Master of Data Science (MDS) is a 2-year, four-semester program by Tribhuvan University, Institute of Science and Technology, School of Mathematical Sciences.
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Educational Background
Applicants must have completed at least 15 years of formal education — that is, 12 years of schooling plus a 3-year undergraduate degree.
Academic Requirements
A minimum CGPA of 2.0, second division, or 45% marks at the Bachelor's level is required. Eligible degrees include:
B.Sc. CSIT or equivalent
B.Math.Sc or equivalent
B.Sc. (Mathematics) or equivalent
B.Sc. (Statistics) or equivalent
B.Sc./B.A. with Mathematics or Statistics in the first two years
B.E. or equivalent
B.I.T. or equivalent
B.C.A. or equivalent
B.I.M. (with at least one course each in Mathematics and Statistics) or equivalent
Applicants meeting these criteria are eligible to sit for the MDS entrance examination.
1st Semester
Course Code | Course Title | 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. |
Course Code | Course Title | 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. |
Course Code | Course Title | 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. |
Course Code | Course Title | Credits | Nature |
|---|---|---|---|
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. |
Course Code | Course Title | 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. |
Course Code | Course Title | 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 (Repeated Name) | 3 | Th. |
Course Code | Course Title | Credits | Nature |
|---|---|---|---|
MDS 651 | Data Visualization | 3 | Th. |
MDS 652 | Capstone Project / Thesis | 3 | Project + Report |
Course Code | Course Title | 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. |
Understand core concepts in data science and analytics
Apply machine learning and AI algorithms
Analyze and visualize complex data sets
Use programming languages like Python and R
Work with databases and big data tools
Build predictive and statistical models
Solve real-world problems using data-driven insights
Ensure data quality, privacy, and ethics
Communicate findings through data storytelling
Conduct independent research in data science
Integrate domain knowledge with analytical skills
Prepare for roles in tech, finance, healthcare, and more
The Master in Data Science (MDS) program at Tribhuvan University is a full-time course offered by the School of Mathematical Sciences (SMS). It equips students with core competencies in areas such as programming, statistics, data analytics, machine learning, data wrangling, data visualization, communication, business fundamentals, and ethics. These skills aim to enhance students’ employability in business, industry, and global companies.
The MDS program is interdisciplinary and the first of its kind under the Institute of Science and Technology at Tribhuvan University. Upon graduation, students will be able to:
Collect, organize, clean, and query data from various public and private sources.
Analyze and evaluate data to meet decision-making demands.
Use suitable analytical methods to generate insights that guide decisions and actions.
Clearly communicate insights and findings through written, spoken, and visual presentations.
