Minor in Data Science
Minor
Data Science
Offering Academic Unit
Department of Data Science
Exclusive Majors
(Students who study the following majors are not allowed to choose this minor)
Data Science or Data and Systems Engineering
Minor Leader
Professor Yu YANG
Note: The following curriculum information is subject to periodic review and changes.
Aims of Minor
This minor aims to provide students with adequate education and training in data science for understanding the methodologies and applying techniques to various disciplines.
Intended Learning Outcomes of Minor (MINILOs)
Upon successful completion of this minor, students should be able to:
- Understand and recognise the fundamental topics of data sciences such as data mining, statistical learning and machine learning.
- Build skills and techniques of organising and analysing big data with a level of flexibility for different applications.
- Apply the data-driven modeling and learning algorithms to quantitatively solve practical problems in social, scientific, engineering and business applications.
Minor Requirements (16 credit units)
| 1. Core Courses (10 credit units, or 7 credit units for those exempted from taking SDSC2102) | ||
| Course Code | Course Title | Credit Units |
| SDSC2001 | Python for Data Science | 4 |
| SDSC2102 | Statistical Methods and Data Analysis* | 3 |
| SDSC3006 | Fundamentals of Machine Learning I | 3 |
Remarks: *Students who have completed MS2602 and obtained a grade “B-” or above will be exempted from taking SDSC2102. These students will be required to take any course from the semi-core or elective course list to make up the 3 missing credit units.
| 2. Semi-core Courses (at least 3 credit units) | ||
| Course Code | Course Title | Credit Units |
| SDSC2002 | Convex Optimization | 3 |
| SDSC2004 / GE2343 | Data Visualization | 3 |
| SDSC3002 | Data Mining | 3 |
| SDSC3007 | Advanced Statistics | 3 |
| SDSC4016 | Fundamentals of Machine Learning II | 3 |
| 3. Electives (at least 3 credit units) | ||
| Course Code | Course Title | Credit Units |
| GE1356 | Introduction to Data Science | 3 |
| SDSC2003 | Human Contexts and Ethics in Data Science | 3 |
| SDSC2005 | Introduction to Computational Social Science | 3 |
| SDSC3001 | Big Data: The Arts and Science of Scaling | 3 |
| SDSC3004 | Computational Optimization | 3 |
| SDSC3005 | Computational Statistics | 3 |
| SDSC3010 | Digital Trace Analytics | 3 |
| SDSC3011 | Social Data Processing and Modelling | 3 |
| SDSC3013 | Introduction to Social Media Analytics | 3 |
| SDSC3015 | Knowledge Graph and Cognitive Computing | 3 |
| SDSC3016 | Social Network Analysis | 3 |
| SDSC3017 | Game Theory and Its Application | 3 |
| SDSC3027 | Smart Logistics and Transportation | 3 |
| SDSC3105 | Bayesian Analysis | 3 |
| SDSC4001 | Foundation of Reinforcement Learning | 3 |
| SDSC4008 | Deep Learning | 3 |
| SDSC4009 | Data Intelligence in Action | 3 |
| SDSC4011 | Experimental Research for Social Media | 3 |
| SDSC4018 | AI in Systematic Trading | 3 |
| SDSC4019 | Stochastic Processes and Applications | 3 |
| SDSC4110 | Statistical Design and Analysis of Experiments | 3 |
Note:
- A student is required to obtain an average GPA of 2.0 or above for the courses from the Core, Semi-core and Elective course lists stated above, and Grade C- or above in all courses for the award of Minor in Data Science.
- A student who intends to take the above minor should seek approval from his/her home department and the Department of Data Science.
- Students who wish to take a Minor in Data Science should take note that they are required to fulfill the prerequisites of the required courses.
Application
Students are required to submit their declaration of minor request through AIMS under Course Registration. Information on the key dates, process and steps for the Declaration of Minors are available in the website of the Academic Regulations and Records Office.
If you only wish to enrol in specific DS courses to enrich your studies, you may try to add the courses on AIMS. Please do pay attention that approval by course leaders is subject to fulfilment of pre-requisites.
Last modified on 12 September, 2024