Master of Science in Data Science (MSDS)

The unique features of the MSDS program are as follows:

• Tight integration of relevant theories and principles from social sciences with computing-driven data science. Given that data is generated by humans or machines, it is paramount to consider social context for any data science problem. Consequently, the core courses focus not only on computing-driven data science courses but also courses and projects that steer students to take a realistic look at a data science problem by considering various social, economics, and behavioural issues. A wide range of elective courses to tackle various data types one may encounter in different application domains along with deeper insights to psychology and economic theories are provided to broaden the knowledge.

  
  

• Comprehensive coverage of the data science ecosystem. In MSDS program, we do not just simply teach analytics. Data analytics techniques are ineffective if your input data is dirty, messy, and not ready for analysis! Our program ensures every student goes through the entire data science ecosystem – from data preparation to data visualization, giving them a comprehensive depth and breadth of data science.

  
  

• The compulsory Capstone Project gives students hands-on opportunities to realize the data science ecosystem for domain-specific applications in industrial settings or academia. The former enables a student to address industry-specific data science problem whereas the latter allows a student opportunity to address such problem for non-industrial end users.

  
  

• Inter-disciplinary nature of MSDS is supported by courses taught by faculty members from different schools. In particular, the core course entitled “Data Science Thinking” will be jointly taught by faculty members from the School of Social Sciences (SSS) and SCSE. Several electives are also taught by faculty members of SSS and the School of Physical and Mathematical Sciences (SPMS). Furthermore, students may take an application domain-specific elective that may be offered by any school (e.g., NBS, SBS, ASE, MAE, EEE, SPMS, SSS, LKC) in NTU (excluding SCSE). Lastly, a capstone project in the MSDS program can be a collaboration between domain-specific industry participants, faculty members from various schools and SCSE.

1. At least a good relevant bachelor's degree
2. Relevant working experience is an advantage
3. For applicants whose native language is not English, TOEFL/IELTS score is to be submitted with the application for admission:

         TOEFL Score (Test dates must be within 2 years or less from the date of application):

          ≥ 600 (paper-based)

          ≥ 250 (computer-based)

          ≥ 100 (internet-based)

IELTS Score (Test date must be within 2 years or less from the date of application):≥ 6.5

GRE is not required for MSc programme (by coursework).

We emphasize that the selection process is competitive. Meeting all prior requirements does not guarantee selection. All applicants will be holistically evaluated academically, relevancy of work experience and other relevant experiences.  

For admissions matters and enquiries, please email directly to [email protected].  Thank you.

Total Graduation Requirement: 30 AUs 

Except for a bridging course, each course is a 3 AU course with 39 teaching hours, including lectures, tutorials, example classes and labs over 13 weeks. The capstone project is a one-year project with 6 AU. The bridging course, Python Programming, with zero AU is for students graduated from bachelor programme with limited programming training and working adults who do not usually write codes. All students can take the bridging course in their first semester. It is not a compulsory course.

View the Course Content  Here

*Note: the curriculum is subject to change.

Top 5 Careers in Data Science

• Data Scientist
• Data Analyst
• Business Intelligence Developer
• Applications Architect
• Statistician

Programme Fees

Please note that this MSc programme is a non-MOE subsidised programme.

For Miscellaneous Fees, please click here

Notes on payment of fees:

Fees are subject to annual revision.

Students will be billed at the commencement of each semester, and payment due date is 2 weeks after billing date. A student who withdraws or leaves the University two or more weeks after the commencement of his candidature or the commencement of the semester is liable to pay the fees due for the semester.

# If you are a Singaporean student, you may use your SkillsFuture credits towards tuition fees. The claim submission has to be completed 60 days before the start date of the next Semester (e.g. You must submit before November to claim towards Semester 2 tuition fees). In your claim, indicate the course start date to be first day of the upcoming Semester in the Academic Calendar. e.g. 14 August 2023

GRANTS

NTU Alumni Grant
NTU alumni who completed an undergraduate or post-graduate degree programme with NTU will receive a one-time grant of 10% off the tuition fees (before GST). This grant will be applied on the final payment of the course and it can only be used to offset the tuition fees and no other fees related to the course of study.

International Students

Please view more information Here.

Frequently Asked Question

For more Frequently Asked Question, please click  here. 

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