Bachelor of Data Science (BDSc)

Undergraduate program built to produce statistically competent, mathematically rigorous data scientists able to work with large-scale data, machine learning systems, and applied analytics.

Description (points)
• Structure centers on statistics, mathematics, computer science, and applied analytics.
• Focus on model building, algorithmic thinking, data engineering foundations, and computational methods.
• Emphasizes Python, R, SQL, OOP, ML, big-data technologies, deep learning, optimization, and statistical modeling.
• Includes two industry-linked capstone components: applied data science immersion and internship/final project.
• Designed for roles in research, finance, industry, development agencies, and governmental data units.

Curricular structure (summary)

Year 1 / Semester 1
Intro to Data Science; Python Programming; Linear Algebra; Calculus I; Computational Statistics I.
Credits: 15

Year 1 / Semester 2
Intro to AI; Technical Communication; Data Structures; Calculus II; Computational Statistics II; R Programming.
Credits: 18

Year 2 / Semester 1
OOP (Java); Data Analytics & Visualization; DBMS + Cloud Computing; Differential Equations; Discrete Mathematics.
Credits: 15

Year 2 / Semester 2
Introduction to ML; OS Fundamentals; Statistical Inference; Scientific Computing; Multivariate Analysis; Project.
Credits: 18

Year 3 / Semester 1
Data Governance; Algorithm Analysis; Parallel Computing; Optimization Techniques; Statistical & Digital Literacy.
Credits: 15

Year 3 / Semester 2
Feature Engineering; Big Data Technologies; Data Security; Data Science in Digital Marketing; Time Series Analysis; Project.
Credits: 18

Year 4 / Semester 1
Deep Learning; Predictive Analytics; Applied Data Science – Industry Immersion; Elective I; Elective II.
Credits: 15

Year 4 / Semester 2
Internship / Final Project.
Credits: 6

Elective cluster includes Biomathematics, Health Informatics, Econometrics, Numeric ODE, Data Mining, Bioinformatics, Stochastic Models, Mathematical/Statistical Modeling, Biostatistics, Industrial and Agricultural Statistics, Population Dynamics, Financial Modeling.

Eligibility (points)
• 10+2 with ≥ 50% overall.
• ≥ 50% in PCM or as specified by KU Academic Council.