Master of Science (Data Science and Analytics)

The Master of Science in Data Science and Analytics is a multidisciplinary field of study that involves scientific methods, processes, and systems in extracting both explicit and implicit information from a variety of data structures. It combines the knowledge of mathematics and statistics, programming, and data analytics.

Programme Overview

This master programme offers a variety of courses with emphasis on data analytics. Students are free to choose from three different learning modules: Data Computing, Data Analytic, and Finance & Business Analytic to match their interests and career paths. The aim of the programme is to produce knowledgeable, ethical, and competitive graduates who can contribute to the nation.

Programme Structure

Study Duration: Minimum 3 semesters (full-time) / 5 semesters (part-time)
Maximum Duration: 4 semesters (full-time) / 8 semesters (part-time)
Intake: Every October, subjected to the UKM academic calendar
Lectures: All lectures are held during weekdays and office hours, including part-time studies

Modules

Data Computing Module

Semester	Core Course	Elective Course
I	STQD6014 Data Science, STQD6214 Mathematical Statistics with Computing, STQD6414 Data Mining	Choose four: STQD6124 Data Visualization and Communication, STQD6324 Data Management, STQD6114 Unstructured Data Analytics, STQS6444 Time Series Modelling and Forecasting, STQM6154 Network Science, STQD6334 Multicriteria Decision Making, STQD6524 Statistical Methods for Computational Biology
II	STQD6024 Machine Learning, STQP6014 Research Methodology and Industrial Seminar
III	STQD6889 Capstone Project
Total Credit	29	16

Data Analytic Module

Semester	Core Course	Elective Course
I	STQD6014 Data Science, STQD6214 Mathematical Statistics with Computing, STQD6414 Data Mining	Choose four: STQD6124 Data Visualization and Communication, STQS6284 Multivariate Analysis, STQD6114 Unstructured Data Analytics, STQD6134 Business Analytics, STQS6444 Time Series Modelling and Forecasting, STQS6234 Bayesian Inference, STQM6154 Network Science, STQD6334 Multicriteria Decision Making, STQD6524 Statistical Methods for Computational Biology
II	STQD6024 Machine Learning, STQP6014 Research Methodology and Industrial Seminar
III	STQD6889 Capstone Project
Total Credit	29	16

Finance and Business Analytic Module

Semester	Core Course	Elective Course
I	STQD6014 Data Science, STQD6214 Mathematical Statistics with Computing, STQD6414 Data Mining	Choose four: STQD6124 Data Visualization and Communication, STQD6134 Business Analytics, STQD6114 Unstructured Data Analytics, STQS6444 Time Series Modelling and Forecasting, STQD6334 Multicriteria Decision Making, STQA6014 Investment Analysis and Portfolio Management, STQA6034 Issues in Risk Management and Insurance
II	STQD6024 Machine Learning, STQP6014 Research Methodology and Industrial Seminar
III	STQD6889 Capstone Project
Total Credit	29	16

Course Synopsis

STQA6014 Investment Analysis and Portfolio Management: Focuses on investment decision-making, covering various investment instruments, risk management, and portfolio construction.
STQA6034 Issues in Risk Management and Insurance: Provides a broad perspective of risk management, emphasizing traditional risk management and insurance, and equips students with tools for analysis.
STQD6014 Data Science: Exposes students to the basic principles of data science and Python programming, covering big data, algorithms, and data analysis.
STQD6024 Machine Learning: Introduces concepts, techniques, and algorithms in machine learning, including neural networks, decision trees, and support vector machines.
STQD6114 Unstructured Data Analytics: Introduces methods for compiling, summarizing, and analyzing unstructured and semi-structured data, including texts, images, and audios.
STQD6124 Data Visualization and Communication: Covers the principles of data visualization and communication, including designing visualizations, human perception, and effective data storytelling.
STQD6134 Business Analytics: Exposes students to techniques and tools for transforming raw data into meaningful information for business analysis purposes.
STQD6214 Mathematical Statistics with Computing: Covers the fundamentals of mathematical statistics, including descriptive statistics, graphical displays, and hypothesis testing, with an emphasis on the use of R.
STQD6324 Data Management: Provides the fundamentals and state-of-the-art technologies used in data management and big data solutions, including data models, databases, and big data processing.
STQD6334 Multicriteria Decision Making: Introduces concepts and techniques for solving multi-criteria decision-making problems, including decision making without probabilities and with sample information.
STQD6414 Data Mining: Explains the process of exploration in databases (KDD) and data mining, covering data preparation, mining methods, and applications.
STQD6524 Statistical Methods for Computational Biology: Gives exposure to statistical methods and computation in biology and bioinformatics, covering genetic data, gene expression data, and statistical modeling.
STQD6889 Capstone Project: Provides an experiential learning opportunity for students to produce a product that solves real-world problems, executed in collaboration with industry, government, or academics.
STQM6154 Network Science: Introduces mathematical theories in network science, investigating problems through a network approach, and applies to mathematics, social networks, biological systems, and transportation.
STQP6014 Research Methodology and Industrial Seminar: Gives a background and method for performing scientific research in the data science field, covering research ethics, principles, designs, and critical literature review.
STQS6234 Bayesian Inference: Introduces Bayesian theories, including inference for normal distributions and other distributions, hierarchical Bayesian models, and applications.
STQS6284 Multivariate Analysis: Covers statistical methods for multivariate data, including matrix algebra, multivariate normal distribution, hypothesis testing, and techniques like principal component analysis and factor analysis.
STQS6444 Time Series Modelling and Forecasting: Estimates simple regression models, explains techniques for modeling trend and volatility in time series data, and discusses cointegration and error-correction mechanisms.

Entry Requirement

Bachelor's Degree in a relevant field with a minimum CGPA of 2.50 or equivalent from any institution of higher learning recognized by the UKM Senate.
Bachelor's Degree in a relevant field with a minimum CGPA of 2.00 – 2.49 or equivalent, with a minimum of 5 years of working experience or a research project in a relevant field.
Fulfill Accreditation of Prior Experiential Learning (APEL A) for local candidates only, with specific requirements including age, qualifications, and MQA APEL certification.
International students must obtain minimum results in English proficiency tests such as TOEFL, IELTS, PTE, CEFR, MUET, or HEET, unless they come from a country where English is the official language or have obtained academic qualifications from institutions that use English as the medium of instruction.

Career Prospect

Data Scientist
Data Analyst
Statistician
Data Engineer
Machine Learning Engineer

Tuition Fees

Local

Full Time: RM9,155.00 per study (minimum 3 semesters)
- Registration Fee: RM1,010.00 (1st semester only)
- Service & Activity Fee: RM420.00 (every semester)
- Tuition Fee: RM153.00 per credit
Part Time: RM8,770.00 per study (minimum 5 semesters)
- Registration Fee: RM1,085.00 (1st semester only)
- Service & Activity Fee: RM200.00 (every semester)
- Tuition Fee: RM153.00 per credit

International

RM37,620.00 per study (minimum 3 semesters)
- Registration Fee: RM1,410.00 (1st semester only)
- Total fee for the 1st semester: RM13,480.00
- Total fee for subsequent semesters: RM12,070.00

Master of Science (Data Science and Analytics)

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