Data science is a data-driven approach to problem solving and scientific exploration that involves the process of collecting, managing, analyzing, explaining, and visualizing data and analysis results. It is inherently interdisciplinary in nature. The aim of the Master of Science in Data Science (MSDS) programme is to provide graduate students with robust training in the field of Data Science. The program is led and mainly run by the School of Computer Science and Engineering (SCSE). Faculty members from several other schools also actively contributes to this programme to support interdisciplinary flavour of data science.
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.
- At least a good relevant bachelor's degree
- Relevant working experience is an advantage
- 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).
Application Period for August intake
Application will open in November
|1. Please click here to submit your application.|
2. Read the Coursework Programmes Admission Guide
3. APPLY NOW. This form may take you 20 minutes to complete.
4. Complete set of supporting documents (must be legible) have to be uploaded via the fully digital application portal. Do not send hardcopy admission package unless requested.
|Pay Application Fee||Application fee payment can be made online before submitting application by VISA/Mastercard. Please ensure that application fee is made to the correct Coursework Application Number (eg C22xxxxx) and programme name.|
|Check Application/Result Status||Owing to the large number of applicants, we are unable to attend to phone or email enquiries on application status/results. You may check your application status 3 working days after you have made successful online payment.|
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.
Applications are to be submitted electronically only. Late submissions would not be accepted and any other form of submission would not be processed.
*Applicants should consider carefully that they are able to cope with work/personal/financial commitments before applying/accepting admission as full-time/part-time candidates.
Deferment of admission base on these reasons may not be considered.
For admissions matters and enquiries, please email directly to [email protected]. Thank you.
Total Graduation Requirement: 30 AUs
| Study Type||Minimum Candidature||Maximum Candidature|
|Part Time Study||2 years (4 semesters)||4 years (8 semesters)|
|Full Time Study||1.5 year (3 semesters)||3 years (6 semesters)|
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
Please note that this MSc programme is a non-MOE subsidised programme.
Non-refundable(Payable when you submit your application)
Deposit Payment(The deposit will be used to offset the semester 1 tuition fees after matriculation)
Non-refundable & non-transferable(Payable upon acceptance of offer of admission)
|5000 (exclusive of GST)|
TUITION FEES FROM AUGUST 2023 INTAKE ONWARDS
|EXCLUSIVE OF GST||INCLUSIVE OF GST|
|Full Programme fees|
|with GST 8% (from 1 Jan 2023)|
|with GST 9% (from 1 Jan 2024)|
Note that the $5000 Acceptance Fee is a part of the total program fee. After the payment of this acceptance fee, it will be deducted from your first semester fee.
- This is a self-financing programme, and is not eligible for Service Obligation or Financial Assistance (i.e. Tuition Fee Loan).
- Tuition fee is payable each semester regardless of the number of courses registered that term.
- Tuition fee is payable even if the student did not register any courses that semester, unless the student has been approved for Leave of Absence (LOA) for that semester by the 2nd week of the academic term.
- Students who apply for LOA or withdraw two or more weeks after the semester commences shall be liable for tuition fees and miscellaneous fees due for the entire term.
- The University reserves the right to revise its fees every year without notice.
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.
Postgraduate AI Scholarship | OCBC Careers (all nationalities may apply)
Please view more information Here.
Frequently Asked Question
For more Frequently Asked Question, please click here.
Email : [email protected]
|2||Programme Director||Assoc Prof Sourav S Bhowmick|
Email: [email protected]