The Master of Applied Science (MAS) in Data Science at Madan Bhandari University of Science and Technology (MBUST) is a two-year, research-based postgraduate program covering data analytics, statistical analysis, machine learning, data engineering, programming, and advanced computational methods.
The program combines technical coursework, practical projects, research methods, and supervised thesis work. Students learn to collect, process, analyze, and interpret data while examining how computational methods can address problems across different disciplines.
Its academic scope includes applications in agriculture, healthcare, tourism, telecommunications, social networks, and other sectors that use data for research and decision-making.
The program is offered through MBUST's Digital Technology academic area at Chitlang, Thaha Municipality, Makawanpur District, Bagmati Province, Nepal.
| Particulars | Details |
|---|---|
| Program | Master of Applied Science in Data Science |
| Abbreviation | MAS in Data Science |
| University | Madan Bhandari University of Science and Technology (MBUST) |
| Academic Level | Master's |
| Duration | 2 years |
| Academic Structure | 4 semesters |
| Study Mode | Full-time, research-based |
| Major Subjects | Programming, Data Analytics, Machine Learning, Deep Learning, Data Engineering |
| Research Component | Supervised thesis |
| Thesis Credits | 30 |
| Eligibility | Four-year Bachelor's degree in a relevant field with minimum CGPA 2.75/4.0 or equivalent |
| Location | Chitlang, Makawanpur, Bagmati Province, Nepal |
The MAS in Data Science provides advanced study in computational and statistical methods used to examine data, identify patterns, evaluate models, and conduct scientific research.
The curriculum begins with programming, data analytics, visualization, machine learning, and research methods. Students subsequently study data engineering, deep learning, and technical electives connected with their research interests.
A substantial part of the degree is devoted to independent research. Students develop a research proposal, undertake thesis work, review their progress, and complete the academic assessment requirements associated with their research.
The program also considers ethical issues related to data analysis and computational decision-making. Scientific communication, development policy, entrepreneurship, and research evaluation form part of its wider academic coverage.
The MAS in Data Science aims to develop advanced technical and research competencies in data science.
Its principal objectives include:
Applying machine learning methods to defined data science problems.
Evaluating and comparing computational models using appropriate analytical methods.
Conducting statistical analysis and interpreting findings.
Developing knowledge of data engineering, programming, and computational techniques.
Applying research methods to investigate complex data-related questions.
Examining ethical considerations associated with data collection, analysis, and computational applications.
Producing independent research through a master's thesis.
The program combines technical specialization with the ability to evaluate research methods and communicate analytical findings.
The MAS follows a four-semester structure consisting of core courses, technical electives, compulsory non-credit courses, and thesis research.
The first semester establishes foundations in programming, analytics, machine learning, and research methods. The second introduces data engineering, deep learning, specialized electives, and thesis work.
The third and fourth semesters concentrate on research, thesis development, and academic assessment.
| Course Code | Course Title | Credits |
|---|---|---|
| DS-CR-501 | Programming for Data Science | 2 |
| DS-CR-502 | Data Analytics and Visualization | 3 |
| DS-CR-503 | Machine Learning for Data Science | 3 |
| DS-CR-504 | Research Methods for Data Science | 1 |
| GC-NC-550 | Entrepreneurship, Scientific Communication and Leadership | 0 |
The first semester introduces the principal technical and research subjects required for postgraduate data science study.
Programming provides a computational foundation, while data analytics and visualization develop methods for examining and presenting information.
Machine Learning for Data Science introduces computational modeling, and Research Methods for Data Science supports preparation for independent academic investigation.
| Course Code | Course Title | Credits |
|---|---|---|
| DS-CR-550 | Data Engineering and Architecture | 2 |
| DS-CR-551 | Deep Learning | 3 |
| DS-EL-561–570 | Technical Elective I | 2 |
| DS-EL-561–570 | Technical Elective II | 2 |
| GC-CR-501 | Development Policy | 3 |
| AI-NC-553 | Case Studies in Ethics and Fairness in AI | 0 |
| DS-TH-699 | Thesis | 4 |
The second semester extends technical study into data engineering and deep learning while allowing students to select electives related to their intended thesis.
Students also begin formal thesis work through research proposal preparation and submission to the advisory committee.
Development Policy and ethical case studies connect computational research with institutional, social, and policy considerations.
| Course Code | Course Title | Credits |
|---|---|---|
| DS-TH-699 | Thesis | 13 |
The third semester concentrates on thesis research.
Students continue their investigation and present their research progress as part of the academic assessment process.
| Course Code | Course Title | Credits |
|---|---|---|
| DS-TH-699 | Thesis | 13 |
The final semester includes thesis completion, a comprehensive examination, and thesis defense.
The thesis carries 30 credits across the second, third, and fourth semesters.
Programming provides the computational basis for working with datasets, implementing analytical methods, and developing data-related applications.
It supports subsequent coursework in machine learning, data engineering, and advanced computational analysis.
This subject concentrates on examining data and presenting analytical findings through appropriate visual methods.
It contributes to students' ability to interpret datasets and communicate the results of statistical and computational investigations.
Machine learning introduces computational methods for identifying patterns, constructing analytical models, and evaluating model performance.
The program emphasizes selecting appropriate techniques and comparing their effectiveness for particular analytical tasks.
Data Engineering and Architecture addresses the technical organization and processing of data for computational applications.
The subject supports the development of knowledge related to data systems and the handling of information used in analysis and research.
Deep Learning extends machine learning study into advanced neural network methods.
It provides preparation for computational research involving complex data patterns and specialized learning models.
Students select technical electives according to their academic interests and intended thesis topic.
The identified elective subjects include:
| Course Code | Technical Elective |
|---|---|
| DS-EL-561 | Generative AI and Applications |
| DS-EL-562 | Text Mining and Information Retrieval |
| DS-EL-563 | Human-Computer Interaction |
| DS-EL-564 | AI in IoT |
| DS-EL-565 | AI in Agriculture |
| DS-EL-566 | AI in Climate |
| DS-EL-567 | AI in Tourism |
| DS-EL-568 | Social Network Analysis |
| DS-EL-569 | Healthcare Analysis |
These subjects connect data science methods with computing, agriculture, climate, tourism, healthcare, and social applications.
Elective selection allows students to develop specialized knowledge relevant to the research they intend to undertake.
Research is a central component of the MAS in Data Science.
The academic structure requires students to develop an original thesis through supervised investigation.
The research process includes proposal development, examination of relevant literature, selection of research methods, data analysis, evaluation of findings, and communication of results.
The program identifies specific research milestones:
Second semester: Research proposal submission and initiation of thesis work.
Third semester: Thesis progress presentation.
Fourth semester: Comprehensive examination and thesis defense.
This sequence connects formal coursework with the development and assessment of independent research.
The program's practical learning approach also includes group projects and applied problem-solving activities alongside its research requirements.
Applicants must have completed a four-year Bachelor's degree in Science, Engineering, Technology, or another relevant subject from a recognized university.
The minimum academic requirement is a CGPA of 2.75 out of 4.0 or an equivalent qualification.
The program involves programming, statistical reasoning, machine learning, and computational research. Prior academic preparation in mathematics and programming is therefore relevant to its technical requirements.
The MAS includes ethical considerations as part of its academic structure.
Students examine ethical questions and fairness in artificial intelligence applications through a compulsory non-credit course.
The broader program also emphasizes responsible data practices and the relationship between computational research and its social applications.
These subjects support the evaluation of data science methods beyond technical performance alone, including their implications for individuals, organizations, and communities.
The program connects its technical subjects with research and analytical applications across several sectors.
Identified areas include agriculture, healthcare, tourism, telecommunications, social media, and cybersecurity.
Data science methods are relevant to examining information, identifying patterns, developing computational models, and evaluating research questions within these fields.
The particular application studied by an individual student depends on their technical electives and thesis research.
The MAS provides postgraduate preparation for technical and research activities related to data science, machine learning, statistical analysis, data engineering, and computational modeling.
Its academic subjects are relevant to data science and analytics positions, machine learning development, research, and data-related analytical work in different sectors.
The program also supports preparation for further academic research. MBUST offers a separate PhD program in Data Science for advanced doctoral study.
Professional opportunities depend on individual technical competence, academic specialization, research experience, and the requirements of employers or higher education institutions.
It is a two-year, research-based master's degree covering programming, data analytics, machine learning, deep learning, data engineering, technical electives, and independent thesis research.
The program lasts two years and consists of four semesters.
Applicants need a four-year Bachelor's degree in Science, Engineering, Technology, or another relevant subject from a recognized university, with a minimum CGPA of 2.75 out of 4.0 or equivalent.
The program includes 30 thesis credits distributed across the second, third, and fourth semesters.
Yes. Students select technical electives relevant to their intended research interests and thesis topic.
Yes. The academic structure combines coursework with group projects, research activities, technical electives, and supervised thesis work.
The research progression includes proposal submission, a thesis progress presentation, a comprehensive examination, and thesis defense.
Yes. MBUST offers a separate PhD in Data Science.
