Minor in Data Science

Minor
Data Science
Offering Academic Unit
School 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
Dr. Xiang ZHOU

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:

  1. Understand and recognise the fundamental topics of data sciences such as data mining, statistical learning and machine learning.
  2. Build skills and techniques of organising and analysing big data with a level of flexibility for different applications.
  3. Apply the data-driven modeling and learning algorithms to quantitatively solve practical problems in social, scientific, engineering and business applications.

Minor Requirements (18 credit units)

1. Core Courses (9 credit units, or 6 credit units for those exempted from taking SDSC2102)
Course Code Course Title Credit Units
SDSC2001 Python for Data Science 3
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 6 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:

  1. 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.
  2. A student who intends to take the above minor should seek approval from his/her home department and the School of Data Science.
  3. 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 SDSC courses to enrich your studies, you may try to add the courses on AIMS (for web-enabled courses) or submit a paper add form to our General Office (for paper-add courses). Please do pay attention that approval is subject to pre-requisites & approval by course leaders.


Last modified on 7 January, 2021