Faculty of Computing

Academic Prospectus & Programme Overview

1. Introduction to the Programme

The B.Sc. Cyber Security programme focuses on equipping students with sufficient knowledge, tools, and technical skills to minimize, prevent, and defend against cybersecurity threats and incidents. It prepares students to gather, analyze, and present digital evidence, understand the impact of cybercrime on business and society, and implement security practices to safeguard computer-based systems and cyberspace.

Philosophy

The philosophy of the programme is to build capacity and human capital in the field of cybersecurity to safeguard business transactions, corporate assets, critical infrastructure, and all operations in cyberspace, nationally and globally.

Objectives

The broad objectives of the programme are to:

1. Produce graduates with foundational knowledge, skills, and strategies to detect and prevent cyber-fraud.
2. Empower graduates to analyze cybersecurity threats, attacks, and risks, and develop supportive software agents/codes to address threats.
3. Provide knowledge of cryptography, steganography, and digital forensic science techniques for detecting cybercrimes and ensuring information privacy.
4. Develop critical thinking skills regarding cyber intelligence and enable students to implement strategic cybersecurity tactics.
5. Prepare graduates for self-employment, cybersecurity-based job placement, and professional practice in government and industry.

1. Entry Requirements

UTME Admission Pathway (4-Year Degree Programme)

• Candidates must possess five Senior School Certificate (SSC) credit passes including English Language, Mathematics, Physics, and any other relevant Science subjects in not more than two sittings, alongside an appropriate UTME score.

Direct Entry (DE) Pathway (3-Year Degree Programme)

• Candidates must possess a minimum of a credit at University/National Diploma (ND) or NCE, along with five SSC credit passes in relevant science subjects, three of which must be English Language, Mathematics, and Physics.

1. Programme Courses (100L to 400L)

100 Level

Total 100L Units: 23

200 Level

Total 200L Units: 22

300 Level

Total 300L Units: 21

400 Level

Total 400L Units: 21

1. Career Prospects and Employability

The programme equips graduates with soft, technical, and implementation skills essential for identifying vulnerabilities and defending digital infrastructures. Career options across military, government, corporate, and private sectors include:

• Information Security Analyst / Officer: Monitoring, detecting, and responding to security incidents and vulnerabilities.
• Ethical Hacker / Penetration Tester: Conducting vulnerability assessments and simulating cyber-attacks to strengthen security architecture.
• Digital Forensics Investigator: Gathering, analyzing, and preserving digital evidence for legal and corporate investigations.
• Cyber Threat Intelligence Analyst: Analyzing advanced persistent threats (APTs), malware campaigns, and emerging cyber risks.
• Security Architect / Cloud Security Specialist: Designing, implementing, and auditing secure network infrastructures and cloud environment controls.
• Cryptographer / Data Privacy Engineer: Developing cryptographic algorithms and data protection protocols to maintain confidentiality and integrity.
• Cybersecurity Entrepreneur: Providing independent consultancy, vulnerability management services, or security solution development

Academic Prospectus & Programme Overview

1. Introduction to the Programme

The B.Sc. Artificial Intelligence programme teaches the essential ideas of Artificial Intelligence, emphasizing core elements of computing, machine learning, natural language processing, computer vision, and robotics. It provides students with a solid understanding of AI concepts and equips them with practical programming skills (Python, Java, C++) and AI framework expertise to develop solutions for societal challenges in healthcare, education, environmental sustainability, and futuristic developments.

Philosophy

The philosophy of the Artificial Intelligence programme is to provide a broad and high-quality education emphasizing the theoretical and practical foundations of machine intelligence that guide the design, implementation, and application of intelligent nonhuman systems.

Objectives

The specific objectives of the programme are to:

1. Create in students the awareness of and enthusiasm for Artificial Intelligence and its capabilities.
2. Provide students with a broad and balanced foundation of Artificial Intelligence knowledge and practical skills.
3. Prepare students to formulate real-world problems in AI, employ problem-solving skills, and use appropriate tools to obtain valid solutions.
4. Develop students' abilities in self-management and teamwork.
5. Prepare students to be proficient, professional, and ethical in their careers.
6. Prepare students to communicate effectively both orally and in writing.
7. Develop the ability to engage in lifelong learning and growth in AI and to become potential job creators.

1. Entry Requirements

UTME Admission (4-Year Degree Programme)

• Candidates must possess credit level passes in five Senior Secondary Certificate (SSC) subjects, including English Language, Mathematics, Physics, and two other relevant Science subjects, at not more than two sittings, in addition to an appropriate score in UTME.

Direct Entry (DE) Mode (3-Year Degree Programme)

• Candidates must possess passes in Mathematics and Physics at GCE Advanced Level, IJMB, or JUPEB, in addition to credit passes in five SSC subjects (English Language, Mathematics, Physics, and two other relevant Science subjects) at not more than two sittings.
• Holders of NCE or National Diploma (ND) at a minimum of Credit level in a relevant discipline are also eligible for Direct Entry into 200 Level.

1. Programme Courses (100L to 400L)

100 Level

Total 100L Units: 23

200 Level

Total 200L Units: 25

300 Level

Total 300L Units: 25

400 Level

Total 400L Units: 14

1. Career Prospects and Employability

The courses in this programme are tailored to equip graduates with high-demand technical and soft skills, including programming, critical thinking, organization, communication, teamwork, and technology self-management.

Graduates of B.Sc. Artificial Intelligence are equipped for careers across tech sectors, research labs, healthcare, finance, and industrial automation. Key career paths include:

• Machine Learning / Deep Learning Engineer: Designing, training, and deploying statistical models and neural network architectures.
• Computer Vision Specialist: Developing image recognition, spatial analysis, and object tracking systems.
• NLP Engineer: Building natural language processing systems, language models, conversational AI, and text analytics tools.
• AI Data Scientist / Analyst: Extracting actionable insights from complex datasets and integrating machine intelligence into decision-making platforms.
• Robotics & Automation Engineer: Designing intelligent non-human control systems and autonomous agent workflows.
• AI Ethics & Governance Specialist: Auditing algorithmic bias, data privacy, compliance, and assessing societal impacts of deployed AI applications.
• Tech Entrepreneur / Innovation Specialist: Launching AI-driven ventures or creating novel AI products.

Academic Prospectus & Programme Overview

1. Introduction to the Programme

The B.Sc. Data Science and Analytics programme focuses on equipping students with computational, statistical, mathematical, and data management skills to harvest actionable intelligence from massive datasets. The curriculum emphasizes model building, model assessment, data curation, analytics, and knowledge transference to drive data-driven decision-making in modern organizations.

Philosophy

The philosophy of the programme is to equip students with the foundations, methods, and practical implications of data science—extracting knowledge from data using concepts and theories from mathematics, statistics, computing, and information technology.

Objectives

The specific objectives of the B.Sc. Data Science and Analytics programme are to:

1. Provide students with skills to analyze computationally intensive real-life problems and develop effective data-driven solutions.
2. Imbibe expertise across the four key pillars of data: architecture, acquisition, analysis, and archiving.
3. Train students to uncover hidden insights within large-scale data in business, healthcare, government, and education sectors.
4. Integrate data-driven IT solutions with business processes to help organizations achieve strategic goals efficiently.

1. Entry Requirements

UTME Admission Pathway (4-Year Degree Programme)

• Candidates must possess five Senior School Certificate (SSC) credit passes including English Language, Mathematics, Physics, and any other relevant Science subjects at not more than two sittings, along with an acceptable UTME score.

Direct Entry (DE) Pathway (3-Year Degree Programme)

• Candidates must possess a minimum of a credit pass at University/National Diploma (ND) or NCE, along with five SSC credit passes in relevant science subjects (three of which must be English Language, Mathematics, and Physics).

1. Programme Courses (100L to 400L)

100 Level

Total 100L Units: 23

200 Level

Total 200L Units: 28

300 Level

Total 300L Units: 21

400 Level

Total 400L Units: 15

1. Career Prospects and Employability

Graduates of B.Sc. Data Science and Analytics are equipped with analytical, statistical, and programming competencies required for high-demand roles across public and private sectors. Career opportunities include:

• Data Scientist / Data Analyst: Uncovering actionable patterns, training predictive models, and analyzing big data for strategic planning.
• Data Engineer / Data Architect: Building, maintaining, and optimizing scalable data pipelines, data warehouses, and Big Data infrastructures.
• Business Intelligence (BI) Developer: Developing interactive dashboards, visual reports, and data pipelines to support enterprise reporting and decision-making.
• Machine Learning Specialist: Designing and implementing predictive models and data-driven algorithms for business applications.
• Big Data Specialist: Managing fast-streaming data and deploying distributed computing systems like Hadoop, Apache Spark, and NoSQL databases.
• Data Governance & Ethics Consultant: Ensuring compliance with data privacy laws, data stewardship rules, and ethical standards in automated systems.