Master in data science (MDS) Course Structure

1. Introduction

Master   in   Data   Science   (MDS) program   of   Tribhuvan   University   is implemented by the School of Mathematical Sciences (SMS) on a full time basis. MDS program focuses on the   sets of core skills in numerous areas including programming, statistics, data analytics, machine learning, data wrangling, data visualization, communication, business foundations, and ethics—that increase their marketability in the business, industry and multinational companies.

2. Objectives

MDS is an interdisciplinary program and is the first of its kind in the Institute of Science and Technology, Tribhuvan University. After graduation, the students will be able to

      1. Collect, clean, store and query data from a variety of private and public data sources.
      2. Assess, evaluate and respond to decision-making needs and requirements.
      3. Apply appropriate analytic techniques to provide estimates that support decision-making and action.
      4. Communicate actionable information and findings in easy-to-understand written, oral and visual formats.

3. Duration and Nature of Course

Master in Data Science is full time, of 4 Semesters in 2 years in duration. This program basically comprises of some compulsory foundational courses consisting of fundamentals of Mathematics, Statistics, and Computer Science and Information Technology plus some elective courses from a list of courses which may vary from year to year as a multi-exit model decided by the subject committee.

Total Credit: 60

Nature of course: Theory, Practical, Project, Seminar, Intern, Thesis.

4. Evaluation System

      1. 40% internal evaluation and 60% external exam. Internal exams are based on: Attendance/Assignment work / Oral test / Class test / Presentation / Class seminar / Project work/ Term exam End semester exam by School in permission of exam board of TU.
      2. Evaluation of project or thesis: research / project monitoring by supervisor; Pre viva by the school after submission; evaluation of thesis by the Research Committee of the School with consent of the supervisor and the external.
      3. In each of the semester Exam and Internal Assessment, the student must secure at least 50% in order to complete the course.

5. Course Structure

In the First and Second Semester, students must take four compulsory courses in each semester and one course from elective courses (the necessary and relevant to them). In the Third Semester, student must take three compulsory courses and two courses from elective coursesIn the Fourth Semester, students must take two compulsory courses and two courses from elective course.

The Structure of the program is as follows


 Compulsory Courses

Course Code Course Titles Credits Nature
MDS 501 Fundamentals of  Data Science 3 Th.
MDS 502 Data Structure and  Algorithms 3 Th.+ Pr.
MDS 503 Statistical Computing with R 3 Th.+ Pr.
MDS 504 Mathematics for Data Science 3 Th.

 Elective Courses (Any One)  

Course Code Course Titles Credits Nature
MDS 505 Data Base Management Systems 3 Th.+ Pr.
MDS 506 Programming skills with C 3 Th.+ Pr.
MDS 507 Linear and Integer Programming 3 Th.+ Pr.


Compulsory Courses

Course Code Course Titles Credits Nature
MDS 551 Programming with Python 3 Th.+ Pr.
MDS 552 Applied Machine Learning 3 Th.+ Pr.
MDS 553 Statistical Methods for Data Science 3 Th.+ Pr.
MDS 554 Multivariable Calculus for Data Science 3 Th.

                Elective Courses (Any One)

CourseCode Course Titles Credits Th.+ Pr.
MDS 555 Natural Language Processing 3 Th.+ Pr.
MDS 556 Artificial Intelligence 3 Th.+ Pr.
MDS 557 Learning Structure and Time Series 3 Th.+ Pr.


 Compulsory Courses

Course Code Course Titles Credits Nature
MDS 601 Research Methodology 3 Th.
MDS 602 Advanced Data Mining 3 Th.+ Pr.
MDS 603 Techniques for Big Data 3 Th.+ Pr.

 Elective Courses (Any Two)

Course Code Course Titles Credits Nature
MDS 604 Cloud Computing 3 Th.+ Pr.
MDS 605 Regression Analysis 3 Th.+ Pr.
MDS 606 Decision Analysis
Monte Carlo Methods
3 Th.+ Pr.
MDS 607 Cloud Computing 3 Th.


 Compulsory Courses

Course Code Course Titles Credits Nature
MDS 651 Data Visualization 3 Th.
MDS 652 Capstone Project/ Thesis 3 Project+ Report

 Elective Courses (Any Two)

Course Code Course Titles Credits Nature
MDS 653 Social Network Analysis 3 Th.+ Pr.
MDS 654 Actuarial Data Analysis 3 Th.+ Pr.
MDS 655 Deep Learning 3 Th.+ Pr.
MDS 656 Business Analytics 3 Th.+ Pr.
MDS 657 Bioinformatics 3 Th.+ Pr.
MDS 658 Economic Analysis 3 Th.+ Pr.

6. Eligibility

The following is the minimum requirements to be eligible to apply for the MDS program:

      1. A minimum of 15 years’ formal education (12 years of schooling plus three years of graduation).
      1. Must have secured a minimum CGPA of 2.0 or second division or 45% in Bachelor’s level with B Sc CSIT or equivalent, B Math Sc or equivalent,  B Sc (Mathematics) or equivalent, B Sc (Statistics) or equivalent, B Sc/BA with  Mathematics  in the first 2 year, B Sc/BA with Statistics in the first 2 year,  BE or equivalent, BIT or equivalent, BCA or equivalent,  BIM       (with  one  Mathematics  and  one  Statistics)     or equivalent, are eligible to appear in the entrance exam.

7. Data Science Jobs

      • Data scientists possess the technical savvy to unravel complex queries and the creativity to know how to get there. They work to gain insights, and ultimately find purpose in petabytes worth of unorganized, scattered and often disparate data.
      • Data scientists translate big data into innovative ideas. Now big data is no longer a hassle for IT to handle. It is a virtual gold mine of information, just waiting for data scientists to translate into innovative ideas that have implications for commercial and even social change.
      • Data scientists obtain, organize, and manipulate data to gain insights. They also communicate those insights to strategists and decision makers.
      • Data scientists possess a deep understanding of the organizations and industries. They support and know which questions to ask; questions that involve looking into the invisible relationship between disparate data sets.

8. Who is using it?

A successful business relies on quick, agile decisions to stay competitive, and most likely big data analytics is involved in making that business tick. Here is how different types of organizations might use the technology:

      • Government agencies
      • Clinical research centers
      • Banking sector
      • Manufacturing industry
      • Travel and hospitality sector
      • Health care industry
      • Business houses