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

This program provides students with a broad range of knowledge, skills and resources to practise effectively as a clinical pharmacy practitioner involved in the quality use of medicines and pharmaceutical care. These skills include the capacity to individualise, review and monitor a patient&#39;s medication regimen; demonstrate effective communication skills in clinical and non-clinical scenarios; critically appraise medical and pharmaceutical literature, as well as to recognise and describe social issues relating to medicine use.In addition pharmacists will gain insight into contemporary management strategies for pharmacy and health care as well as explore research and evaluation of pharmacy services and practice.Regular updates of the program integrate emerging clinical evidence and current controversies. A major strength of the program is the emphasis on developing clinical practice and the integration of evidence based medicine.

Focus on evidence-based, patient-centred care. You’ll explore up to date management and optimisation of pharmacotherapy. Gain skills to provide positive patient outcomes within a complex health environment. Taught by world-leading teachers.

Master of Clinical Pharmacy

Scientific Computing

Mapping, Analysing and Visualising Data

Introduction to Spatial Information

From Logic to Data Processing

Database for the Enterprise

Data and Analysis

Data Analytics Concepts

Business Intelligence

Data Analytics for Cyber Security

Data Visualisation

The world now runs on data. If knowledge is power, data science is increasingly how we discover that knowledge. With limitless possibilities for exploration and innovation, it is a field driven by discovery and communication. This subject introduces you to data science using simple and efficient toolkits in Python. You will explore various datasets and apply machine learning algorithms to them. In doing so, you will gain an understanding of data processing workflows, exploration, and visualisation, as well as a conceptual understanding of many of the algorithms themselves. Designed to be exploratory and collaborative, the subject offers you invaluable insights into a rapidly evolving field with diverse and increasingly important applications.

Data Science Studio 1

Equip yourself with the skills to effectively interact with large structured and unstructured data sets in our comprehensive course.Explore critical topics such as concurrency and transaction management, data organisation, hashing, indexing, and query optimisation. Learn about active databases and procedures, distributed database design, fragmentation, and replication. Master the intricacies of distributed transactions and delve into the architecture and components of data warehouses.Understand data warehouse modelling, including star and snowflake schemas, and gain proficiency in multidimensional Online Analytical Processing (OLAP) and relational OLAP analysis. Learn to design data warehouses and manage data uploading, updating, and integration.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 Bachelor of Data Analytics. When studying this course as a &quot;single subject&quot;, prior knowledge from INFT 1032 , INFT 1025 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.

In an era of rapid technological innovation, data and the capacity to process it are the most valuable assets in many fields. Studying this subject, you will learn how data is represented, stored and processed in computer systems. Extending your high-level technological literacy, the subject introduces you to a way of thinking that uses abstraction to move from low level hardware concepts to higher level programming concepts. Working through the natural progression of related concepts in computer programming, you will first explore how circuits can implement logic, then how logic lets us implement computer architecture (memory, processors), and how assembly language lets us write programs that manipulate data, before circling back to the relationship between programs and logic.

Get an introductory understanding of the links between logic, reasoning, data, and computation. Learn how data is represented, stored, and processed in computer systems. 

This subject introduces essential concepts and techniques of computing for conducting science, with special emphasis on the preparation and manipulation of data. We discuss the role of computers and computing tools in science and focus on the use of spreadsheets and other data manipulation tools. This subject introduces vital skills for tertiary learning and explores their relationship to success in future careers. Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (<a href="https://sdgs.un.org/goals" target="_blank">UNSDG</a>s) Industry, Innovation and Infrastructure

Build data and IT skills that you’ll use throughout your science degree and career. You’ll learn how to collect, prepare, and process data using computers. See how spreadsheets can enhance the efficiency and accuracy of your scientific research.

Throughout history, maps have been fundamental in shaping our view of the world and articulating our place in it, at local and regional scales. Maps guide us in everyday tasks but can also be a means to get global overviews on current topics. Today, as in the past, geo-spatial information is a valuable commodity, harnessed by researchers, governments, business and the public. But maps are not purely scientific objects; they are inherently ideological and subjective, communicating the beliefs, power structures and aspirations of particular times, people and places. This subject introduces concepts and perceptions of geographical space and relationships in different cultures, past and present.

This subject is designed to give students a general overview of spatial information and its applications. Spatial information is becoming an increasingly prevalent part of our daily lives. The ability to incorporate spatial information into various applications is a valuable skill, with applications across all manner of disciplines and vocations. This subject will introduce students to Geographic Information Systems (GIS) and basic remote sensing analysis.In this subject, students will learn the fundamental concepts underpinning spatial information, and how to represent a variety of variables in a spatial way. Content in this subject is delivered at the introductory level for students who may be curious about the potential of spatial information, and want to develop a basic skillset to analyse and interpret datasets. Concepts that will be introduced include: spatial referencing systems, types of spatial data, creating new datasets, basic image analysis, and data visualisation.

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 UniSA Online’s Data Analytics degree. When studying this course as a &quot;single subject&quot;, prior knowledge from MATH 1080, MATH 2032 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.

Mathematics Essentials in Data Analytics

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 UniSA Online’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.

Fundamentals of Mathematics for Data Analytics

Enhance your skills in applying supervised, unsupervised, and deep machine learning techniques to solve real-world data analytics challenges.In this course, you&#39;ll explore linear regression, regularisation, and logistic regression, and delve into advanced neural network architectures and learning techniques. Gain expertise in convolutional and recurrent neural networks, including long short-term memory (LSTM) networks. Learn practical applications of machine learning, including clustering, dimensionality reduction, anomaly detection, and large-scale machine learning.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 Data Analytics degree. When studying this course as a &quot;single subject&quot;, prior knowledge from INFS 3081, INFS 3076 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.

Machine Learning

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.

Master of Data Science Courses & Degrees | Open Universities Australia

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