The Doctor of Philosophy (PhD) in Data Science at Madan Bhandari University of Science and Technology (MBUST) is a three-year, research-based doctoral program focused on advanced data analysis, machine learning, statistical methods, data engineering, and independent scientific research.
The program combines technical coursework, specialized electives, research methods, practical projects, and doctoral thesis work. Its academic coverage includes programming, data analytics and visualization, machine learning, deep learning, and the development of computational methods for analyzing complex datasets.
Research applications extend to agriculture, healthcare, tourism, telecommunications, social networks, and cybersecurity. The program also addresses ethical data practices, research evaluation, and the social implications of computational analysis.
MBUST offers the PhD through its Digital Technology academic area at Chitlang, Thaha Municipality, Makawanpur District, Bagmati Province, Nepal.
| Particulars | Details |
|---|---|
| Program | Doctor of Philosophy in Data Science |
| Abbreviation | PhD in Data Science |
| University | Madan Bhandari University of Science and Technology (MBUST) |
| Academic Level | Doctoral |
| Duration | 3 years |
| Academic Structure | 6 semesters |
| Study Mode | Research-based |
| Total Credits | 75 |
| Core Coursework | 17 credits |
| Technical Electives | 8 credits |
| Thesis Credits | 50 |
| Major Areas | Data Analytics, Machine Learning, Deep Learning, Data Engineering, Statistical Analysis |
| Research Component | Independent doctoral thesis |
| Location | Chitlang, Makawanpur, Bagmati Province, Nepal |
The PhD in Data Science develops advanced scientific and computational research competencies in data analysis, statistical methods, and machine learning.
Students study methods for processing, analyzing, interpreting, and evaluating data. The program combines computational techniques with research methods, allowing doctoral students to investigate problems involving complex datasets and develop original research findings.
The curriculum begins with programming, data analytics, machine learning, and research methods. Advanced coursework introduces data engineering, deep learning, development policy, and specialized technical subjects.
Independent doctoral research forms the largest component of the degree. Students undertake thesis work involving a selected research problem, with opportunities to apply data science methods to different academic and sectoral subjects.
The program also incorporates ethical considerations, scientific communication, and research connected with the Sustainable Development Goals (SDGs).
The PhD in Data Science aims to develop researchers capable of conducting independent scientific investigations involving data and computational methods.
Its principal objectives include:
Developing advanced knowledge of data science principles, statistical methods, and computational techniques.
Applying machine learning methods to complex research problems.
Evaluating and comparing computational models.
Conducting statistical analysis and interpreting research findings.
Investigating data engineering methods and computational systems.
Applying appropriate research methodologies and ethical principles.
Developing original research contributions through doctoral thesis work.
Communicating scientific findings through research presentations and academic writing.
These objectives connect technical competence with independent investigation and critical evaluation of scientific results.
The PhD in Data Science follows a six-semester academic structure carrying 75 credits.
The curriculum combines core coursework, technical electives, compulsory non-credit subjects, and doctoral thesis research.
| Academic Component | Credits |
|---|---|
| Core Coursework | 17 |
| Technical Electives | 8 |
| Doctoral Thesis | 50 |
| Total | 75 |
The thesis accounts for 50 credits, making independent research the principal academic component of the program.
The first semester establishes the computational and research foundation for doctoral study.
| 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 semester introduces programming, analytical methods, machine learning, and scientific investigation.
The compulsory non-credit component addresses entrepreneurship, communication, and leadership.
The second semester extends technical study into data engineering and deep learning while introducing specialized electives and thesis research.
| 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 |
Students continue specialized coursework and begin doctoral research activities.
The ethical studies component addresses questions associated with computational analysis and artificial intelligence.
The third semester includes 13 thesis credits.
The academic progression also identifies additional technical-elective study and research proposal submission to the advisory committee.
Students begin developing their independent investigation through the selected research topic and appropriate scientific methods.
The fourth semester includes 13 thesis credits, with the academic timeline also identifying further technical-elective study.
Students continue their doctoral investigation and present research progress.
The fifth semester carries 10 thesis credits.
Research activities concentrate on the continued development of the doctoral investigation, including research progress presentation.
The final semester carries 10 thesis credits.
The academic progression includes a comprehensive examination and thesis defense.
These assessment components form part of the program's doctoral research requirements.
Programming provides the computational foundation for processing datasets, implementing analytical methods, and developing research applications.
The subject supports advanced work in machine learning, data engineering, and computational analysis.
Data Analytics and Visualization addresses methods for examining datasets and presenting analytical results.
Students develop knowledge relevant to identifying patterns, interpreting information, and communicating findings through appropriate visual representations.
Machine Learning for Data Science examines computational methods used to construct and evaluate analytical models.
The academic objectives include applying machine learning methods to research problems, comparing model performance, and selecting suitable analytical techniques.
This course supports preparation for independent scientific investigation.
It connects research questions with appropriate methods, analysis, evaluation, and the critical assessment of scientific findings.
Data Engineering and Architecture addresses the organization and processing of information used in computational research and analysis.
The subject supports understanding of data systems and the technical requirements of handling datasets.
Deep Learning extends machine learning study into advanced neural network methods.
It supports research involving complex computational models and specialized data analysis.
The doctoral curriculum includes eight technical-elective credits.
Elective study supports specialization in subjects related to the student's doctoral research.
The Data Science academic portfolio includes areas such as text mining, information retrieval, human-computer interaction, artificial intelligence applications in agriculture and healthcare, climate-related analysis, tourism, and social network analysis.
The program's wider research interests also include Bayesian machine learning, graph and network analysis, and computational methods associated with early warning systems.
Students develop their research specialization through the combination of technical study and doctoral thesis work.
Independent doctoral research is the largest academic component of the PhD in Data Science.
The 50-credit thesis requires students to investigate a selected research problem using appropriate computational and scientific methods.
The research process involves developing a research question, reviewing relevant literature, selecting methods, analyzing data, evaluating results, and preparing the doctoral thesis.
The program identifies several research milestones, including proposal submission, thesis progress presentations, a comprehensive examination, and thesis defense.
Practical group projects are also included within the wider graduate program's research-oriented learning approach.
The program connects data science research with several application areas.
Agriculture, healthcare, tourism, telecommunications, social media, and cybersecurity are among the identified fields.
These areas provide subjects for investigating computational analysis, machine learning, data processing, and research methods.
The program also emphasizes community-related research and the consideration of societal benefits when examining data science applications.
Individual research topics depend on the student's specialization and doctoral research direction.
Ethical data practices form part of the PhD program's academic orientation.
Students examine questions of ethics and fairness in artificial intelligence and data-related applications.
The program also connects research with the Sustainable Development Goals and community-related problems.
These components support the evaluation of computational methods in relation to their technical performance, reliability, and implications for people and institutions.
The PhD provides advanced preparation for scientific research involving data science, machine learning, statistical analysis, data engineering, and computational modeling.
Its academic coverage is relevant to university research, data science laboratories, technology development, analytical research, and specialized computing activities.
The identified application areas include banking, healthcare, agriculture, tourism, telecommunications, academia, and cybersecurity.
Graduates may pursue research, university teaching, advanced data analysis, machine learning development, or related technical activities, depending on their specialization, research experience, and institutional requirements.
It is a three-year doctoral program covering programming, data analytics, machine learning, deep learning, data engineering, specialized electives, and independent scientific research.
The program follows a three-year academic structure consisting of six semesters.
The program carries 75 credits, including 17 core-course credits, eight technical-elective credits, and 50 thesis credits.
Major subjects include Programming for Data Science, Data Analytics and Visualization, Machine Learning for Data Science, Research Methods for Data Science, Data Engineering and Architecture, and Deep Learning.
Yes. Independent doctoral thesis research accounts for 50 credits.
Yes. The curriculum includes eight technical-elective credits supporting specialized doctoral study.
The academic progression includes research proposal submission, thesis progress presentations, a comprehensive examination, and thesis defense.
The program is offered at Madan Bhandari University of Science and Technology in Chitlang, Makawanpur District, Bagmati Province, Nepal.
