Bachelor of Data Science and Analytics
University of Wollongong
About
The Bachelor of Data Science and Analytics is designed for students who wish to develop strong, rigourous mathematical, statistical and computing skills, which combined with good communication and consulting skills, will enable them to pursue a career in the data driven industries.
The vast increase in data available in science, industry, commerce and governments has led to demand for professionals who can design, organise, manage and manipulate databases and sources, and analyse and extract useful and actionable insights and information from data sets of differing size and complexity and effectively communicate the conclusions.
The degree will develop highly transferable technical and professional skills.A Data Science and Analytics (Honours) degree is available to candidates who have achieved a distinction average or better in the Bachelor Data Science and Analytics degree.
Structure
To qualify for award of this degree, a candidate must satisfactorily complete at least 144 credit points, comprised of the following:
Year 1
| Subject Code | Subject Name | Credit Points | Session(s) |
|---|---|---|---|
| CSIT110 | Fundamental Programming with Python | 6 | Autumn |
| CSIT111 | Programming Fundamentals | 6 | Autumn, Spring |
| CSIT113 | Problem Solving | 6 | Autumn |
| MATH187 | Mathematics 1: Algebra and Differential Calculus | 6 | Autumn |
| CSCI203 | Algorithms and Data Structures | 6 | Spring |
| CSIT115 | Data Management and Security | 6 | Autumn, Spring |
| MATH188 | Mathematics 2: Series and Integral Calculus | 6 | Spring |
| STAT101 | Introduction to Statistics | 6 | Spring |
Year 2
| Subject Code | Subject Name | Credit Points | Session(s) |
|---|---|---|---|
| CSCI235 | Database Systems | 6 | Autumn, Spring |
| CSIT121 | Object Oriented Design and Programming | 6 | Autumn, Spring, Summer 2020/2021 |
| MATH201 | Multivariate and Vector Calculus | 6 | Autumn |
| STAT201 | Random Variables and Estimation | 6 | Autumn |
| ISIT312 | Big Data Management | 6 | Spring |
| MATH203 | Linear Algebra and Groups | 6 | Spring |
| MATH318 | Optimisation and Applications | 6 | Spring |
| STAT202 | Statistical Inference and Introduction to Model Building | 6 | Spring |
Year 3
| Subject Code | Subject Name | Credit Points | Session(s) |
|---|---|---|---|
| CSCI317 | Database Performance Tuning | 6 | Autumn |
| DSAA311 | Data Analytics and Visualisation | 6 | Not available in 2020 |
| STAT332 | Generalised Linear Models | 6 | Autumn |
| STAT335 | Sample Surveys and Experimental Design | 6 | Autumn |
| CSCI316 | Big Data Mining Techniques and Implementation | 6 | Spring |
| DSAA301 | Professional Practice | 6 | Not available in 2020 |
| STAT301 | Statistical Methods for Data Science | 6 | Spring |
| STAT304 | Stochastic Methods in Statistical Analysis | 6 | Spring |
Entry requirements
Information on academic and English language requirements, as well as eligibility for credit for prior learning, is available from the Course Finder.
Learning outcomes
Course Learning Outcomes are statements of learning achievement that are expressed in terms of what the learner is expected to know, understand and be able to do upon completion of a course. Students graduating from this course will be able to:
CLO Description 1 Identify and address ethical issues arising in their professional activities. 2 Design, organise, manage and manipulate databases and sources, and analyse and extract useful and actionable insights and information from data sets of differing size and complexity, including unstructured data. 3 Determine appropriate procedures to use to generate, obtain and analyse complex data in a wide variety of situations. 4 Effectively communicate with a client, user or decision maker to identify the problems and determining the next step, and report findings. 5 Plan, manage your involvement in, and undertake a project, both with autonomy and as part of a team, and report to a client in a timely and effective manner.
Institution
