Bachelor of Data Science
Duration: 4 Years
Start Date:
Cost: KES 105,000 per year
The Bachelor's program in Data Science represents a rigorous educational journey, thoughtfully designed to mold graduates into adept professionals equipped with essential technical and professional competencies essential for tackling the multifaceted challenges within the realm of data science. Participants will gain comprehensive knowledge and proficiency in substantial data analytics, data mining, computational intelligence, machine learning, statistical learning, scalable algorithms, and the optimization of expansive databases.
Career Prospects in Data Science:
Graduates of this program can explore diverse and promising career trajectories, including but not limited to:
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Definitions of Terms
Upon successful completion of this program, students will have cultivated the following capabilities:
Learning Outcomes
By the end of this programme, the student will be able to:
Total credit hours and course units required for graduation
The programme shall be offered in 8 semesters. The minimum total courses for the programme are 48. The minimum total course credit hours required for graduation is 144 hours.
A candidate must satisfy the general University admission criteria for undergraduate programmes.
Credit Accumulation
Regulations on credit accumulation, including possible pathways, shall be in line with the provisions of Universities Regulations, Universities Standards and Guidelines, and general national trends.
Credit Transfer
A candidate may be allowed to transfer credits from part or all of the coursework requirements if the senate is satisfied that the candidate has completed and passed the prescribed courses(s) at the undergraduate level from accredited institutions and programs recognized by the senate. Any course considered for credit transfer must have been completed at an equivalent level and in an equivalent institution, with a minimum grade of 50%.
Guidelines For Transfer Of Credit/ Exemptions
A candidate may be exempted from degree level courses if the Senate is satisfied that the candidate has completed a similar course at the Diploma level from a recognized institution. The general rules governing credit transfers and exemptions will apply. In addition, the following rules apply:
Student Assessment at programme level
The course will be assessed through:
The projects will be assessed through e- portfolios. Students will present their work to an evaluation panel. All students’ work will be checked for plagiarism. The students should be logged in with the university provided login details in order to carry out any task.
Countinous Assessment
Tests/Tasks: 50%
Examination 50%
FIRST SEMESTER | SECOND SEMESTER |
Introduction to Computing | Introduction to Database Systems |
Introduction to Programming I | Introduction to Programming II |
IT Entrepreneurship | Data Structures and Algorithms |
Foundations of Mathematics | Fundamentals of Data science |
Basic Statistics with R | Calculus |
Contemporary Issues in Psychology | Discrete Mathematics |
FIRST SEMESTER | SECOND SEMESTER |
Object Oriented Programming | Applied Research Project I |
Advanced Database Systems | Software Engineering |
Python for Data Science | Web Development I |
Linear Algebra | Essential Techniques in Machine Learning |
Multivariate Calculus | Mathematical Optimisation |
Probability and Statistics | Statistical Inference for Data Science |
FIRST SEMESTER | SECOND SEMESTER |
Computer Communication Network | Web Application Development II |
Data Warehousing | Data Mining |
Research Methods in Data Science | Artificial Intelligence and Machine Learning |
Graph and Network | Web Security and Privacy |
Differential Equations with Numerical Methods | Statistical Simulation and Modeling |
Applied Statistical Hypothesis Testing | Linear Modelling |
Introduction to Philosophy and Critical Thinking |
FIRST SEMESTER | SECOND SEMESTER |
Data Governance, Ethics and Law | Deep Learning and Computer Vision |
Applied Research Project II | Data Science on Cloud |
Database Administration | Natural Language Processing |
Data Science Project Management | Machine Learning Deployment and Monitoring |
Big Data Analytics with Apache Hadoop | Recommendations Systems |
Time Series Analysis | Bayesian Inference and Decision Theory |