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3.4.2.2    Second Major in Data Analytics

Host Department: Statistics

To be awarded a B.Sc. with a second major in Data Analytics, candidates must satisfy the following:

Levels Second Major Requirements Cumulative Major MCs
Level 1000(10 – 12 MCs) Pass

One of the following modules:

CS1010/CS1010E/CS1010J/CS1010S/CS1010X Programming Methodology

IT1007 Introduction to Programming with Python and C

One of the following modules:

  • MA1101R Linear Algebra I
  • MA1311 Matrix Algebra
  • MA1508E Linear Algebra for Engineering
  • MA1513 Linear Algebra with Differential Equations (2 MCs) †

One of the following modules:

  • MA1102R Calculus
  • MA1312 Calculus with Applications
  • MA1505 Mathematics I
  • MA1507 Advanced Calculus
  • MA1511 Engineering Calculus (2 MCs) and
       MA1512  Differential Equations for Engineering (2 MCs)
  • MA1521 Calculus for Computing
10 – 12
Level 2000(16  MCs) Pass

CS2040 Data Structures and Algorithms

ST2131/MA2216 Probability

ST2132 Mathematical Statistics

One of the following modules:

  • DSA2101 Essential Data Analytics Tools: Data Visualisation
  • DSA2102 Essential Data Analytics Tools: Numerical Computation
26 – 28
Level 3000 and 4000 (20 – 24 MCs) Pass

ST3131 Regression Analysis*

One of the following modules:

  • DSA3102 Essential Data Analytics Tools: Convex Optimisation*
  • DBA3701 Introduction to Optimisation
  • MA3236 Nonlinear Programming*
  • MA3252 Linear and Network Optimisation

One module from List I

One module from List II

One other module from List I or List II

One additional module from List I or List II †

48 – 50

* Students who passed EC3303 Econometrics I need not read ST3131. They are allowed to read and pass an additional module from List I or List II in lieu of ST3131. However, where a module in List I or Lit II requires ST3131 as pre-requisite, the pre-requisite may not be fulfilled by EC3303.

† Applicable only to students who use MA1513 Linear Algebra with Differential Equations (2 MCs) to fulfil the second major requirements.

List I^

DSA4211      High-Dimensional Statistical Analysis

DSA4212      Optimisation for Large-Scale Data-Driven Inference*

List II

CS3244          Machine Learning

ST3240          Multivariate Statistical Analysis

ST3247          Simulation

ST3248         Statistical Learning I

ST4248         Statistical Learning II

* Students may need to read additional modules outside the second major requirements to satisfy the pre-requisites of these modules.

^ (1) As part of the Data Science and Analytics programme, FoS is planning to co-develop modules on data analytics for functional areas such as business, healthcare and public policy making with other Faculties/Schools. These modules will be coded as DSA modules and added to List I. (2) Students who participate in credit-bearing full-time internships/industrial attachments/professional placements as part of their degree requirements may be approved to double-count up to 8 MCs into List I if their internships/industrial attachments/professional placements have substantial data-analytics content, provided the limit of 16 MCs of double-counting in primary and second major requirements is not exceeded.

This second major is not offered with the following primary majors and minors:

Primary Majors: Applied Mathematics, Business Analytics, Computational Biology, Computer Engineering, Computer Science, Data Science and Analytics, Industrial and Systems Engineering,  Information Security, Mathematics, Quantitative Finance, Statistics.

Minors: Financial Mathematics, Mathematics, Statistics.