Master in Data Science

Two-year, four-semester technical program combining mathematics, statistics, and computing to train specialists in data analysis, machine learning, big-data technologies, and applied decision-science.
Program builds quantitative, computational, analytical, and modeling capability. Training spans data engineering, statistical computing, ML, algorithmic thinking, big-data systems, visualization, and applied research. Curriculum stresses problem-solving through coding, statistical reasoning, experimentation, and project-based learning. Capstone work evaluates ability to design, implement, and communicate end-to-end data-science solutions.

Curricular structure (condensed)

Features

• First interdisciplinary data-science graduate program in Nepal.
• Strong math–stat–CS integration.
• Heavy practical work: coding labs, computational assignments, large datasets.
• Multi-exit elective model across AI, NLP, cloud, regression, deep learning, business analytics, bioinformatics, actuarial analysis.
• Capstone/thesis emphasizes end-to-end applied research.
• Standard 40% internal + 60% final evaluation model.

Eligibility
Bachelor’s degree with quantitative and computational grounding: calculus, linear algebra, introductory statistics.
Accepted backgrounds: BSc CSIT, BMathSc, BSc Math/Stat, BSc/BA with Math/Stat for two years, BE, BIT, BCA (with two Math + one Stat).

Admission criteria

• TU IOST MSc entrance exam; 100 MCQs; 2 hours; pass mark 35%.
• Merit-based selection.
• One must secure at least 50% in internal and external evaluations each semester to complete the degree.

Admission process (point form)

• Apply to School of Mathematical Sciences.
• Sit for TU IOST MSc entrance examination.
• Secure minimum qualifying score.
• Merit-based selection and enrollment.

• Government data and analytics units
• Banking, insurance, and fintech analytics
• Health, medical, and clinical-research data roles
• Manufacturing and industrial analytics
• Travel, tourism, and hospitality analytics
• IT, software, and cloud-service data teams
• Research laboratories and academic data-research units
• Private enterprises requiring large-scale data management and modeling