• Thu. Jul 18th, 2024

Bachelor of Data Science Kuccps Course requirements, fees and duration

Jul 15, 2024

Duration: 4 Years

Start Date: 

Cost: KES 105,000 per year

Mode of Delivery: Online

Application Due: Open

Fee Structure and Payment

Overview

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

Programme Structure

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:

  1. Describe the applications of data science in a wide range of data-related fields.
  2. Analyse and adapt the latest data science technologies to solve real-world problems in a broad range of sectors.
  3. Formulate appropriate models of data analysis and undertake a project in an independent or collaborative environment
  4. 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.

Admission Requirements

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.

Regulations On Credit Accumulation And Transfer

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:

  1. Must meet the requirements for admission to the Bachelor of Data Science program.
  2. Must obtain and submit an official transcript from the previous university/college indicating academic status, courses offered, credits units completed, and grades obtained.
  3. Will be allowed to transfer/exempt credits earned from the courses described, but only up to 49%.
  4. 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%.
  5. All applications must be accompanied by recommendations from the institution from which he or she is transferring.
  6. The school will evaluate the application and make recommendations to the Sen- ate.

Student Assessment Levels

Student Assessment at programme level

The course will be assessed through:

  1. Content embedded quizzes
  2. Online practical work
  3. Open book tests
  4. Project reports
  5. 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%

Programme Courses

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

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