Bachelor of Data Science
Duration: 4 Years
Start Date:
Cost: KES 79,000 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:
- Data Scientist
- Data Analyst
- Machine Learning Scientist
- Machine Learning Engineer
- Business Intelligence Scientist
- Market Research Analyst
Kindly ask for a return call from our proficient OUK course consultants to have your inquiries addressed.
How to Apply:
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- Bachelor of Technology Education
- Postgraduate Diploma in Leadership and Accountability
- Postgraduate Diploma in Learning Design and Technology
Definitions of Terms
- Credit hours: A credit hour is equivalent to a minimum of 13 Instructional hours;
- Lecture/Instructional hours: means a period of time equivalent to one hour and representing one such continuous hour in lecture form, two in a tutorial or open learning session, three in a laboratory practical or practicum and five in farm or similar practice;
- Contact hours: Is the duration designated for a lecture session; Course units: Course Content covered in one credit hour.
Upon successful completion of this program, students will have cultivated the following capabilities:
- Articulate comprehensive insights into the applications of data science across a diverse spectrum of data-centric domains.
- Apply advanced data science technologies adeptly to resolve intricate real- world challenges spanning multiple sectors.
- Formulate sophisticated data analysis models and undertake independent or collaborative projects with precision.
- Demonstrate a profound understanding of the ethical codes and professional conduct principles inherent to the field of data science.
Learning Outcomes
By the end of this programme, the student will be able to:
- Describe the applications of data science in a wide range of data-related fields.
- Analyse and adapt the latest data science technologies to solve real-world problems in a broad range of sectors.
- Formulate appropriate models of data analysis and undertake a project in an independent or collaborative environment
- Exhibit an in-depth understanding to the codes of ethics and conduct of data science professions.
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.
- A mean grade of C+ and above at KCSE OR
- Diplomas or professional qualifications OR
- A certificate of foundation or bridging courses from recognised institutions OR
- A portfolio for the purpose of recognition of prior learning OR
- Kenya Advanced Certificate of Education with a minimum of 1 principal OR
- A bachelor’s degree from an institution recognised by Senate.
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:
- Must meet the requirements for admission to the Bachelor of Data Science program.
- Must obtain and submit an official transcript from the previous university/college indicating academic status, courses offered, credits units completed, and grades obtained.
- Will be allowed to transfer/exempt credits earned from the courses described, but only up to 49%.
- If permitted to transfer/exempt, he/she will not be permitted to transfer units in courses in which he/she received a pass mark of less than 50%.
- All applications must be accompanied by recommendations from the institution from which he or she is transferring.
- The school will evaluate the application and make recommendations to the Sen- ate.
Student Assessment at programme level
The course will be assessed through:
- Content embedded quizzes
- Online practical work
- Open book tests
- Project reports
- End of course online examination
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 |