The Bachelor of Data Science and Decisions is a unique multidisciplinary program which will equip students with skills in mathematical methods, statistics, computing, business decisions, and communication.
The Bachelor of Data Science and Decisions trains graduates to meet the growing demand for Data Scientists and Analysts in the Sydney region, nationally and internationally.
Students undertaking the Bachelor of Data Science and Decisions (program 3959; UAC code: 429150) will benefit from the expertise of educators across three different Schools at UNSW: Mathematics and Statistics; Computer Science and Engineering; and the UNSW Business School.
The Bachelor of Data Science and Decisions can also be combined with studying Law ().Ìý
We also offer a range of Masters-level Data Science programs. Please see ourÌýPostgraduate Coursework Programs webpageÌýfor more information on these.Ìý
Entry requirements for the Bachelor of Data Science and Decisions
Please visit theÌýUNSW Degree FinderÌýfor all admissions and entry details. This website provides information about ATARs and the relevant Lowest Selection Rank for the Bachelor of Data Science and Decisions.Ìý
Structure of the degree
The Bachelor of Data Science and Decisions (3959; UAC code: 429150) is a three-year program, comprising a central core course requirement and featuring three separate streams of study: Quantitative Data Science, Computational Data Science, and Business Data Science.Ìý
See the for the most up to date information about courses.ÌýÌý
The Bachelor of Data Science and Decisions comprises:Ìý
| Core courses: 72 Units of Credit | A major:Ìý48 UOCÌý |
| Free electives: 12 UOCÌý | General Education: 12 UOCÌý |
See below for core courses and courses by stream.
Ìý
Stage |
Core courses |
MATHE1 Quantitative Data Science |
COMPZ1 Computational Data Science |
ECONL1 Business Data ScienceÌý |
|---|---|---|---|---|
Level 1 coursesÌý |
Ìý COMP1511 Introduction to ProgrammingÌý Ìý COMP2521 Data Structures & AlgorithmsÌý Ìý DATA1001 Introduction to Data Science & DecisionsÌý Ìý ECON1101 Microeconomics 1Ìý Ìý One of:Ìý -MATH1131 Mathematics 1AÌý -MATH1141 Higher Mathematics 1AÌý Ìý One of:Ìý -MATH1231 Mathematics 1BÌý -MATH1241 Higher Mathematics 1BÌý Ìý |
Complete Level 1 Core Courses | Complete Level 1 core courses, PLUS:Ìý Ìý MATH1081Ìý Discrete MathematicsÌý |
Complete Level 1 Core CoursesÌý |
Level 2 coursesÌý |
Ìý ECON2112 Game Theory & Business StrategyÌý Ìý One of:Ìý -MATH2501 Linear AlgebraÌý -MATH2601 Higher Linear AlgebraÌý Ìý One of:Ìý -MATH2801 Theory of StatisticsÌý -MATH2901 Higher Theory of StatisticsÌý Ìý |
Complete Level 2 core courses, PLUS: One of:Ìý MATH2871 Data Management for Statistical Analysis |
Complete Level 2 core courses, PLUS:Ìý Ìý ÌýÌý Ìý COMP2041 Software Construction: Techniques & ToolsÌý |
Complete Level 2 core courses, PLUS:Ìý Ìý One of:Ìý -ECON2206 Introductory EconometricsÌý -MATH2831 Linear ModelsÌý -MATH2931 Higher Linear ModelsÌý Ìý Ìý ECON2209 Business ForecastingÌý |
Level 3 courses |
COMP3311 Database Systems DATA3001 Data Science and Decisions in Practice ECON3203 Econometric Theory & Methods |
Complete Level 3 core courses, PLUS:Ìý ÌýÌý MATH3871 Bayesian Inference & ComputationÌý |
COMP3121 Algorithms & Programming TechniquesÌý Ìý COMP9313 Big Data ManagementÌý Ìý COMP9417 Machine Learning & Data Mining |
Complete Level 3 core courses, PLUS: ECON3208 Applied Econometric Methods |
ElectivesÌý |
Ìý | 24 UOC | 12 UOC | 30 UOC (18 from UNSW Business School) Ìý |
Data Science streams
Business Data Science focuses on econometrics and business applications of data science.Ìý
Quantitative Data Science revolves around mathematical and statistical methods to interpret, understand and predict data.Ìý
Computational Data Science enables further study into manipulation and management of data.Ìý
UNSW
DataSoc
Link in with ourÌýData Science Student Society, DataSoc, and the initiatives and events they run during the year.Ìý
Ìýcovering some of the frequently asked questions about the degree.Ìý
Data Science Engagement
View our programs and student support opportunities.
More information
Students of Data Science and Decisions
Jessica Boyle
"My favourite part about the Data Science and Decisions degree is that I can customise it to fit my interests.
I get to pick electives from a pool that contains everything from Software Construction to Calculus to Economic Game Theory. This has allowed me to follow my passion for mathematics while also building on my newly discovered love of coding and then I get to throw a bit of economics into the mix!
The combination of courses available in this degree has taught me so many practical skills relevant to being a Data Scientist and I can't wait to test them out on real-world problems when I graduate."Ìý
Serena Xu
“My experience of the Data Science degree has been fabulous so far. I have learnt so many interesting things on a large variety of topics from computer science, maths and economics/business. They have all been incredibly interesting and fascinating, broadening up an entire new way of thinking for me.
One of the highlights of the program is the vast scope of knowledge that we are exposed to, which I believe is incredibly useful in the modern world. We live in a world of technology, which revolves around economics, but is all underpinned by maths and numbers. This program covers all three major areas, which are incredibly useful to contribute to society."
Saksham Yadav
"The data science program at UNSW has provided me with a solid foundation in advanced mathematics and computer science, as well as the opportunity to pursue elective courses in physics. Knowledge of these three fields has proven invaluable to my career as they are fundamental to almost every industry."
Careers in Data Science
Data scientists and analysts use their skills to make discoveries and gain new insights through exploring data. The soaring demand for employees with qualifications in data science and analysis means that career opportunities abound across a range of areas.Ìý
-
The skills of data scientists are so highly coveted due to the sheer volume of data which organisations must contend with. Data scientists have the skills to analyse and interpret the growing volumes and help convert this information into insights, which can transform businesses and industries. Over the last four years, jobs requiring the use of data analysis and data management skills and tools have doubled. Jobs in data science can attract generous remuneration.Ìý
-
"As data volume continues to grow and organisations are looking to become more strategically focused, data science and decision modelling are increasingly essential tools for businesses to thrive in a competitive global environment. Having the right analytical tools is critical for growth and success in the 21st century workplace."Ìý
Ron Elazar, Senior Consultant at Deloitte Australia. Graduate of UNSW Mathematics and Statistics.
-
“The potential for data analysis to impact big and small decisions in our lives is endless. From forecasting the peaks and troughs of chocolate biscuits in your local supermarket throughout a year, to pushing out intervention programs at the right time to people who are high risk of falling into debt. The best thing? These skills are transferable – the same techniques can be used across multiple applications, the only limit is your imagination.â€
Annelies Tjetjep, Customer Success Manager, SAS Australia.
-
"Every job, every industry, requires more and more analytics, more and more understanding of numbers, and more competency when it comes to looking at problems and modelling things."
Ben Waterhouse, Founder of Model Solutions, a data analytics and forecast modelling company for the pharmaceutical industry. Graduate of UNSW Mathematics and Statistics.
-
“Data science takes the huge and growing amount of data now available in industry and research, and makes it meaningful and useful. This improves efficiency in business, and allows scientists to answer important questions in fields like climate change and cancer research. â€
Gordana Popovic, Statistical Consultant at Stats Central UNSW, Former PhD Student (Statistics) at UNSW Mathematics and Statistics.