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Bachelor of Science in Data Science and Analytics (BSDSA)

Course ID
BSCS
Department
Computer Science
Campus
Main Campus
COURSE DESCRIPTION

This course is designed to prepare students for professional careers in machine learning, programming practices, cloud computing, robotics, systems and network administration. The course was designed with the intention of making the program competitive while maintaining the high standard of the program. With the courses offered the students will become highly competitive in the job market and at the same time prepare them in becoming independent in making choices of career and self-employment. It is intended to equip students with both theoretical and mathematical foundations in computing.

 

COURSE OBJECTIVES

The program will enable learning to:

  1. Developing a strong foundation in mathematics, statistics, and computer science: The program aims to equip students with the necessary skills and knowledge in mathematical concepts, statistics, and computer science that are essential in the field of data science.

  2. Acquiring knowledge of data management and manipulation: Students learn how to collect, organize, store, and retrieve data from various sources, including databases, spreadsheets, and other software applications.

  3. Learning data visualization and presentation techniques: Students learn how to present and communicate data insights in a visually appealing and easily understandable manner.

  4. Developing skills in data analysis and modeling: Students learn how to use various data analysis techniques, including regression analysis, machine learning, and data mining, to extract insights from complex data sets.

  5. Understanding business and organizational applications of data analytics: Students learn how to apply data analytics to real-world business problems and understand the impact of data analysis on organizational decision-making.

  6. Building teamwork and collaboration skills: The program encourages students to work in teams, which helps them develop teamwork and collaboration skills, which are essential in the workplace.

  7. Gaining experience with industry-standard tools and technologies: Students are exposed to various data analytics tools and technologies, including programming languages like Python and R, databases, and data visualization software, among others.

Entry requirements

Direct Entry

A candidate shall be deemed eligible for consideration for admission to a first-degree program of the university if the candidate has obtained:

  1. The Uganda Certificate of Education (‘O’ level) or an equivalent qualification,
  2. At least two advanced level passes with physics or mathematics at the same sitting of the Uganda Advanced Certificate of Education (‘A’ level) or equivalent.
  3. Higher Education Certificate in the relevant discipline.

The Mature Age/Special Entry

Candidate for the Mature Age/Special Entry scheme must be Ugandan nationals of at least 22 years and have had formal education. Those who are successful, in both the written and the oral examination, are then considered for admission.

The Diploma Holders Entry

Candidate must be a holder of a relevant diploma in computer science, information technology, or any other related field but should have studied mathematics, from a recognized institution of higher learning.

Course Details

Code Course Name Type LH PH SH CH CU
FIRST YEAR Semester 1            
DSC1101 Introduction to Data Science C 15 30 45 45 3
MTH1102 Discrete Mathematics C 30   45 45 3
CSC1101 Structured Programming C 30 30 45 60 4
ICT1102 Essential Hardware and Software concepts C 30 30 45 60 4
ICT1103 Fundamentals of Computing C 30 30 45 60 4
LNG1101 Writing and Study Skills C 15 30 45 45 3
TBS1103 Understanding the Old Testament C 15 30 45 45 3
  Total credits for Year 1 Semester 1           24
FIRST YEAR Semester 2            
CSC1203 Data Structures and Algorithms C 30 30 45 60 4
CSC2210 Web Programming C 30 30 45 60 4
MTH1203 Probability and Statistics C 30   45 45 3
ICT1205 Database Design and Applications C 30 30 45 60 4
ICT1206 Local Area Computer Networking C 15 30 45 45 3
TBS1201 Understanding the New Testament C 15 30 45 45 3
PBH2108 Health and Wholeness C 15 30 45 45 3
  Total credits for Year 1 Semester 2           24
RECESS Semester 1            
DSC1302 DS Field Attachment I – Workshop Practice C   60 45 45 3
SECOND YEAR Semester 1            
MTH2104 Calculus C 15 30 45 45 3
MTH2206 Linear Algebra C 30   45 45 3
CSC2105 Object Oriented Programming C 30 30 45 60 4
CSC2208 Artificial Intelligence E 30 30 45 60 4
SYE2101 Software Design and Engineering C 15 30 45 45 3
TST2206 Understanding Ethics from a Christian Perspective C 15 30 45 45 3
  Total credits for Year 2 Semester 1           20
SECOND YEAR Semester 2            
DSC2204 Big Data Analytics with R C 30 30 45 60 4
DSC2205 Data Visualisation and Storytelling C 30 30 45 60 4
DSC2207 Data Mining and Wrangling C 30 30 45 60 4
MTH2207 Optimization Theory C 15 30 45 45 3
CSC2214 Computational Research Methods C 15 30 45 45 3
Electives (Select one)            
DSC2206 Time Series Analysis and Forecasting E 15 30 45 45 3
CSC3116 Machine Learning E 30 30 45 60 4
  Total credits for Year 2 Semester 2           21
RECESS Semester 2            
DSC2302 DS Field Attachment II – Internship C   60 45 45 3
THIRD YEAR Semester 1            
DSC3121 DS Research Project I C   60 45 45 3
DSC3114 Scientific writing: reoirting and publishing C 15 30 45 45 3
CSC3218 Deep Learning E 30 30 45 60 4
CSC2209 Database Programming C 30 30 45 60 4
  Specialization tracks (Select two courses from any track)          
  Track 1 Electives (Business Intelligence)          
DSC3108 Big Data Mining and Analytics E 15 30 45 45 3
DSC3110 Business Intelligence E 15 30 45 45 3
  Track 2 Electives (Data Engineering)          
DSC3112 Cognitive Computing E 15 30 45 45 3
DSC3113 Knowledge Engineering E 15 30 45 45 3
  Track 3 Electives (Computational Biology)          
ICT3114 Biological Modeling and Simulation E 15 30 45 45 3
ICT3113 Biostatistics E 15 30 45 45 3
  Total credits for Year 3 Semester 1           20
THIRD YEAR Semester 2            
DSC3221 DS Research Project II C   60 45 45 3
CSC3221 Cyber Threat Intelligence and Data Security C 30 30 45 60 4
ENT3152 Advanced Topics and Technopreneurship C 30   45 45 3
TST3108 Understanding World View C 15 30 45 45 3
  Specialization tracks (Select two courses from any track)          
  Track 1 Electives (Business Intelligence)          
DSC3215 Financial and Risk Analytics E 30 30 45 60 4
DSC3216 Operations-Related Data Analytics E 15 30 45 45 3
DSC3218 Text Analytics and Natural Language Processing E 15 30 45 45 3
  Track 2 Electives (Data Engineering)          
SYE3206 Internet of Things E 15 30 45 45 3
DSC3220 Data Engineering and Data Warehousing E 15 30 45 45 3
DSC3219 Cloud and Distributed Computing E 15 30 45 45 3
  Track 3 Electives (Computational Biology)          
DSC3217 Sequence Analysis E 15 30 45 45 3
ICT3219 Introduction to Bioinformatics E 15 30 45 45 3
ICT3218 Computational Genomics E 15 30 45 45 3
  Total credits for Year 3 Semester 2           19
               
  Total minimum credits required to graduate           134

CU – Credit Unit                                 CH – Contact Hours

LH – Lecture Hours                             PH – Practical Hours

TH – Tutorial Hours                            FH – Field Hours

SH – Student Hours                             NH – National Hours

DNH – Daily National Hours

 

How you study

Blended Approach: Both Physical and online

 

Career opportunities

  1. Machine learning engineers
  2. Data scientists, AI engineers, Database Administrators
  3. Full stack developers
  4. Cloud computing, robotics
  5. Systems and Network Administrators