Master in data science (MDS) Course Structure

1. Introduction

The Modern Computerized World demands the human resource having all three – analytical ability, data processing capability and fast computing efficiency, i.e., the combined knowledge of Mathematics, Statistics, and Computer Science and Information Technology. Tribhuvan University has taken up this as a challenge and has decided to run Bachelor’s and Master’s Degree Program in Mathematical Sciences that will help produce at least a critical mass of experts with sound knowledge of fundamentals of Mathematics, Statistical and Analytical capability and fluent computational skills. To run these programs, Tribhuvan University established School of Mathematical Sciences under Institute of Science and Technology in 2016 at Kirtipur as its autonomous body.

Computational simulations are everywhere and the amount of data available for many enterprises is increasing exponentially. The internet makes these large quantities of data readily available for many enterprises. Many areas of science, engineering, and industry are now concerned with building and evaluating mathematical models, exploring them computationally, and analyzing enormous amounts of observed and computed data. These activities are all inherently mathematical in nature. Thus, Master’s Program in Data Science is an ideal program to start at SMS TU.

2. Objectives

This interdisciplinary program is the first of its kind in the country. After graduation, the students will be able to

  • Collect, clean, store and query data from a variety of private and public data sources.
  • Assess, evaluate and respond to decision-making needs and requirements.
  • Apply appropriate analytic techniques to provide estimates that support decision-making and action.
  • 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 CodeCourse TitlesCreditsNature
MDS 501Fundamentals of  Data Science3Th.
MDS 502Data Structure and  Algorithms3Th.+ Pr.
MDS 503Statistical Computing with R3Th.+ Pr.
MDS 504Mathematics for Data Science3Th.

 Elective Courses (Any One)  

Course CodeCourse TitlesCreditsNature
MDS 505Data Base Management Systems3Th.+ Pr.
MDS 506Programming skills with C3Th.+ Pr.
MDS 507Linear and Integer Programming3Th.+ Pr.


Compulsory Courses

Course CodeCourse TitlesCreditsNature
MDS 551Programming with Python3Th.+ Pr.
MDS 552Applied Machine Learning3Th.+ Pr.
MDS 553Statistical Methods for Data Science3Th.+ Pr.
MDS 554Multivariable Calculus for Data Science3Th.

                Elective Courses (Any One)

CourseCodeCourse TitlesCreditsTh.+ Pr.
MDS 555Natural Language Processing3Th.+ Pr.
MDS 556Artificial Intelligence3Th.+ Pr.
MDS 557Learning Structure and Time Series3Th.+ Pr.


 Compulsory Courses

Course CodeCourse TitlesCreditsNature
MDS 601Research Methodology3Th.
MDS 602Advanced Data Mining3Th.+ Pr.
MDS 603Techniques for Big Data3Th.+ Pr.

 Elective Courses (Any Two)

Course CodeCourse TitlesCreditsNature
MDS 604Cloud Computing3Th.+ Pr.
MDS 605Regression Analysis3Th.+ Pr.
MDS 606Decision Analysis
Monte Carlo Methods
3Th.+ Pr.
MDS 607Cloud Computing3Th.


 Compulsory Courses

Course CodeCourse TitlesCreditsNature
MDS 651Data Visualization3Th.
MDS 652Capstone Project/ Thesis3Project+ Report

 Elective Courses (Any Two)

Course CodeCourse TitlesCreditsNature
MDS 653Social Network Analysis3Th.+ Pr.
MDS 654Actuarial Data Analysis3Th.+ Pr.
MDS 655Deep Learning3Th.+ Pr.
MDS 656Business Analytics3Th.+ Pr.
MDS 657Bioinformatics3Th.+ Pr.
MDS 658Economic Analysis3Th.+ Pr.

6. Eligibility

Students applying to the program are expected to have a Bachelor’s Degree with a strong quantitative and computational background including coursework in calculus, linear algebra and introductory statistics. So students with B Sc CSIT, B Math Sc, B Sc. (Math), B Sc (Stat), B Sc/BA with  Math / Stat in the first 2 years, BE, BIT, BCA (with two Math and one Stat).

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