AI and Data Science
Grasp AI and Data Science (DS) fundamentals and use AI and DS methods to tackle real-world problems in the first discipline.
Introduction
The advancement of AI technologies has significantly impacted various disciplines in aspects such as enhancing access to information, analysing complex scenarios, and generating solutions to practical problems. For example, AI aids biologists and chemists in identifying patterns that are challenging for humans to detect, simulating biological, chemical, and physical processes, and predicting outcomes. Recent reputable journal articles have highlighted the acceleration of chemical science through AI and the trends and future directions of AI in Chemistry. Additionally, AI has discovered novel and more efficient methods for solving fundamental mathematical operations, such as matrix multiplication and sorting. In the business sector, AI is reshaping practices by identifying new business opportunities and leveraging AI tools for competitive trading. Notably, the 2024 Nobel Prizes in Physics and Chemistry were awarded to AI researchers for their groundbreaking contributions, underscoring AI’s transformative impact on these fields.
Given AI’s disruptive influence in workplaces, it is essential for the next generation of graduates to have a structured pathway to recognise the development and potentials of AI technologies and apply them to solve problems in their first major discipline.
Curriculum
Second Major (SM) Unit Requirement:
SM Required Courses
24
+
SM Elective Courses
18
=
SM Total
42
Units
SM Required Courses (24 units)
| Course Code | Title | Units |
|---|---|---|
| COMP1007 | Introduction of Python and Its Applications | 3 |
| COMP1016 | Mathematical Methods for Business Computing | 3 |
| COMP2016 | Database Management | 3 |
| COMP3057 | Introduction to Artificial Intelligence and Machine Learning | 3 |
| MATH1005 | Calculus I | 3 |
| MATH1026 | Probability and Statistics with Software | 3 |
| MATH1205 | Discrete Mathematics | 3 |
| MATH2207 | Linear Algebra I | 3 |
SM Elective Courses (18 units)
| Course Code | Title | Units |
|---|---|---|
| COMP3065 | Artificial Intelligence Application Development | 3 |
| COMP3066 | Health and Assistive Technology: Practicum | 3 |
| COMP3076 | AI and Generative Arts | 3 |
| COMP3115 | Exploratory Data Analysis and Visualization | 3 |
| COMP4125 | Visual Analytics | 3 |
| COMP4026 | Computer Vision and Pattern Recognition | 3 |
| COMP4045 | Human-Computer Interaction | 3 |
| COMP4135 | Recommender Systems and Applications | 3 |
| COMP4136 | Natural Language Processing | 3 |
| MATH3206 | Scientific Computing I | 3 |
| MATH3626 | Computational Statistics for Data Science | 3 |
| MATH3805 | Regression Analysis | 3 |
| MATH3807 | Simulation | 3 |
| MATH3816 | Statistical Analysis of Sample Surveys | 3 |
| MATH3836 | Data Mining | 3 |
| MATH3845 | Interest Theory and Applications | 3 |
| MATH4225 | Foundation of Big Data and Learning | 3 |
| MATH4227 | Programming for Data Science | 3 |
| MATH4826 | Time Series and Forecasting | 3 |
Study Schedule (tentative)
It should be noted that the study of SM commences typically in the third year of studies of the students. This SM suggests students to take COMP1007 Introduction of Python and Its Applications and MATH1005 Calculus I before the third year.
| Semester 1 | Units | Semester 2 | Units |
|---|---|---|---|
| COMP1007 Introduction of Python and Its Applications | 3 | ||
| MATH1005 Calculus I | 3 | ||
| Sub-total | 0 | Sub-total | 6 |
| Semester 1 | Units | Semester 2 | Units |
|---|---|---|---|
| MATH1026 Probability and Statistics with Software | 3 | COMP1016 Mathematical Methods for Business Computing | 3 |
| MATH1205 Discrete Mathematics | 3 | COMP2016 Database Management | 3 |
| MATH2207 Linear Algebra I | 3 | ||
| Sub-total | 6 | Sub-total | 9 |
| Semester 1 | Units | Semester 2 | Units |
|---|---|---|---|
| COMP3057 Introduction to Artificial Intelligence and Machine Learning | 3 | Major Elective 3 | 3 |
| Major Elective 1 | 3 | Major Elective 4 | 3 |
| Major Elective 2 | 3 | Major Elective 5 | 3 |
| Major Elective 6 | 3 | ||
| Sub-total | 9 | Sub-total | 12 |
Target Students
This major is targeted at students who aim to learn solid AI technologies to design and implement solutions to real-world problems within their first major discipline, and to gain hands-on experiences in using AI for practical applications. The second major programme is NOT applicable to students pursuing a BSc in Computer Science or a BSc in Business Computing and Data Analytics.
Contact
For more information, please contact our department office: