Bachelor of Data Science

Bachelor of Data Science

4 years
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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