School of Science & Information Technology (SSIT)
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B.Sc. Data Science
Our B.Sc. Data Science programme aims to provide a study that combines data science, machine learning, statistics and mathematics. The programme uses a rigorous approach, has a mathematical focus and involves applying data science to the social sciences. B.Sc. Data Science will prepare students for further studies or for professional and managerial careers, particularly in areas requiring the application of quantitative skills. The programme also allows students to choose amongst specialized areas according to their developing interests and career plans. As a partaker of the B.Sc. Data Science programme, students will gain practical skills, theoretical knowledge and related information that will be excellent in preparing them for qualitative careers in a range of industries.
Students should be able to efficiently work on a computer, and obtain:
- Firsthand experience of carrying out typical workflows of data analytics
- Knowledge about acquiring, querying and understanding the basic properties of data, analysis, how to extract insights from data and how to report the results
- Ideas on suitable use and understanding on classical and modern data analytics techniques, statistical machine learning and artificial intelligence techniques
- Competence in computer programming in the data analytics contexts
- A broad range of knowledge useful in the data analytics contexts
- The ability to think in a critical manner
- Transferable skills in some, or all of: presentations, library and internet research, report writing, information technology (IT) expertise and the use of statistical software.
The objective of this programme is to develop in its students and graduates:
- A broad academic and practical literacy in computer science, statistics, and optimization, with relevance in data science and artificial intelligence
- A strong and core training so that graduates can adapt easily to changes and new demands from industry and society
- An understanding of not only how to apply certain data science methods, but also when and why they are appropriate
- Integrate field qualities within computer science, optimization and statistics to create adept and well-rounded data scientists
- Suitable exposure to real-world problems in the classroom and through experiential learning
- Critical techniques and methods to extract relevant and important information from data
- Adequate preparations for the purpose of self-employment and job placements in government and industries
- The eligibility and qualities for professional practices and commitment to a lifelong learning.
B.Sc. Data Science - 4 Years Curriculum Plan
S/No. | Course Code/Course Title | Units |
| Level 100 First Semester |
|
| 1. | GST111 - Communication in English | 3 |
| 2. | GST121 - Use of Library, Study Skills and ICT | 2 |
| 3. | GST103 - Introduction to Computer | 2 |
| 4. | MTH101 - Mathematics I | 3 |
| 5. | PHY101 - Physics 1 | 2 |
| 6. | PHY107 - Physics 1 Practical | 1 |
| 6. | CSC101/201 - Problem Solving & Programming -1 | 3 |
| 8. | CHM101 - General Chemistry –I | 2 |
9. | CHM107 - Experimental Chemistry –I | 1 |
| Total | 19 |
| Level 100 Second Semester | 2 |
1. | GST 112 - Logic, Philosophy and Human Existence | 2 |
2. | GST123 - Basic Communication in French | 2 |
3. | GST113 - Nigerian People and Culture | 3 |
4. | MTH102 - Mathematics II | 2 |
5. | PHY102 - Physics 2 | 1 |
6. | PHY108 - Physics 2 Practical | 3 |
7. | CYB102 - Fundamentals of Cyber Security I | 2 |
8. | BIO101 - General Biology I | 1 |
9. | BIO103 - General Biology Practical I | 2 |
Total | 18 | |
Level 200 First Semester | ||
1. | GST 225 - Contemporary Health Issues | 2 |
2. | GST 224 - Leadership Skills | 2 |
3. | GST223 - Introduction to Entrepreneurship Skills | 2 |
4. | CSC202 - Object Oriented Programming (Programming -2) | 3 |
5. | STA102 - Statistics for Data Science & Engineering | 3 |
6. | DSC201 - Advanced Web Technology | 2 |
7. | CSC304/404 - Database Management Systems | 3 |
8. | CSC208 & MTH103 - Discrete Structures & Linear algebra | 3 |
| Total credit | 20 |
| Level 200 Second Semester |
|
1. | GST211 - Environment and sustainable development | 2 |
2. | GST 222 - Peace and Conflict resolution | 2 |
3. | GST 226 - Entrepreneurship | 2 |
4. | MTH201 - Computational Science & Numerical Methods | 2 |
5. | CYB205 - Introduction to Digital Forensics | 2 |
6. | CSC314&315 - Computer Organization & Architecture | 3 |
7. | CSC205 - Operating Systems | 3 |
8. | CSC204 - Data structures | 3 |
| Total credit | 19 |
| Level 300 First Semester |
|
1. | CSC411 - Artificial Intelligence and Expert Systems | 3 |
2. | CYB204 - System & Network Administration | 2 |
3. | CYB206 & 208 - Enterprise Perimeter Security & Information Security Policy | 3 |
4. | SEN201 - Introduction to Software Engineering | 2 |
5. | SEN405 - Research Methods | 1 |
6. | CSC310 - Algorithms & Complexity Analysis | 2 |
7. | CSC321 - System Analysis and Design | 2 |
8. | CSC423 - Computer networks | 3 |
| Total credit | 18 |
Level 300 Second Semester | ||
1. | CSC299/399 - SIWES (Students Industrial Work Experience Scheme) / Industrial Attachment | 6 |
| Total Credit | 6 |
| Level 400 First Semester |
|
1. | CYB301 - Software Defined Network & Security | 2 |
2. | CYB303 - Cryptographic Techniques | 2 |
3. | CYB305 & 309 - Biometrics & Systems Security | 3 |
4. | DSC407 - Data Science for Cyber security | 3 |
5. | CYB310 - Information Security Engineering | 2 |
6. | CYB346 - Ethical Hacking | 2 |
| Total Credit | 14 |
Level 400 Second Semester |
| |
1. | DSC402 - Health Analytics | 2 |
2. | CYB405 - IoT and Cloud Data Management | 3 |
3. | DSC404 - Business Analytics | 2 |
4. | CSC499 - Project Work (IT) | 6 |
5. | CYB407 - Software Security and Information Disaster Recovery | 3 |
Total Credit | 16 | |
| Total Credit for the Programme | 132 |