Bachelor of Data Science (BDS) is a 4-year course by Tribhuvan University, Institute of Science and Technology (IoST), School of Mathematical Science.
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Applicants seeking admission to the BDS program must have successfully completed 10+2 or an equivalent qualification in any stream with at least a second division (minimum 45%) or have secured a minimum 'C' grade in all subjects of both Grade 11 and Grade 12.
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS101 | Introduction to Data Science | 3 |
BDS102 | Basic Computer Organization | 3 |
BDS103 | Programming in C | 3 |
BDS104 | Statistics for Data Science | 3 |
BDS105 | Calculus I | 3 |
Total Credit Hours | 15 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS151 | Python Programming | 3 |
BDS152 | Database Management System | 3 |
BDS153 | Probability Distribution | 3 |
BDS154 | Calculus II | 3 |
BDS155 | Linear Algebra | 3 |
Total Credit Hours | 15 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS201 | Data Structure and Algorithms | 3 |
BDS202 | Operating System | 3 |
BDS203 | R Programming | 3 |
BDS204 | Inferential Statistics | 3 |
BDS205 | Differential Equations | 3 |
BDS206 | Seminar | 1 |
Total Credit Hours | 16 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS251 | Artificial Intelligence | 3 |
BDS252 | Web Development | 3 |
BDS253 | Data Communications and Computer Networking | 3 |
BDS254 | Discrete Mathematics | 3 |
BDS255 | Technical Writing | 3 |
Total Credit Hours | 15 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS301 | Machine Learning | 3 |
BDS302 | Software Design and Development | 3 |
BDS303 | Data Visualization | 3 |
BDS304 | Numerical Methods | 3 |
BDS305 | Economics | 2 |
BDS306 | Project I | 3 |
Total Credit Hours | 17 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS351 | Data Warehousing and Data Mining | 3 |
BDS352 | Artificial Neural Network | 3 |
BDS353 | Computer Graphics and Image Processing | 3 |
BDS354 | Research Methodology | 3 |
BDS355 | Principles of Management | 3 |
Total Credit Hours | 15 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS401 | Object Relational and NoSQL Databases | 3 |
BDS402 | Simulation and Modeling | 3 |
BDS403 | Data Security | 3 |
BDS404 | Project II | 3 |
Elective I | (Choose any one) | 3 |
BDS421 | Cloud Computing | |
BDS422 | Deep Learning | |
BDS423 | Mobile Application Development | |
BDS424 | Blockchain Technology | |
BDS425 | Exploratory Data Analysis | |
Total Credit Hours | 15 |
Course Code | Course Title | Credit Hours |
|---|---|---|
BDS451 | Information Retrieval | 3 |
BDS452 | Big Data Analytics with Hadoop | 3 |
BDS453 | Natural Language Processing | 3 |
BDS454 | Internship | 3 |
Elective II | (Choose any one) | 3 |
BDS471 | Social Network Analysis | |
BDS472 | Forecasting | |
BDS473 | Digital Marketing | |
BDS474 | Business Strategy | |
BDS475 | Internet of Things | |
Total Credit Hours | 15 |
Data Analyst – Analyze data to help businesses make smart decisions.
Data Scientist – Use advanced tools to solve complex problems using data.
Business Intelligence (BI) Analyst – Create reports and dashboards for company insights.
Machine Learning Engineer – Build systems that learn from data automatically.
Data Engineer – Design and maintain systems to collect and store data.
AI Specialist – Work on artificial intelligence solutions in various industries.
Statistician – Interpret data trends and patterns using statistical methods.
Risk Analyst – Help companies assess and manage financial or operational risks.
Big Data Analyst – Work with massive datasets using tools like Hadoop and Spark.
Further Studies – Pursue Master’s in Data Science, AI, or related fields for advanced roles.
Data Science is rapidly emerging as a crucial field across various industries, evolving alongside advancements in time and technology. Today, it plays a vital role in universities, particularly in the areas of Statistics and Computer Science, and is increasingly important for government agencies. Businesses rely on big data to enhance customer service and make better decisions, making data science professionals highly sought-after in the job market— a demand that is expected to rise steadily.
The Data Science program is inherently interdisciplinary, combining Mathematics, Statistics, Computer Science, and Information Technology to analyze and interpret data. Its primary goal is to derive meaningful insights from data that can inform strategic decisions, aid in product development, and enable trend forecasting.
The BDS curriculum is carefully crafted to offer both comprehensive knowledge and specialized skills required for a thriving career in data science. It places strong emphasis on hands-on learning through coursework in statistical modeling, data management, artificial intelligence, machine learning, data visualization, and other related domains. These skills help uncover patterns and insights hidden within organizational data. With the ever-increasing volume of data, data science continues to be one of the fastest-growing and most in-demand fields globally.
Upon completion of this program, students will be able to:
Build expertise in computer science, statistical modeling, and mathematics for complex data-driven problem solving.
Continuously enhance their understanding of ethical, professional, and social dimensions related to data science.
The Bachelor in Data Science is a full-time program spread across eight semesters over four years. It includes core foundational subjects in Mathematics, Statistics, and Computer Science & Technology. Courses cover areas such as algorithms, artificial intelligence, machine learning, data analysis, statistical and computer programming, database systems, and web development.
Each standard course carries 3 credit hours, with the exception of seminars, project work, and internships. Each 3-credit course involves 48 hours of instruction per semester.
Total Credit Hours: 123
Nature of Courses: Theoretical, Practical, Project, Seminar, Internship
All courses (excluding seminar, project work, and internship) are evaluated based on 40% internal and 60% external assessment.
Students must score at least 40% in both internal and external evaluations to pass a course.
Final exams for applicable courses are held at the end of each semester, contributing 60% of the overall course weight.
For laboratory-based courses:
50% of the total evaluation is reserved for practical exams.
An external expert may be invited for the practical evaluation.
For seminars, project work, and internships:
These are assessed by multiple evaluators.
Students must score at least 40% in each evaluation component to pass.
The final grade is based on the aggregate of all evaluations.
External examiners are assigned for final presentations.
Grades are awarded based on a combination of internal and external performance. The final grade uses a letter-based system, correlating to grade points and performance remarks.
| Letter Grade | Grading Scale (%) | Grade Point | Performance Remarks |
|---|---|---|---|
| A | 90 – 100 | 4.0 | Outstanding |
| A− | 80 – less than 90 | 3.7 | Excellent |
| B+ | 70 – less than 80 | 3.3 | Very Good |
| B | 60 – less than 70 | 3.0 | Good |
| B− | 50 – less than 60 | 2.7 | Satisfactory |
| C | 40 – less than 50 | 2.3 | Pass* |
| F | Less than 40 | 0.0 | Fail |
*Note: A 'Pass' grade refers to the minimum acceptable performance.
