Graduate Certificate in Data Science

UniSA's Graduate Certificate in Data Science gives you current knowledge of data science techniques and research. It caters for students with a mathematics or an IT background, with courses tailored for both.

Taught by leading researchers you will learn to analyse and solve complex problems involving large volumes of data. You will also develop specialised technical skills in generating statistics and manipulating large databases. You will develop skills in data and statistics through courses such as:

• Big Data Basics
• Statistical Programming for Data Science
• Statistics for Data Science; and Probabilities and Data (students with an IT background)
• Relational Databases and Warehouses; and Business Intelligence and Analysis (students with a maths background)

This qualification can be studied on-campus, online or a combination of both. Once you have been accepted into the program you can select your mode of study.

In the Graduate Certificate in Data Science you will gain an understanding of core concepts in information technology and statistics. You will develop:

• cognitive skills to review, analyse, consolidate and synthesise knowledge and identify and provide solutions to complex problems in data science
• cognitive skills to think critically and to generate and evaluate complex ideas
• specialised technical and creative skills in data science
• communication skills to demonstrate an understanding of theoretical concepts
• communication skills to transfer complex knowledge and ideas to a variety of audiences

The field of data science field is evolving at a rapid rate. It will continue to grow as savvy business leaders integrate analytics into every facet of their organisation. Analytics, science, data, and reasoning are becoming embedded into decision-making processes, every day and everywhere in the business world1.

Careers to consider:

• data scientist: understanding interfaces, data migrations, big data and databases; taking the lead in processing raw data and determining the best types of analysis; mining large volumes of data to understand user behaviours and interactions; communicating data findings to IT leadership and business leaders to promote innovation
• big data visualiser: using visualisation software to analyse data, drawing implications and communicating findings; providing input on database requirements for reporting/analytics; acquiring, managing and documenting data (e.g. geo-spatial); creating visualisations from data or GIS data analysis
• business data analyst: working with stakeholders, analysts and senior management to understand business strategy and the questions that need to be asked; identifying research needs; designing experiments and making recommendations based on results; driving complex analytics projects to support the business
• information security analyst: reporting and recommendations to prevent security incidents; security control monitoring; implementing new security technology, methods and techniques; championing security best practice; reviewing systems for security risks and compliance issues
• data engineer: managing data workflows, pipelines, and ETL processes, preparing 'big data' infrastructure, working with data scientists and analysts
• machine learning analyst: building and implementing machine learning models, developing production software through systems in big data production pipeline, working with recommendation systems, developing customer analytics solutions

1Deloitte analytics trends 2016