Bachelor in Data Science

• Four-year, eight-semester TU program integrating mathematics, statistics, and computer science to produce graduates skilled in data handling, modeling, machine learning, and analytical system development.
• Structured to build competency in statistical reasoning, algorithmic thinking, data manipulation, ML methods, modeling, programming, visualization, and applied computational techniques.
• Covers programming (C, Python, R), AI/ML, databases, data mining, deep learning, simulation, big data, NLP, cloud systems, and applied mathematics.
• Designed to develop operational, analytical, and technical talent for data-driven industries and graduate-level studies in data science, computing, or applied statistics.
• Includes project work, seminar components, and a full-term internship for industry exposure.

Curricular Structure in Table

Features
• Multi-disciplinary curriculum: statistics, mathematics, computing, AI/ML
• Strong programming foundation across C, Python, R
• Integrated ML, AI, neural networks, NLP, big data, and data mining tracks
• Two project courses + seminar + full internship
• Electives covering advanced industry areas (cloud, deep learning, blockchain, IoT, BI, SNA, forecasting)
• 121 credit hours with theoretical, practical, project, seminar, and internship components

Fee (Tentative)
• TU constituent schools typically under moderate public-fee structure
• Estimated annual range: NPR 80,000–150,000 (varies by institution; exact fees not publicly standardized)

Admission Process
• Eligibility: +2 or equivalent in any stream with minimum second division or C grade, with Mathematics/Business Mathematics (100 marks or 5 credit hours)
• Form submission at School of Mathematical Sciences, TU
• Screening/entrance evaluation as decided by SMS-TU
• Selection based on merit and seat availability
• Document verification and admission confirmation through payment

Job Prospects
• Data analyst (entry to mid-level)
• Machine learning technician
• Business intelligence assistant
• Data engineering support roles
• Statistical analyst assistant
• AI/ML model support technician
• Database and ETL support staff
• Data visualization assistant
• Junior software developer
• Research and analytics assistant (government, NGOs, private sector)
• Roles in fintech, telecom, health data, banking, insurance, logistics, retail analytics