Introduction
Our B.Sc. Data Science aims to provide a programme of 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. The BSc Data Science will prepare you for further study, or for professional and managerial careers, particularly in areas requiring the application of quantitative skills. The programme also allows you to choose to study a specialist area according to your developing interests and career plans. As a student on the BSc Data Science, student will gain practical skills, theoretical knowledge and related information that will be excellent preparation quantitative careers in a range of industries.
Goals
Students should be able to work on computer
- First-hand experience of carrying out typical workflows of data analytics.
- Learn about acquiring, querying and understanding the basic properties of data, analysis, how to extract insights from data and how to report the results.
- Use and understand classical and modern data-analytics techniques, statistical machine learning and artificial intelligence techniques.
- Competent in computer programming in data-analytic contexts.
- Have a broad range of knowledge useful in data-analytic contexts
- Think in a critical manner.
- Acquire transferable skills in some or all of: presentations, library and internet research, report writing, information technology (IT) expertise and the use of statistical software.
Objectives
- Develop a broad academic and practical literacy in computer science, statistics, and optimization, with relevance in data science and artificial intelligence,
- Provide strong core training so that graduates can adapt easily to changes and new demands from industry.
- Enable students to understand not only how to apply certain data science methods, but when and why they are appropriate.
- Integrate fields within computer science, optimization, and statistics to create adept and well-rounded data scientists.
- Expose students to real-world problems in the classroom and through experiential learning.
- Provide critical methods and techniques to extract relevant and important information from data.
- Prepare graduates for the purpose of self-employment and job placement in government and industries; and
- Develop graduates for professional practice and commitment to lifelong learning.
Course Curriculum
B.Sc. Data Science - 4 YEARS CURRICULUM PLAN
|
S/No. |
Course Code/Course Title |
Units |
|
|
Level 100 First Semester |
|
|
GST111 - Communication in English |
3 |
|
|
GST121 - Use of Library, Study Skills and ICT |
2 |
|
|
GST103- Introduction to Computer |
2 |
|
|
MTH101 - Mathematics I |
3 |
|
|
PHY101 - Physics 1 |
2 |
|
|
PHY107 - Physics 1 Practical |
1 |
|
|
CSC101/201 Problem Solving & Programming -1 |
3 |
|
|
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 |