn the principles of statistical inference and how to apply them effectively to data analytics.This is a first-year course from Adelaide University&#39;s Data Analytics degree. When studying this course as a &quot;single subject&quot;, prior knowledge from MATH 1033, MATH 2018 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.

Build a strong mathematical foundation for your data analytics journey with our comprehensive course.Explore essential topics including arithmetic and algebraic operations, linear and quadratic equations, and logarithmic and exponential functions. Dive into matrices and determinants, and master absolute values, inequalities, and sets. Gain a solid grounding in basic probability and statistics to support your data analytics skills.Join us to develop the mathematical skills needed to excel in data analytics and unlock new opportunities for data-driven insights!This is a first-year course from Adelaide University&#39;s Data Analytics 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. 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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