Bachelor of Science (Honours) Scheme in Data Science and Artificial Intelligence (JUPAS Code: JS3223)
Bachelor of Science (Honours) Scheme in Data Science and Artificial Intelligence (JUPAS Code: JS3223)
Curriculum
All admitted students will embark on a Common Year One curriculum within the Faculty of Computer and Mathematical Sciences.
Years Two-Four: Students are streamed into one of the two Majors under the Scheme based on their individual preferences. They continue to complete subjects required by the Major.
Scheme Curriculum Summary (for normal intake):
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BSc (Hons) Scheme in Data Science and Artificial Intelligence |
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Subject |
Credits |
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General University Requirements (GUR) (27 credits) |
APSS1L01 Tomorrow’s Leaders |
3 |
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DSAI1202 Introduction to Artificial Intelligence and Data Analytics |
2 |
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MM1031 Introduction to Innovation and Entrepreneurship |
1 |
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CAR-A Subject |
3 |
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CAR-M Subject |
3 |
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CAR-N Subject [recommend AF1605] |
3 |
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Service Learning Subject |
3 |
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LCR Electives |
6 |
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AI-as-Tool for Language Learning (AITLL) |
3 |
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Healthy Lifestyle |
0 |
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Common Compulsory Subjects (35 credits) |
CMS1000 Computer and Mathematical Sciences Professionals in Society |
1 |
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AMA1702 Calculus |
3 |
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AMA1751 Linear Algebra |
3 |
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COMP1010 Computational Thinking and Problem Solving |
3 |
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COMP1011 Programming Fundamentals |
3 |
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DSAI1103 Principles of Data Science |
3 |
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DSAI2201 Data Structures and Algorithms |
3 |
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DSAI2202 Database Principles |
3 |
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DSAI3911 Professionalism and Ethics in Data Science and AI |
2 |
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DSAI4205 Big Data Analytics |
3 |
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DSAI4901 Capstone Project (6-credit two-semester subject) |
6 |
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ELC3124 Professional English for Data Science and Analytics Students |
2 |
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Major Compulsory Subjects (33-39 credits)
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Artificial Intelligence (AI)* |
Artificial Intelligence with Financial Technology (AIFT) |
BSc in Data Science and Analytics (DSA) |
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Subject |
Credits |
Subject |
Credits |
Subject |
Credits |
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AMA2233 Data Analytics and Visualization |
3 |
AF1605 Introduction to Economics# ^ |
3 |
AMA2233 Data Analytics and Visualization |
3 |
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AMA2702 Multivariable Calculus |
3 |
AF2108 Financial Accounting or AF2111 Accounting for Decision Making |
3 |
DSAI4207 Introduction to Large Language Models |
3 |
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COMP2021 Object-oriented Programming |
3 |
AF3313 Business Finance |
3 |
AMA2702 Multivariable Calculus |
3 |
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COMP2322 Computer Networking |
3 |
AMA2233 Data Analytics and Visualization |
3 |
DSAI3102 Multivariate Statistical Analysis |
3 |
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COMP3211 Software Engineering |
3 |
COMP2021 Object-oriented Programming |
3 |
AMA3640 Statistical Inference |
3 |
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DSAI3201 Introduction to Natural Language Processing |
3 |
COMP2322 Computer Networking |
3 |
AMA3724 Further Mathematical Methods |
3 |
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DSAI4203 Machine Learning |
3 |
COMP3211 Software Engineering |
3 |
AMA3820 Operations Research Methods |
3 |
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DSAI4204 Data Mining and Data Warehousing |
3 |
COMP3334 Computer Systems Security |
3 |
DSAI4203 Machine Learning |
3 |
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DSAI4207 Introduction to Large Language Models |
3 |
DSAI3201 Introduction to Natural Language Processing |
3 |
DSAI3101 Bayesian Methods for Data Science |
3 |
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DSAI4208 Introduction to Machine Vision and Intelligence |
3 |
DSAI4201 Data Protection and Security |
3 |
AMA4850 Optimization Methods |
3 |
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DSAI4209 Introduction Generative AI and Foundation Models |
3 |
DSAI4203 Machine Learning |
3 |
DSAI4204 Data Mining and Data Warehousing |
3 |
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DSAI4207 Introduction to Large Language Models |
3 |
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DSAI4206 Emerging Topics in FinTech |
3 |
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Elective Subjects (19-25 credits) |
AI Elective x 4 |
12 |
AIFT Elective x 4 |
12 |
DSA Elective x 4 |
12 |
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Free Elective |
13 |
Free Elective |
7 |
Free Elective |
13 |
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WIE (2 training credits) |
DSAI3001 Work-Integrated Education (Training Credits) |
2 |
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Total |
120 + 2 (training credits) |
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# If students use AF1605 to satisfy the CAR-N requirement (i.e., AF1BN02), they will be required to take another 3-credit free elective subject to make up for the total credit requirement.
^ BScAIFT students are required to take AMA1751 and COMP1011 as the 2 faculty electives.
* Subject to approval.
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