Analytics @ MAS

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MSc in Analytics Programme

The Master of Science (MSc) in Analytics programme is an interdisciplinary post-graduate programme suitable for professionals wanting to leverage analytics in their respective fields, as well as recent college graduates planning to pursue a career in the data science industry. The programme provides students with the skills and knowledge to apply cutting-edge data science techniques to solve critical business challenges. It is designed to provide exposure to innovative thinking and problem solving skills that are invaluable in the modern economy.

Programme Structure

The MSc in Analytics programme is an intensive one-year full-time or two-year part-time programme by coursework, taught in three semesters. Upon completion of study, students are awarded the Master of Science (MSc) in Analytics degree.

The programme consists of a total of 30 Academic Units (AU), with 24 AU stemming from core courses and 6 AU from elective courses:

  • 11 Core Subjects
  • 9 Elective Modules to Choose From
  • 1 Analytics Practicum Module (Internship)
  • Ad-hoc Seminars on Analytics Software Tools
  • Ad-hoc Seminars from Industry Professionals

The curriculum strongly emphasizes the application of analytics on real-world data, and includes two practicum modules of 6 AU each (under both the core and elective modules). Sample partner organizations for the practicum module include APL, BreadTalk, Charles & Keith, DHL, Experian, Grab, Johnson & Johnson, JR Group, Lenovo, MSD, PSA, and PwC.

Academic Timeline

Trimester 1 1st Half 22 July 2019 – 1 Sep 2019 (6 weeks)

Recess: 2 Sep 2019 – 8 Sep 2019 (1 week)
2nd Half 9 Sep 2019 – 27 Oct 2018 (7 weeks)

Recess: 28 Oct 2019 – 3 Nov 2019 (1 week)
Trimester 2 1st Half 4 Nov 2019 – 15 Dec 2019 (6 weeks)

Recess: 16 Dec 2019 – 29 Dec 2019 (2 weeks)
2nd Half 30 Dec 2020 – 16 Feb 2020 (7 weeks)

Recess: 17 Feb 2020 – 23 Feb 2020 (1 week)
Trimester 3 1st Half 24 Feb 2020 – 5 Apr 2020 (6 weeks)

Recess: 6 Apr 2020 – 12 Apr 2020 (1 week)
2nd Half 13 Apr 2020 – 31 May 2020 (7 weeks)

Recess: 1 Jun 2020 – 26 Jul 2020 (8 weeks)

Compulsory Courses

MH8101 Operations Research I (1.5AU)
This course introduces a number of optimization methods commonly used in operations research. Topics covered include linear programming, nonlinear optimization, discrete optimization, dynamic programming, and heuristics.
MH8102 Operations Research II (1.5AU)
This course is a continuation of MH8101 Operations Research I. Topics covered include Monte-Carlo simulation, queuing theory, discrete event simulation, stochastic programming, dynamic programming and optimal control, and inventory theory.
MH8111 Analytics software I (1.5AU)
In this course, we introduce state of the art software packages such as SAS, R, IBM Business Analytics to teach students data analysis, data mining, predictive modelling, data visualization, decision optimization, and report generation. In this course, we cover topics including Python, Cplex, R, Matlab, and SAS.
M8112 Analytics Software II (1.5AU)
In this course, we introduce state of the art software packages such as SAS, R, IBM Business Analytics to teach students data analysis, data mining, predictive modelling, data visualization, decision optimization, and report generation. In this course, we cover topics including weka, libsvm, IBM Business Analytics, Matlab, SAS, Rapid Miner and Cplex.
MH8121 Analytics Workshop I (1.5AU)
This course provides opportunities for students to learn cutting-edge technologies in data analytics, through interactive workshops. During workshops, the instructor will brief each topic and summarize the state of the art. Students will form groups, to conduct deep survey and present the findings to the class.
MH8122 Analytics Workshop II (1.5AU)
This course provides opportunities for students to learn cutting-edge technologies in data analytics, through interactive workshops. During workshops, the instructor will brief each topic and summarize the state of the art. Students will form groups, to conduct deep survey and present the findings to the class.
MH8131 Probability and Statistics (1.5AU)
The probability and statistics course provides a systematic approach to understanding uncertainties. Topics covered include probability, conditional probability; random variables, joint distributions, conditional distributions and independence; probability laws, multivariate normal distribution; order statistics; convergence concepts, the law of large numbers, central limit theorem; estimation, Bayes estimators, interval estimation including confidence intervals, prediction intervals, Bayesian interval estimation; hypothesis testing, likelihood ratio tests; Bayesian tests; nonparametric methods, bootstrap.
MH8141 Time Series Analysis (1.5AU)
Many of the business systems are dynamic systems in which their states change over time. This course introduces time series models and associated methods of data analysis and inference. Topics include auto regressive (AR), moving average (MA), ARMA, and ARIMA processes, stationary and non-stationary processes, seasonal processes, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, and spectral analysis. Real-world applications for understanding characteristics of time series data in economics, finance, management and industries, and modelling and evaluating forecasts upon which decision-making would depend are emphasized with lab using SAS.
MH6142 Database Systems (3AU)
This course covers basic and advanced topics in database management systems. The first part introduces the foundation and practices in database design, including conceptual modelling, SQL, relational algebra and calculus, functional dependency and normalization. The second part covers the implementation of a database system, including indexing, query processing and optimization and transactions. Finally, a few advanced topics such as XML database, trajectory database and big data will be covered.
MH6151 Data Mining (3AU)
Data mining is the process of knowledge discovery. Topics taught include data preparation (data cleaning, outlier analysis and transformation) and statistical techniques (regression modelling, multivariate statistics, and statistical inference). Supervised and unsupervised learning techniques including decision tree induction, nearest neighbour categorisation, cluster analysis, association analysis, support vector machines, Bayesian learning and neural networks are touched upon. As well, data mining software and tools, and applications of data mining to complex data types are covered.
MH6191 Practicum I (6AU)
Professional consulting project mentored by experienced instructors to solve problems that are of great importance to the sponsoring companies. Practicum I is a compulsory course.

Elective Courses

MH6301 Information Retrieval and Analysis (3AU)
This course focuses on issues, data structures and algorithms on representation, storage, and access to very large digital document collections. Information retrieval models (including Boolean, vector space and probabilistic models), indexing and retrieval techniques, evaluation of information retrieval systems, text and web mining (content, structure and usage mining), web search (search engines, spiders, link analysis, agents), recommender systems and intelligent information retrieval, information extraction and integration are covered in this course.
MH8311 Stochastic Modelling (1.5AU)
Stochastic Processes. Gaussian and Markovian Processes. Markov Chains, Markov Decision Processes. Poisson Processes. Continuous-Time Markov Chains. Stochastic Modelling Applications.
MH8321 Survival Data Analysis (1.5AU)
Survival analysis includes a cluster of techniques primarily developed in the biomedical sciences, but are also widely used in social sciences like economics, and in engineering. This course focuses on the statistical methods related to the analysis of survival or time to event data, introduces hazard & survival functions, censoring mechanisms, parametric and non-parametric estimation, and comparison of survival curves. The course emphasizes basic concepts and techniques as well as practical applications relevant to business, social sciences and life sciences.
MH8322 Uncertainty and Dependence (1.5AU)
Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are some of the topics covered in this course.
MH8331 Financial and Risk Analytics I (1.5AU)
Techniques for measuring and managing the risk of trading and investment positions for positions in equities, credit, interest rates, foreign exchange, commodities, vanilla options, and exotic options; risk sensitivity reports, design of static and dynamic hedges, measure value-at-risk and stress tests; Monte Carlo simulations determining hedge effectiveness; case studies.
MH8332 Financial and Risk Analytics II (1.5AU)
Techniques for measuring and managing the risk of trading and investment positions for positions in equities, credit, interest rates, foreign exchange, commodities, vanilla options, and exotic options; risk sensitivity reports, design of static and dynamic hedges, measure value-at-risk and stress tests; Monte Carlo simulations determining hedge effectiveness; case studies.
MH8341 Data Management and Business Intelligence (1.5AU)
This course explores management, organizational, and technological issues in terms of the ways data are stored, managed and applied in businesses. Using a simulated business, the database module covers data concepts, structures, conceptual and physical design techniques, data administration and data mining. Theory and practice of database management systems are integrated through hands-on experience with the design and implementation of a business solution. By the end of the course, participants will gain critical IT skills in analysing business processes, improving these processes, developing business applications with an industry standard database and use data for business requirements.
MH8351 Web Analytics (1.5AU)
Topics covered include structure of the web, random graph models of networks, link analysis and web search, network dynamics, network effects, power law phenomena, the small-world phenomenon, and diffusion through networks.
MH6391 Practicum II (6AU)
Professional internship with an analytics project. Mentored by experienced instructors to solve problems that are of great importance to the sponsoring companies.

Practicum II is an elective course.

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