Overview
Master in Data Science (MDS) - School of Mathematical Sciences, Tribhuvan University (SMSTU)
Overview
The modern computerized world demands human resources with analytical ability, data processing capability, and fast computing efficiency. This requires combined knowledge in Mathematics, Statistics, and Computer Science. Tribhuvan University (TU) has taken up this challenge by offering Bachelor's and Master's Degree Programs in Mathematical Sciences. The School of Mathematical Sciences (SMSTU) was established in 2016 under the Institute of Science and Technology at Kirtipur as an autonomous body to run these programs and produce experts with sound fundamentals in Mathematics, Statistical and Analytical capability, and computational skills.
Objectives
This interdisciplinary program is the first of its kind in Nepal. Graduates will be able to:
- Collect, clean, store, and query data from various private and public sources.
- Assess, evaluate, and respond to decision-making needs and requirements.
- Apply appropriate analytic techniques to support decision-making and action.
- Communicate actionable information and findings in written, oral, and visual formats.
Duration and Nature of Course
The Master in Data Science is a full-time program spanning four semesters over two years. The curriculum comprises compulsory foundational courses in Mathematics, Statistics, Computer Science, and Information Technology, along with elective courses that may vary each year. The program includes theory, practical sessions, projects, seminars, internships, and a thesis. The total credit requirement is 60.
Eligibility
Applicants must have a Bachelor’s Degree with a solid quantitative and computational background, including coursework in calculus, linear algebra, and introductory statistics. Eligible degrees include B.Sc. CSIT, B.Math.Sc, B.Sc. (Math), B.Sc. (Stat), B.Sc./BA with Math/Stat in the first two years, BE, BIT, and BCA (with two Math and one Stat courses).
Entrance Examination
- The entrance exam has a weightage of 100 marks and lasts for 2 hours.
- The questions are based on the B.Sc. syllabus prescribed by the committee and are multiple-choice.
- Each question carries one mark, and a minimum of 35% is required to pass.
Career Prospects
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 find purpose in vast amounts of unorganized data. Data scientists translate big data into innovative ideas, organize and manipulate data to gain insights, and communicate those insights to strategists and decision-makers. They support organizations by asking the right questions and identifying relationships between disparate data sets.
Industries Using Data Science
- Government agencies
- Clinical research centers
- Banking sector
- Manufacturing industry
- Travel and hospitality sector
- Healthcare industry
- Business houses
Curricular Structure
Semester I
| Course Code | Course Title | Credits | Nature |
|---|---|---|---|
| MDS 501 | Fundamentals of Data Science | 3 | Theory |
| MDS 502 | Data Structure and Algorithms | 3 | Theory + Practical |
| MDS 503 | Statistical Computing with R | 3 | Theory + Practical |
| MDS 504 | Mathematics for Data Science | 3 | Theory |
| --- | Elective I (Any One) | 3 | --- |
| Total | 15 | --- |
Semester II
| Course Code | Course Title | Credits | Nature |
|---|---|---|---|
| MDS 551 | Programming with Python | 3 | Theory + Practical |
| MDS 552 | Applied Machine Learning | 3 | Theory + Practical |
| MDS 553 | Statistical Methods for Data Science | 3 | Theory + Practical |
| MDS 554 | Multivariable Calculus for Data Science | 3 | Theory |
| --- | Elective II (Any One) | 3 | --- |
| Total | 15 | --- |
Semester III
| Course Code | Course Title | Credits | Nature |
|---|---|---|---|
| MDS 601 | Research Methodology | 3 | Theory |
| MDS 602 | Advanced Data-Mining | 3 | Theory + Practical |
| MDS 603 | Techniques for Big Data | 3 | Theory + Practical |
| --- | Elective III (Any Two) | 3+3 | --- |
| Total | 15 | --- |
Semester IV
| Course Code | Course Title | Credits | Nature |
|---|---|---|---|
| MDS 651 | Data Visualization | 3 | Theory |
| MDS 652 | Capstone Project/Thesis | 3 | Project + Report |
| --- | Elective IV (Any Two) | 3+3 | --- |
| Total | 12 | --- |
Elective Courses
- Elective I
- MDS 505: Database Management System (3 Credits, Theory + Practical)
- MDS 506: Programming Skills with C (3 Credits, Theory + Practical)
- MDS 507: Linear and Integer Programming (3 Credits, Theory + Practical)
- Elective II
- MDS 555: Natural Language Processing (3 Credits, Theory + Practical)
- MDS 556: Artificial Intelligence (3 Credits, Theory + Practical)
- MDS 557: Learning Structure and Time Series (3 Credits, Theory + Practical)
- Elective III
- MDS 604: Cloud Computing (3 Credits, Theory + Practical)
- MDS 605: Regression Analysis (3 Credits, Theory + Practical)
- MDS 606: Decision Analysis: Monte Carlo Methods (3 Credits, Theory + Practical)
- MDS 607: Cloud Computing (3 Credits, Theory)
- Elective IV
- MDS 653: Social Network Analysis (3 Credits, Theory + Practical)
- MDS 654: Actuarial Data Analysis (3 Credits, Theory + Practical)
- MDS 655: Deep Learning (3 Credits, Theory + Practical)
- MDS 656: Business Analytics (3 Credits, Theory + Practical)
- MDS 657: Bioinformatics (3 Credits, Theory + Practical)
- MDS 658: Economic Analysis (3 Credits, Theory + Practical)
Evaluation System
- 40% internal evaluation and 60% external exam. Internal exams are based on attendance, assignment work, oral tests, class tests, presentations, class seminars, project work, and term exams.
- End semester exams are conducted by the School with permission from the TU exam board.
- Evaluation of projects or theses includes research/project monitoring by a supervisor, pre-viva by the School after submission, and evaluation by the Research Committee of the School with the supervisor's consent and external review.
- Students must secure at least 50% in each semester's exams and internal assessments to complete the course.
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