Overview | Information for Prospective Students | Information for Current Students

Master of Science in Data Science and Machine Learning

  • Programme

  • Programme Structure

    Students admitted to the Master of Science in Data Science and Machine Learning programme are required to pass 40 units based on the approved course list.


    Duration

    The candidature period for full-time students is 1 to 2 years, while the candidature period for part-time students is 2 to 4 years.


    Graduation and Continuation Requirements

    The programme uses the Grade Point Average (GPA) as a criterion for continuation and graduation. GPA is computed based on all courses read, be it pass or fail.


    In order to continue in the programme, a student must not have a:

      • GPA below 3.00 (but ≥2.50) for three consecutive semesters; or
      • GPA below 2.50 for two consecutive semesters.

    In order to graduate from the programme, students are required to:

      • Read and pass five core courses and five elective courses that are in their respective approved course list.
      • Obtained a minimum GPA of 3.00 or, equivalently, an average grade of at least B-.

    For more details, please refer to the Office of University Registrar website here.

  • Course List

  • The following Course List applies to students enrolled from the AY2025 intake onwards.

    For earlier intakes, please refer to this Course List here: 


    Core Courses — Students must complete all 5 Courses. (Total: 20 Units)
    Course Code Course Title Units Remarks
    DSA5101 Introduction to Big Data for Industry 4
    DSA5103 Optimization Algorithms for Data Modelling 4
    DSA5104 Principles of Data Management and Retrieval 4
    DSA5105 Principles of Machine Learning 4
    DSA5106 Deep Learning: Foundations and Techniques 4
    Elective Courses — Students must complete 5 Courses from the following list. (Total: 20 Units)
    Courses offered by Department of Mathematics
    DSA5201 DSML Industry Consulting and Applications Project 4
    DSA5202 Advanced Topics in Machine Learning (TGS-2024050005) 4 SSG-subsidised
    DSA5203 Visual Data Processing and Interpretation (TGS-2020502798) 4 SSG-subsidised
    DSA5205 Data Science in Quantitative Finance 4
    DSA5206 Advanced Topics in Data Science 4
    DSA5207 Text Processing & Interpretation with Machine Learning 4
    DSA5208 Scalable Distributed Computing for Data Science 4
    DSA5209 Stochastic Methods and Inferences for Big Data 4
    DSA5210 AI for Mathematics 4
    MA4230 Matrix Computation 4
    MA5232 Modelling and Numerical Simulations 4
    MA5270 Game Theory and Applications 4
    QF5204 Numerical Methods in Quantitative Finance 4
    Courses offered by other departments
    CS4248 Natural Language Processing (TGS-2021008669) 4 SSG-subsidised
    CS5224 Cloud Computing 4
    CS5228 Knowledge Discovery and Data Mining 4
    CS5344 Big-Data Analytics Technology (TGS-2021008668) 4 SSG-subsidised
    ST5201 Statistical Foundations of Data Science 4
    ST5202 Applied Regression Analysis 4
    ST5225 Statistical Analysis of Networks (TGS-2020507587) 4 SSG-subsidised

    #SSG funding for courses is limited in duration and subject to availability.


    NOTE:
    a) Enrollment in the courses from other departments will be subjected to the specific quotas from the corresponding department.
    b) As the course list could be updated from time to time, please check back on a regular basis to ensure that you are referring to the most updated version.

  • Fees

  • Tuition Fee for Local Students (Singapore Citizens / PRs)


    Tuition Fee for International Students


      Miscellaneous student fees

      • This is compulsory and payable every semester, for which information can be found on the NUS Office of University Registar’s website.

      ‎

      Payment of Fees

    • Leave of Absence

    • Leave of Absence (LOA)

      • Application for Leave of Absence are to be submitted via the online Leave of Absence system at NUS Education Records System if they have to be away during their candidature or are unable to read any courses in a semester.
      • Application for a semester-long Leave of Absence must be submitted by Instructional Week 1 of the semester.  Otherwise, a student will be liable to pay fees for the entire semester.
      • Leave may be granted for up to one year only and the period of absence will not be counted towards the maximum period of candidature.
      • A student who is absent from the programme for more than one year will have to withdraw from the programme.
      • Students who are absent without leave are subject to disciplinary actions.

    • Course Registration

    • Course Registration

      • Students have to register courses on the CourseReg@EduRec system by following the course registration procedure and schedule by the Office of University Registrar.
      • Students will bear the consequences of their decisions in adding/dropping courses, which may result in a delay in their graduation if not planned properly.
      • As not all the courses are offered in the same semester or in the same format every year. Students are responsible in ensuring that they do not register for courses with any timetable clashes, and are able to attend classes and sit for examinations as scheduled.
      • Students may refer to NUSMODS for the course and exam timetables.
      • A full-time student may read up to five courses per regular semester, whereas a part-time student may read up to three courses per regular semester.
    • Graduate Student Life

    • Useful Information on Graduate Student Life

    Programme Structure

    Students admitted to the Master of Science in Data Science and Machine Learning programme are required to pass 40 units based on the approved course list.


    Duration

    The candidature period for full-time students is 1 to 2 years, while the candidature period for part-time students is 2 to 4 years.


    Graduation and Continuation Requirements

    The programme uses the Grade Point Average (GPA) as a criterion for continuation and graduation. GPA is computed based on all courses read, be it pass or fail.


    In order to continue in the programme, a student must not have a:

      • GPA below 3.00 (but ≥2.50) for three consecutive semesters; or
      • GPA below 2.50 for two consecutive semesters.

    In order to graduate from the programme, students are required to:

      • Read and pass five core courses and five elective courses that are in their respective approved course list.
      • Obtained a minimum GPA of 3.00 or, equivalently, an average grade of at least B-.

    For more details, please refer to the Office of University Registrar website here.

    The following Course List applies to students enrolled from the AY2025 intake onwards.

    For earlier intakes, please refer to this Course List here: 


    Core Courses — Students must complete all 5 Courses. (Total: 20 Units)
    Course Code Course Title Units Remarks
    DSA5101 Introduction to Big Data for Industry 4
    DSA5103 Optimization Algorithms for Data Modelling 4
    DSA5104 Principles of Data Management and Retrieval 4
    DSA5105 Principles of Machine Learning 4
    DSA5106 Deep Learning: Foundations and Techniques 4
    Elective Courses — Students must complete 5 Courses from the following list. (Total: 20 Units)
    Courses offered by Department of Mathematics
    DSA5201 DSML Industry Consulting and Applications Project 4
    DSA5202 Advanced Topics in Machine Learning (TGS-2024050005) 4 SSG-subsidised
    DSA5203 Visual Data Processing and Interpretation (TGS-2020502798) 4 SSG-subsidised
    DSA5205 Data Science in Quantitative Finance 4
    DSA5206 Advanced Topics in Data Science 4
    DSA5207 Text Processing & Interpretation with Machine Learning 4
    DSA5208 Scalable Distributed Computing for Data Science 4
    DSA5209 Stochastic Methods and Inferences for Big Data 4
    DSA5210 AI for Mathematics 4
    MA4230 Matrix Computation 4
    MA5232 Modelling and Numerical Simulations 4
    MA5270 Game Theory and Applications 4
    QF5204 Numerical Methods in Quantitative Finance 4
    Courses offered by other departments
    CS4248 Natural Language Processing (TGS-2021008669) 4 SSG-subsidised
    CS5224 Cloud Computing 4
    CS5228 Knowledge Discovery and Data Mining 4
    CS5344 Big-Data Analytics Technology (TGS-2021008668) 4 SSG-subsidised
    ST5201 Statistical Foundations of Data Science 4
    ST5202 Applied Regression Analysis 4
    ST5225 Statistical Analysis of Networks (TGS-2020507587) 4 SSG-subsidised

    #SSG funding for courses is limited in duration and subject to availability.


    NOTE:
    a) Enrollment in the courses from other departments will be subjected to the specific quotas from the corresponding department.
    b) As the course list could be updated from time to time, please check back on a regular basis to ensure that you are referring to the most updated version.

    Tuition Fee for Local Students (Singapore Citizens / PRs)


    Tuition Fee for International Students


      Miscellaneous student fees

      • This is compulsory and payable every semester, for which information can be found on the NUS Office of University Registar’s website.

      ‎

      Payment of Fees

      Leave of Absence (LOA)

      • Application for Leave of Absence are to be submitted via the online Leave of Absence system at NUS Education Records System if they have to be away during their candidature or are unable to read any courses in a semester.
      • Application for a semester-long Leave of Absence must be submitted by Instructional Week 1 of the semester.  Otherwise, a student will be liable to pay fees for the entire semester.
      • Leave may be granted for up to one year only and the period of absence will not be counted towards the maximum period of candidature.
      • A student who is absent from the programme for more than one year will have to withdraw from the programme.
      • Students who are absent without leave are subject to disciplinary actions.

      Course Registration

      • Students have to register courses on the CourseReg@EduRec system by following the course registration procedure and schedule by the Office of University Registrar.
      • Students will bear the consequences of their decisions in adding/dropping courses, which may result in a delay in their graduation if not planned properly.
      • As not all the courses are offered in the same semester or in the same format every year. Students are responsible in ensuring that they do not register for courses with any timetable clashes, and are able to attend classes and sit for examinations as scheduled.
      • Students may refer to NUSMODS for the course and exam timetables.
      • A full-time student may read up to five courses per regular semester, whereas a part-time student may read up to three courses per regular semester.

      Useful Information on Graduate Student Life

      Resources

      DSML Course List

      Course List for current DSML Students

      DSA5201

      Information on DSA5201 DSML Industry Consulting and Applications Project

      Academic Calendar

      Schedule of academic events

      NUSMODS

      NUS course catalogue and timetable

      Examination

      Examination timetable and important deadlines on examination-related matters

      Education Record System (myEduRec)

      Student administrative system to perform key activities of a student academic life cycle

      Canvas

      Learning management system containing online course learning materials

      MyPortal@NUS (FOS)

      Online portal for NUS students under the Faculty of Science relating to administrative matters, circulars, annoucements, etc.

      Graduation

      Conferment

      Actions and information pertaining to conferment, i.e. graduation.

      Degree Scroll

      Collection of degree scroll

      Transcript

      Collection of transcript

      Commencement

      Information on graduation ceremony