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Master in Data Science

The Master in Data Science (MDS) is a cutting-edge program designed to meet the growing demand for skilled data professionals across various industries. In an era where data-driven decision-making is crucial in sectors such as technology, healthcare, finance, and manufacturing, this program provides the expertise needed to harness the power of data. Our MDS program offers flexible online learning, dual certification with a Google Data Analytics Professional Certificate, and specialized tracks in Digital Business Analytics and Smart Manufacturing Analytics, making it your gateway to a world of opportunities in the data science field.

Why Study with Us

Our Master in Data Science program not only equips you with advanced data science skills but also empowers you to excel in your career. You’ll gain practical knowledge and strategies that are highly valued in industries like technology, healthcare, finance, and manufacturing. Our focus on ethical values and innovation ensures you are prepared to make a positive impact. The Master in Data Science is more than just a qualification; it’s your gateway to many opportunities. Whether you aim to pursue a PhD or embark on a rewarding career in data science, this program will set you on the path to sucess. Your journey starts here, where data science meets endless possibilities.

Our Advantage

Flexible Online Learning

Study from anywhere in the world with our 100% online program. Fit your studies around your schedule, whether you’re a full-time professional or a recent graduate.

Dual Certification

Earn both a Master’s degree from WOU and a Google Data Analytics Professional Certificate. This unique combination enhances your resume and boosts your job prospects.

Specialized Tracks

Tailor your education with elective tracks in Digital Business Analytics and Smart Manufacturing Analytics. Gain in-depth expertise in areas that align with your career goals and industry demands.

Master In Data Science Course Essentials Quick Facts

Master In Data Science Course Overview

All the facts and figures about your study programme at a glance

Study Model

Online ODL

1 to 3 Years – Full Time
2 to 4 Years – Part Time
Online ODL

MASTER IN DATA SCIENCE

40 Credits

Language

English

Accreditation & Recognition

Accreditation

Malaysian Qualifications Agency (MQA)
(MQA/PA 17350)

Fees & Scholarships*

Tuition Fees

RM22,000

Scholarships Available

Study Duration

Study Start

Jan, May & Sept Terms

Duration

1 to 3 Years – Full Time
2 to 4 Years – Part Time

Apply Now

Top Core Courses

Get a head start on your career with our Master in Data Science. Here are some core courses designed to equip you with the most relevant and up-to-date skill set:
Data Science Techniques and Concepts

Gain a comprehensive understanding of the fundamental techniques and concepts in data science. This course covers key methodologies, tools, and best practices essential for analyzing and interpreting data effectively.

Machine Learning for Data Science

Explore the world of machine learning and its applications in data science. Learn about various algorithms, model building, and evaluation techniques to solve complex data problems.

Predictive Analytics and Forecasting in Data Science

Develop skills in predictive analytics and forecasting to make informed decisions. This course focuses on statistical models and machine learning techniques used to predict future trends and outcomes.

Data Wrangling and Preprocessing

Master the crucial steps of data wrangling and preprocessing. Learn how to clean, transform, and prepare data for analysis, ensuring high-quality and reliable datasets for your projects.

Deep Learning and Neural Networks

Dive into advanced topics in deep learning and neural networks. Understand the architecture, algorithms, and applications of deep learning models, and how they are used to solve complex problems in data science.

Your Learning Pathway: From Basics to Mastery

Online (ODL) Full-Time

Year 1

In the first year, students will build a strong foundation in data science, beginning with essential research methodologies and core concepts. They will gain practical programming skills specific to data science, and learn to handle and preprocess data efficiently. Courses on applied statistics and machine learning will equip them with the necessary analytical skills to derive insights from data and develop predictive models. This year lays the groundwork for advanced data science techniques and prepares students for more specialized study in their second year.

Year 2

In the second year, students will advance their knowledge with courses on intelligent data visualization, enabling them to effectively communicate their data findings. They will choose elective courses to specialize in areas of interest, such as digital business analytics or smart manufacturing analytics, tailoring their education to their career goals. The year culminates with two capstone projects, allowing students to apply their learned skills to real-world problems, integrating their knowledge and demonstrating their competency in data science. This comprehensive approach ensures they are well-prepared for professional roles in various industries.

Study Content

Core courses
No. Course Code Course Title Credit Hours
1 DDS501/03 Research Methodology for Data Science 3
2 DDS502/03 Data Science Techniques and Concepts 3
3 DDS503/03 Programming for Data Science 3
4 DDS504/03 Data Wrangling and Preprocessing 3
5 DDS505/03 Applied Statistics in Data Science 3
6 DDS506/03 Machine Learning for Data Science 3
7 DDS507/03 Intelligent Data Visualization 3
8 Total Credits 21
Elective Courses ( Choose Three Courses )
No. Course Code Course Title Credit Hours
T1: Digital Business Analytics  
1 DDS510/03 Predictive Analytics and Forecasting in Data Science 3
2 DDS511/03 Deep Learning and Neural Networks 3
3 DDS512/03 Consumer Behavioural and Social Media Analytics 3
T2: Smart Manufacturing Analytics  
4 TEL504/03 Internet of Things 3
5 TME501/03 Manufacturing Data Analytics 3
6 TMM526/03 Advanced Manufacturing & Technology 3
Master Project Report
1 DDS508/05 Data Science Capstone Project 1 5
2 DDS509/05 Data Science Capstone Project 2 5

 

IMPORTANT NOTES

1.Students must complete 40 credit hours with a minimum CGPA of 3.0 in order to graduate.

2.Students must complete passed minimum 3 core courses before enrolling in DDS508/05

Admission

Entry Requirements
  • A Bachelor’s degree (Level 6, Malaysian Qualifications Framework (MQF)) in Computing or related fields with a minimum CGPA of 2.50, as accepted by the Higher Education Provider (HEP) Senate; OR
  • A Bachelor’s degree (Level 6, Malaysian Qualifications Framework (MQF)) in Computing or related fields with a minimum Cumulative Grade Point Average (CGPA) of 2.00 and not meeting a Cumulative Grade Point Average (CGPA) of 2.50 can be accepted subject to a thorough rigorous assessment as determined by the Higher Education Provider (HEP); OR
  • A Bachelor’s degree (Level 6, Malaysian Qualifications Framework (MQF)) in Non-Computing field with a minimum Cumulative Grade Point Average (CGPA) of 2.00 can be accepted subject to a thorough rigorous assessment as determined by the HEP to identify the appropriate prerequisite courses that equivalent to their working experience in Computing or related fields; OR
  • A Bachelor’s degree (Level 6, Malaysian Qualifications Framework (MQF)) in a non-computing field with a minimum Cumulative Grade Point Average (CGPA) of 2.00 can be accepted, subject to the completion of appropriate prerequisite courses: Fundamentals of Programming and Database Fundamentals; OR
  • Other qualifications equivalent to a Bachelor’s degree (Level 6, Malaysian Qualifications Framework (MQF)) in Computing or related fields recognised by the Government of Malaysia must fulfil the requirement on item i or ii. OR
  • Through APEL (A) where the candidate needs to be a Malaysian citizen, should be at least 30 years of age in the year of application and possess relevant work experience. The candidate must have at least an STPM or diploma qualification and pass the APEL evaluation assessment.

English Competency Requirement (For international students)

  • Achieve a minimum of Band 4 in MUET or equivalent to CEFR (Low B2).
APEL Entry: Turning Experience into Credentials

Accreditation of Prior Experimental Learning (APEL) provides an opportunity to individuals with working experience but lack of formal academic, qualifications to pursue their tertiary studies. APEL involves the identification, documentation and assessment of prior experiential learning to determine the extent to which an individual has achieved the desired learning outcomes, for access to a programme of study.

Admission criteria: 35 years old Bachelor’s degree in relevant field/equivalent 5 years’ work experience Passed APEL assessment

EL Requirement for International Students

  • MUET Band 4, or
  • CEFR B2, or
  • IELTS 6.0, or
  • Is a Native speaker, or graduated from an English-medium Higher Education. Institution
Scholarships & Financial Aid

For details and inquiries on Scholarships & Financial Aid, you can find out more here.

Future Career Paths with a MDS Degree

Graduates of the Master in Data Science program are equipped with the skills and knowledge to pursue various high-demand roles in the data science field.

Here are the top five career paths:

5
Data Scientist
5
Data Analyst
5
Machine Learning Engineer
5
Business Intelligence Analyst
5
Data Engineer

Why WOU is The Best Online University of Choice

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Your Studies are Flexible
and 100% Online

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The Ideal Mix of Theory
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Our Success Stories

Discover the impact we've had, straight from our campus family.

Saw Chee Hua

Bachelor of Software Engineering

Thanks to WOU’s Apprenticeship programme, I now have an enriching career at Sophic Automation even before I graduate. I was exposed to a dynamic and innovative work environment that nurtured both my personal and professional growth. This help me fast track my career progression compared to my peers in other institution.

Soo Wei Xi

Bachelor of Software Engineering

I’ve got to give a huge shout-out to the Bachelor of Degree in Software Engineering (BDSE) programme at Wawasan Open University. It seriously paved the way for my gig at ViTrox. Dr. Andrew and our amazing lecturers, they were like my guiding lights. They didn’t just teach, they were mentors, offering top-notch education, advice, and support. Thanks to their know-how and the well-rounded BDSE program gave me the skills and smarts to kickstart my software engineering career. These educators went above and beyond, and I owe them big time for making my journey to ViTrox a reality.

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Meet Our Faculty Members

Our faculty members blend academic excellence with rich industry experience, ensuring your education is both rigorous and practically relevant. Committed to your success, they equip you with the insights and skills essential for navigating the dynamic landscape of digital technology and engineering.

Associate Professor Ts Dr Tan Kian Lam (Andrew)

Associate Professor Ts. Dr. Tan Kian Lam (Andrew)

Head of School of Digital Technology

Dr Lee Heng Wei

Dr. Lee Heng Wei

Head of School of Digital Technology

Dr-Lau-Pei-Mey-image

Dr. Lau Pei Mey

School of Digital Technology, Programme Lead

Dr Jayaeswari Sangaralingam

Dr. Jayaeswari Sangaralingam

School of Digital Technology, Senior Lecturer

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