Who are the graduate level courses for?
If you already have a degree in science, engineering, arts or computing, you can become a data scientist as well!
Develop your skills in the analytic, organisational and/or computational aspects of data and gain hands on experience with state-of-art tools from Australia's most comprehensive and respected Data Science faculty.
The course has a focus on enriching your future career, helping you master the field with fundamental theory, experience with real tools on real problems, and exposure to key industry players.
The online Graduate Diploma in Data Science (GDDS) and the on-campus Master of Data Science (MDS) will put your analytical thinking and problem solving to the test. Whether you've developed your talent for understanding data through statistics, analytics, finance or economics, engineering, mathematics or physics – you will broaden the depth and creativity of your data analysis.
- Course lecturers have a wealth of experience across the broad data science field.
- The courses offer a unique blend of statistical machine learning, visualisation and digital curation that forms the core of the degree.
- Core statistical and computing theory is combined with experience on real industry tools and business problems.
- The course also covers the two main languages for data science, R and Python, and distributed computing with Hadoop and Spark.
- There are 6 teaching periods in the year, each is six weeks long, and ordinarily the course takes 16 months to complete.
- Students can expect to require about 23 hours a week of study and contact during teaching periods.
- Coursework covers the business and objectives of data science, data analysis, and of data management as well as statistical modelling with a focus on understanding rather than theory, applied and theoretical data analysis and visualisation, data preprocessing, big data handling.
On campus MDS:
- Students can be full or part-time in the usual two-semester teaching model, typically taking 18-24 months to complete depending on background, and including a year of coursework.
- Students missing pre-requisite computing background can undertake initial bridging coursework.
- The main coursework has a common core that covers the business and objectives of data science, data analysis, and of data management.
- You can then tailor 50% of your coursework with data science electives covering applied and theoretical data analysis and visualisation, data preprocessing, big data handling and data in society. The remaining 25% of your coursework is then drawn as well from broader, related electives.
- To complete the masters and give you professional experience, you can either undertake an Industry Experience projectin a small team for an industry client, or undertake research with our faculty to demonstrate your ingenuity.
The main units available to students are listed below. Other data science relevant masters level units from Monash are also included.
|Unit code||Unit title|
|FIT5145||Introduction to Data Science|
|FIT5197||Modelling for data analysis|
|FIT5147||Data exploration and visualisation|
|FIT5146||Data curation and management|
|FIT5148||Distributed and big data processing|
|FIT5149||Applied data analysis|