The Department of Mathematics has introduced a new program “Bachelor of Data Science (BDSc)” commencement from August, 2024. This program integrates research activities, discussions, lectures, and teaching at various levels. The entire course is highly participatory and practice-oriented. The didactics of core courses and workshops are designed in a highly participatory and practice-oriented manner. The interactive learning environment combines theory and practice through modern didactics.
The department is renowned for its dedicated and strong academic faculty, who possess diverse academic backgrounds and actively participate in a variety of research endeavours. The faculty consists of highly experienced, renowned and educated from around the world, representing diverse perspectives from academia, industry and evaluation practice.
Data Science is a rapidly growing and versatile interdisciplinary field. This course aims to familiarize students with the various components of a comprehensive Data Science project and the process of developing, implementing, and evaluating it. After completing this course, students will be prepared to begin the process of comprehensive Data Science. The course objective is to help students understand the significance of Data Science.
Program Features
- Four years program compatible with international standards.
- Emphasize on Statistics, Mathematics, Computer Science and Business Intelligence.
- The emphasis of the program will be primarily on statistical methods, machine learning, data analysis, and professional development.
- Students are exposed to wide range of problems in Science, Engineering, Business, Technology and Industries.
Objective of the Course
- To produce future Data Scientists proficient in utilizing statistics, advanced analytics, and machine learning across various disciplines with the expertise needed to tackle intricate real-world issues and tasks.
- To integrate fields within computer science, optimization, and statistics, creating proficient and well-rounded data scientists.
- To offer practical and computer-based professional training to explore, organize, and analyze large data sets from various sources, thereby enabling optimal decision-making and process optimization.
Career Opportunities
- IT and Software Industries
- Healthcare Industries
- Oil and Gas Industries
- Business Houses
- Governmental Organizations and Ministries
- Financial Institutions
- Academic Industries and Research Centers
Financial Aid and Scholarship
- Merit-based full tuition fee waiver scholarships
- Need and merit-based partial tuition fee waiver scholarships
- Loan scholarship
Course Structure
This program will emphasize on Statistics, Mathematics, Computer Science and Business with the following course structure.
DETAILS OF THE COURSE STRUCTURE |
||||||||
Year |
Semester |
Course Plan of Bachelor of Data Science (BDSc) (2024 onwards) |
Total Credits |
|||||
I |
I |
DSMA 111 |
|
DSMA 113 |
DSMA 114 |
DSMA 115 |
DSMA 116 |
15 |
II |
DSMA 121 |
DSMA 122 |
DSMA 123 |
DSMA 125 |
DSMA 126 |
DSMA 199 |
18 |
|
II |
I |
DSMA 211 |
DSMA 212 |
|
DSMA 214 |
DSMA 215 |
DSMA 216 |
15 |
II |
DSMA 221 |
DSMA 222 |
DSMA 224 |
DSMA 225 |
DSMA 226 |
DSMA 299 |
18 |
|
III |
I |
DSMA 311 |
|
DSMA 313 |
DSMA 314 |
DSMA 315 |
DSMA 316 |
15 |
II |
DSMA 321 |
DSMA 322 |
DSMA 323 |
DSMA 324 |
DSMA 325 |
DSMA 399 |
18 |
|
IV |
I |
DSMA 411 |
DSMA 412 |
DSMA 413 |
Elective I |
Elective II |
|
15 |
II |
DSMA 498/ DSMA 499 |
06 |
||||||
Total |
120 |
Elective courses
DSMA 451 (Biomathematics), DSMA 452 (Health Informatics), DSMA 453 (Econometrics), DSMA 454 (Numerical Methods in ODE), DSMA 455 (Data Mining), DSMA 456 (Bioinformatics), DSMA 457 (Stochastic Models), DSMA 458 (Mathematical Modeling), DSMA 459 (Statistical Modeling), DSMA 460 Biostatistics, DSMA 461 (Industrial Statistics), DSMA 462 (Agricultural Statistics), DSMA 463 (Population Dynamics), DSMA 464 (Financial modelling) |
Contact Information
Department of Mathematics, School of Science, KU
Contact No.: 9801670053, 9701002602
Email ID: math_hod@ku.edu.np
Website: math.ku.edu.np