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The Master of Financial Technology offers a qualification that provides advanced studies in modern technology with knowledge of the global financial market. The finance industry is becoming more information and data-driven. Businesses have greatly benefited from using advanced data analytics to improve their financial position.The program is designed for students, either with or without the background in IT or Finance, to enter a rapidly growing FinTech industry, which includes big data analytics, artificial intelligence, blockchain, programming and cybersecurity, while at the same time understanding how financial markets function.Students will be learning advanced Excel skills combined with the Python programming language, R Studio and Tableau to evaluate a large amount of financial data and assess different investment decisions across various market conditions.This program provides a unique opportunity to combine these skills in a unified degree, and graduates will comprehend specialised knowledge in the financial services organisation and the application of emerging technologies in the finance industry.The Master of Financial Technology has two different entry points depending on your previous studies:Standard pathway: If you are interested in moving into the field and do not have a background in finance or IT, you will complete the standard pathway (2 years) which includes a compulsory foundation component.Advanced standing pathway: If you have a prior Finance or Information technology degree you will follow the advanced standing pathway (1.5 years) and skip the foundation course component. You will focus on core Finance and Information Technology courses.

Explore the latest technologies and business models that are changing financial services. Offering study pathways for graduates from IT, finance, and other backgrounds. Disrupt banking and finance. Advance the industry and stay relevant.

Master of Financial Technology

Data and Analysis

Data Analytics Concepts

Business Intelligence

Scientific Computing

Data Analytics for Cyber Security

Data Visualisation

Data-Based Critical Thinking

HR Analytics

Professional Practice in Data Analytics

Disruption in Business

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.

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.

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.Data analytics is present in every moment of our lives. Smartphones, smart cars, smart fridges. Netflix, Stan, Amazon, and Facebook. Strava and Nike providing feedback on your current workout. FlyBuys, Qantas Frequent Flyers, and Woolworths Rewards. At the heart of comprehensive data collection and analysis is a desire and quest to understand ‘everything’, and this unit invites students into every layer of this data-driven world. MSP202 teaches students how to clean, manipulate, and transform data into creative visualisations using Microsoft Excel to tell a story for invested stakeholders. Students examine the impact of data analytics in the context of their career and the world of work more broadly, including the spheres of marketing and communications, journalism, the creative arts, sport, health, and education, grounding practical skills in a social context and reinforcing the how and why of data analysis. We explore ethical issues relating to data surveillance, the positive and negative implications of data mining on human behaviour, and the analytics machines central to the functioning of Google, Amazon, Facebook, and Uber. The unit offers students the skills to engage with data with an awareness of ethical considerations, and the capacity to manipulate that data to make sense of it for invested stakeholders, telling a story that has the capacity to change the world.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 the 21st Century

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 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.

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.

The use of data in management decision making is essential for organisations of all sizes to be able to uncover and respond to opportunities and threats. The fields of business intelligence and analytics (including data mining) have consistently been top investment priorities, and with the new capacities of self-serve, visualisations and cloud-based (web-based) technologies, organisations are becoming even more information rich. The course explores how professionals can use information systems and business intelligence to conduct an analysis of business operations and performance for enhanced decision making. Be introduced to concepts, processes and technologies in business intelligence and business analytics, and learn to use selected analytics, to interpret solutions for business-related problems and provide relevant business advice.  This is a great course for those who have not had experience with business intelligence, and who don’t come from a technical background.  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 degree. When studying this course as a &quot;single subject&quot;, prior knowledge from 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.

Make better business decisions using analytics and information systems. You’ll explore key analytics tools and methods. Gain the competitive advantage with reliable business intelligence. Identify opportunities, optimise processes, and drive profitability.

Data analytics Courses & Degrees | Open Universities Australia

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