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3.3.3.10    Statistics

Statistics is the scientific application of mathematical principles to the collection, analysis, and presentation of numerical data. How does a business determine if an available site for a new restaurant is a potentially successful location? How does the health authority assess statistical evidence for the effectiveness of a new vaccine? How does an insurance company determine the risk level of a new proposal?

Statisticians contribute to scientific inquiry by applying their mathematical and statistical knowledge to the design of surveys and experiments; the collection, processing, and analysis of data; and the interpretation of the results. Statisticians may apply their knowledge of statistical methods to a variety of subject areas, such as biology, business, economics, education, engineering, finance, marketing, medicine, psychology, public health, and sports. In particular, data science is an interdisciplinary field, driven by statistical ideas, for obtaining insights from data in various forms. It is often, but not always, concerned with methodology for big data problems, those which are at the forefront of the challenges of modern science.

Programme Structure and Curriculum Rationale

Statistics is an interdisciplinary subject by nature. In the development of Statistics, Mathematics and Computer Science respectively provide the theoretical foundation and the computational tools while real-world problems stimulate and guide further research. These considerations are reflected in the curriculum. In addition to Statistics modules, Statistics students are encouraged to read modules in Mathematics and Computer Science.

The core statistical education consists of probability and stochastic processes, statistical principles, computer-aided data analysis, and regression analysis. Honours students majoring in Statistics have the option to specialise in Data Science or in Finance and Business Statistics. The department has particular strengths in computational statistics, high-dimensional statistical analysis and statistical learning for data science, and in time series analysis, statistical methods for actuarial science and finance, and stochastic processes for Finance and Business Statistics.

Career Prospects

The world is becoming increasingly quantitative and data-focused. Many professions, organisations and businesses depend on numerical measurements to make decisions in the face of uncertainty. Statistics graduates may look forward to being employed as statisticians in government, medical and pharmaceutical industry, manufacturing and engineering companies, banking and financial institutions, research and tertiary institutions.

Further, there are many jobs that do not bear the word “statistician” but will rely much on the knowledge and training that a student can acquire from studying Statistics at NUS. Some of these are business analyst, quality assurance engineer, pharmaceutical engineer, marketing professional, financial analyst, banking executive, telecommunication executive, actuary, data analyst, and risk analyst.

Graduation Requirements (Statistics)

To be awarded a B.Sc. or B.Sc. (Hons.) with a primary major in Statistics, candidates must satisfy the following:

Module Level Major Requirements Cumulative Major MCs
Level-1000

(16 MCs)

Pass

ST1131         Introduction to Statistics

or

ST1232        Statistics for Life Sciences

MA1101R    Linear Algebra I

MA1102R    Calculus

CS1010        Programming Methodology

or

CS1010E      Programming Methodology

or

CS1010S      Programming Methodology

or

CS1010X    Programming Methodology

16
Level-2000

(16-17 MCs)

Pass

ST2131/       Probability
MA2216

ST2132         Mathematical Statistics

ST2137         Computer Aided Data Analysis

MA2311        Techniques in Advanced Calculus

or

MA2104         Multivariable Calculus

or

MA2108       Mathematical Analysis I

or

MA2108S     Mathematical Analysis I (S)

32-33
Level-3000

(28-29 MCs)

Pass

ST3131        Regression Analysis

ST3236        Stochastic Processes I

  • Three other modules from ST32xx or ST4xxx modules
  • Two additional modules from ST32xx or ST4xxx modules or List A or List B modules
60-62
Level-4000

(32-33 MCs)

Pass

  • ST4199        Honours Project in Statistics OR
          ST4299        Applied Project in Statistics
  • ST4231        Computer Intensive Statistical Methods
  • ST4233        Linear Models
  • Two other modules from ST4xxx modules
  • One additional module from ST4xxx, ST5xxx or List B modules
92-94

List A
MA3209    Metric and Topological Spaces or Mathematical Analysis III
MA3218    Applied Algebra
MA3227    Numerical Analysis II
MA3229    Introduction to Geometric Modelling
MA3233    Combinatorics and Graphs I
MA3236    Nonlinear Programming
MA3252    Linear and Network Optimisation
MA3256    Applied Cryptography
MA3259    Mathematical Methods in Genomics
MA3269    Mathematical Finance I
QF3101    Investment instruments: Theory and Computation
CS3210    Parallel Computing
CS3230    Design and Analysis of Algorithm
CS3223    Database Systems Implementation
CS3243    Introduction to Artificial Intelligence
CS3244    Machine Learning
EC3304    Econometrics II

List B
MA4211    Functional Analysis
MA4229   Fourier Analysis and Approximation or Approximation Theory
MA4230    Matrix Computation
MA4233    Dynamical Systems
MA4254    Discrete Optimisation
MA4260    Stochastic Operations Research
MA4261    Coding and Cryptography
MA4262    Measure and Integration
MA4268    Mathematics for Visual Data Processing
MA4269    Mathematical Finance II
CS4231    Parallel and Distributed Algorithm
CS4220    Knowledge Discovery Methods in Bioinformatics
CS4243    Computer Vision and Pattern Recognition
CS4248    Natural Language Processing
CS5340    Uncertainty Modelling in AI
DSA4211 High-Dimensional Statistical Analysis
DSA4212 Optimisation for Large-Scale Data-Driven Inference
EC4303    Econometrics III

Honours students majoring in Statistics have the option to qualify for specialisation in

  1. Data Science or
  2. Finance and Business Statistics.

(A) To be awarded a specialisation in Data Science, a candidate must pass at least six modules (24 MCs) from the following two lists, with at least two modules (8 MCs) from list DS 1, as part of the major requirements for B.Sc. (Hons.) with a primary major in Statistics:

 

DS 1

ST3240 Multivariate Statistical Analysis

CS3243 Introduction to Artificial Intelligence*

CS3244 Machine Learning*

ST3248 Statistical Learning I

ST4248 Statistical Learning II

 

DS 2

ST3247 Simulation

CS3210 Parallel Computing*

MA3252 Linear and Network Optimisation

ST4234 Bayesian Statistics

CS4231 Parallel and Distributed Algorithms*

DSA4211 High-Dimensional Statistical Analysis

DSA4212 Optimisation for Large-Scale Data-Driven Inference

MA4268 Mathematics for Visual Data Processing*

 

*Modules with hidden pre-requisites (indicated in brackets):

  • CS3210 (CS2100 Computer Organisation)
  • CS3243 (CS1232 Discrete Structures and CS2040 Data Structures and Algorithms)
  • CS3244 (CS2040 Data Structures and Algorithms)
  • CS4231 (CS3230 Design and Analysis of Algorithms or CS3210 Parallel Computing)
  • MA4268 (MA2213 Numerical Analysis I)

For students who wish to read these modules for the Data Science specialisation, the Faculty/Department will provide them with academic advice on their study plans (where necessary) as such students would have to read ‘additional’ pre-requisite modules.

(B) To be awarded a specialisation in Finance and Business Statistics, a candidate must pass at least six modules (24 MCs) from the following two lists, with at least two modules (8 MCs) from each of the lists (FBS 1, FBS 2), as part of the major requirements for B.Sc. (Hons.) with a primary major in Statistics:

FBS 1
ST3233    Applied Times Series Analysis
ST3234    Actuarial Statistics
ST3246    Statistical Models for Actuarial Science
MA3269     Mathematical Finance I
ST4245    Statistical Methods for Finance
MA4269     Mathematical Finance II

FBS 2
ST3232 Design and Analysis of Experiments
ST3239    Survey Methodology
ST3242 Introduction to Survival Analysis
ST3244 Demographic Methods
ST4238    Stochastic Processes II

Summary of Requirements B.Sc. B.Sc. (Hons.)
University Requirements 20 MCs 20 MCs
Faculty Requirements 8 MCs* 8 MCs*
Major Requirements 60-62 MCs 92-94 MCs
Unrestricted Elective Modules 30-32 MCs 38-40 MCs
Total 120 MCs 160 MCs

*  Faculty requirements of 12 MCs and 16 MCs [required for the B.Sc. and B.Sc. (Hons.) programmes respectively] are partially fulfilled through the reading of CS/MA modules within the major.

Students undertaking the B.Sc. and B.Sc. (Hons.) programmes are required to fulfil the remaining 8 MCs of Faculty requirements from any two (2) of the following subject groups: Chemical Sciences, Life Sciences, Physical Sciences and Multidisciplinary & Interdisciplinary Sciences; but not from the following groups: Computing Sciences and Mathematical & Statistical Sciences.