Offering Colleges (1)
In the modern era, knowledge in Data Science is crucial due to the prevalence of Computers, Artificial Intelligence, and Big Data. The computational power of modern machines facilitates statistics and data analysis, which are key aspects of Data Science. Statisticians play a vital role in interpreting vast amounts of data generated across various fields, identifying significant patterns and trends, and understanding what the data reveals.
The Bachelor of Data Science program of Kathmandu University aims to address challenges in data storage, organization, and retrieval. This field employs tools and methodologies such as machine learning, data analysis, and data visualization to transform raw data into actionable insights.
In Nepal, data originates from diverse sources, including research, field studies by NGOs or INGOs, quality control in industries, planning commission data, stock market studies, and credit card transactions. International data, accessible through cloud-sharing technologies from organizations like NASA and the WHO, can be used in Nepal. Mastery of mathematical techniques is essential for processing these large datasets and drawing significant conclusions that aid societal development and guide policymakers.
Statistics provides methods for extracting information from samples and designing experiments, making it applicable in various fields such as sciences, agriculture, business, finance, and engineering. It also quantifies uncertainty from complex experimental interactions, enhancing the reliability of conclusions.
Objectives of the Course
- To cultivate Data Scientists skilled in interdisciplinary applications, employing statistics, advanced analytics, and machine learning to solve complex real-world problems and challenges.
- 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.
- To integrate fields within computer science, optimization, and statistics, creating proficient and well-rounded data scientists.
Importance of the course in Nepalese context
The global demand for data science professionals has led to widespread use of data for decision-making and competitive advantage. In Nepal, this trend is reflected in the growing popularity of data science courses, which prepare individuals for rewarding careers and support the country's technological progress. The importance of skilled data scientists in Nepal's data-driven decision-making landscape cannot be overstated, with Kathmandu University's Data Science course playing a pivotal role in shaping the nation's technical future.
Graduates of this program find ample opportunities in various fields, benefiting from high demand, attractive salaries, and global employment prospects. The course not only equips students with essential skills but also contributes to Nepal's broader technological advancement, underscoring the critical importance of data science expertise. Upon completing this course, individuals can pursue careers in various sectors within Nepal:
a. NGO/INGO: Data scientists apply their mathematical and statistical expertise to interpret data collected and stored in clouds by both national and international agencies.
b. Business and Industry: Data scientists play pivotal roles in quality control, product development, and enhancement within businesses and industries. They utilize data science to determine product manufacturing, pricing strategies, target markets, and manage assets and liabilities, assessing risks and returns for investments in banking and insurance sectors.
c. Governmental Organizations and Ministries: Every government agency can benefit from employing data scientists. They contribute to scientific, environmental, and agricultural endeavors, aiding in tasks like assessing pesticide levels in drinking water, monitoring endangered species populations, or analyzing disease prevalence among the populace.
Eligibility
The applicants for this course should have 10+2 with 50% and above marks and they should have above 50% in PCM or as per passed by the Academic Council of Kathmandu University.
Admission Criteria
Applicants must satisfy the general requirements for admission to the University.
Curricular Structure
This program will basically emphasize on Statistics, Mathematics, Computer Science and Business. The emphasis of the program will be primarily on statistical methods, machine learning, data analysis, and professional development. This is a Four-Year Course with the following course structure.
Year I / Semester I
Course Code | Course Title | Credits |
---|---|---|
DSMA 111 | Introduction to Data Science | 3 |
DSMA 113 | Introduction to Python Programming | 3 |
DSMA 114 | Linear Algebra | 3 |
DSMA 115 | Calculus-I | 3 |
DSMA 116 | Computational Statistics-I | 3 |
Total | 15 |
Year I / Semester II
Course Code | Course Title | Credits |
---|---|---|
DSMA 121 | Introduction to AI | 3 |
DSMA 122 | Technical Communication Skill | 3 |
DSMA 123 | Data Structures | 3 |
DSMA 125 | Calculus-II | 3 |
DSMA 126 | Computational Statistics-II | 3 |
DSMA 199 | R-Programming | 3 |
Total | 18 |
Year II / Semester I
Course Code | Course Title | Credits |
---|---|---|
DSMA 211 | Programming Concepts Using OOP (Java) | 3 |
DSMA 212 | Data Analytics and Visualization | 3 |
DSMA 214 | DBMS+ Cloud Computing | 3 |
DSMA 215 | Differential Equations | 3 |
DSMA 216 | Discrete Mathematics | 3 |
Total | 15 |
Year II / Semester II
Course Code | Course Title | Credits |
---|---|---|
DSMA 221 | Introduction to Machine Learning | 3 |
DSMA 222 | Operating System Fundamentals | 3 |
DSMA 225 | Statistical Inference | 3 |
DSMA 224 | Scientific Computing | 3 |
DSMA 226 | Multivariate Analysis | 3 |
DSMA 299 | Project | 3 |
Total | 18 |
Year III / Semester I
Course Code | Course Title | Credits |
---|---|---|
DSMA 311 | Data Governance | 3 |
DSMA 313 | Algorithm Analysis | 3 |
DSMA 314 | Parallel Computing | 3 |
DSMA 315 | Optimization Techniques | 3 |
DSMA 316 | Statistical and Digital Literacy | 3 |
Total | 15 |
Year III / Semester II
Course Code | Course Title | Credits |
---|---|---|
DSMA 321 | Feature Engineering | 3 |
DSMA 322 | Big Data Technologies | 3 |
DSMA 323 | Data Security | 3 |
DSMA 324 | Data Science in Digital Marketing | 3 |
DSMA 325 | Time Series Analysis | 3 |
DSMA 399 | Project | 3 |
Total | 18 |
Year IV / Semester I
Course Code | Course Title | Credits |
---|---|---|
DSMA 411 | Deep Learning | 3 |
DSMA 412 | Predictive Analytics | 3 |
DSMA 413 | Applied Data Science; Industry Immersion | 3 |
DSMA | Elective I | 3 |
DSMA | Elective II | 3 |
Total | 15 |
Year IV / Semester II
Course Code | Course Title | Credits |
---|---|---|
DSMA 498 / DSMA 499 | Internship / Final Project | 6 |
Total | 6 |
Total Credit: 120
Course Code | Course Title |
---|---|
DSMA 111 | Introduction to Data Science |
DSMA 112 | Set and Logic |
DSMA 113 | Introduction to Python Programming |
DSMA 114 | Linear Algebra |
DSMA 115 | Calculus-I |
DSMA 116 | Computational Statistics-I |
DSMA 121 | Introduction to AI |
DSMA 122 | Technical Communication Skill |
DSMA 123 | Data Structures |
DSMA 125 | Calculus-II |
DSMA 126 | Computational Statistics-II |
DSMA 199 | Project + R-Programming |
DSMA 211 | Programming Concepts Using OOP (Java) |
DSMA 212 | Data Analytics |
DSMA 213 | Data Visualization |
DSMA 214 | DBMS |
DSMA 215 | Differential Equations |
DSMA 216 | Discrete Mathematics |
DSMA 221 | Introduction to Machine Learning |
DSMA 222 | Operating System Fundamentals |
DSMA 224 | Scientific Computing |
DSMA 225 | Statistical Inference |
DSMA 226 | Multivariate Analysis |
DSMA 299 | Project (SQL) |
DSMA 311 | Data Governance |
DSMA 312 | Social Network Analysis |
DSMA 313 | Algorithm Analysis |
DSMA 314 | Parallel Computing |
DSMA 315 | Optimization Techniques |
DSMA 316 | Statistical Literacy and Digital Literacy |
DSMA 321 | Feature Engineering |
DSMA 322 | Big Data Technologies |
DSMA 323 | Data Security |
DSMA 324 | Data Science in Digital Marketing |
DSMA 325 | Time Series Analysis |
DSMA 399 | Project + Literate Programming |
DSMA 411 | Deep Learning |
DSMA 412 | Predictive Analytics |
DSMA 413 | Applied Data Science; Industry Immersion |
DSMA | Elective I |
DSMA | Elective II |
DSMA 498 | Internship |
DSMA 499 | Final Project |
Elective Courses:
DSMA 451 Biomathematics
DSMA 452 Health Informatics
DSMA 453 Econometrics
DSMA 454 Numeric in ODE
DSMA 455 Data Mining
DSMA 456 Bio Informatics
DSMA 457 Stochastic Models
DSMA 458 Mathematical Modeling
DSMA 459 Statistical Modeling
DSMA 460 Biostatistics
DSMA 461 Industrial Statistics
DSMA 462 Agriculture Statistics
DSMA 463 Population Dynamics
DSMA 464 Financial Modeling