Baze University

Software Engineering

About the course

The Bachelor of Science (Software Engineering) programme teaches you how to apply proper engineering techniques to software development projects, building effective software products for desktops and mobile devices.
Information communication technology (ICT) continues to play a crucial part in effective computer systems that can meet increasing user demands.


We live in a world dominated by online services and social interactions, so the ICT systems behind them must be responsive, reliable and secure. This practical course covers the development and use of ICT systems in business and industry, and focuses on core business technologies.

While we concentrate on developing the skills you need to understand an organisation's information and technical needs, you'll also study elements of computer programming and gain additional skills for a career in technical and end-user support, or application development.

The programme philosophy is that computing and information systems concepts and practices have to be complemented by organisational and management concepts and practices in order to be effective. The programme provides students with theoretical knowledge and practical skills in both areas, enabling graduates to contribute to the effective development and exploitation of information systems and technology in companies, organisations and society as a whole.

It is recognised that today's skills are often transitory, especially in computing and information systems. Therefore the programme strikes a balance between learning current skills and emphasizing the underlying theories which justify the choice of one skill set rather than another. The fundamental theories last longer than particular skills and provide a sound basis for understanding and evaluating new developments in computer science and information systems.

Our Software Engineering programme provides a strong technical basis, together with a functional management focus, ensuring graduates have the right combination of business awareness and technical ability.

The programme is designed to enable you, as a software engineer, business analyst or consultant, to leverage the use of computing systems within organisations.

What you will learn

The course aims:
with a strong focus on programming, this course includes software development, database and networking units.
covered technologies include Java, Android, Oracle SQL, PHP, Web API's, JSON, HTML5, jQuery and big data analytics;
students work on a range of projects, producing a portfolio of completed work which they can refer back to throughout their career.;
students learning is supported by the use of industry standard facilities. These include high-spec IT suites and a professional usability lab;
this programme includes optional units in the second and third year, enabling students to develop specialisms and improve their career prospects;
to provide the foundation for a professional career in the computing-based industries, including telecommunications, process control, business-, mission-, and safety-critical fields;
to enhance the skills of a professional who is already working in one of these industries;
to present knowledge, experience, reasoning methods and design and implementation techniques that are robust and forward-looking.

Graduate destinations

Programmer
Software Engineer
Program Analyst
System Analyst

Course Details

Course Structure
Year 1 | Semester 1
Code: GEN103
Lecturer: Mercy Johnson
Unit: 3
Prerequisite: No Prerequisite
Overview:

This module will introduce students to basic mathematical topics useful in their different courses of study.

Aims:

To introduce students to basic mathematical topics useful in their different courses of study at Baze University. Apart from learning the basic statistical tools useful for data collection, they will also gain valuable insight into number system, the concept of sets, laws of indices, solving equations and a wide range of other basic mathematical techniques. In essence, this module is designed to equip students with useful methods of solving and approaching mathematical problems.

Syllabus:

Introduction to Number System, Laws of Indices, General Inequality, Equation Systems, Algebra, Sequences and Series, Trigonometry as well as general overview of Statistics.

Teaching and learning methods:
  • Lectures: Lectures will be used to introduce and explain major ideas and theories and to illustrate their wide-ranging applications. 
  • Interactive lectures will review materials by encouraging their active participation - inviting questions, working through examples, giving short quizzes, discussing case studies, or showing a  video followed by a quiz, etc.
  • Classes: This will encourage students to begin to apply the knowledge gained to real and hypothetical cases and will encourage them also to gain confidence in presenting and defending their own ideas. Classes will usually require them to read some material(s) for discussion, or prepare answers, give some presentations, research a topic, take part in a debate, etc. 
  • Homework: Homework will be assigned regularly. Regular assignments will help them understand the material and they will get feedback.

Intended learning outcomes:

On the  successful completion of this module, students are expected to have developed their skills and have:

  • Ability to read and understand fundamental mathematics.
  • Ability to apply range of concepts in Mathematics or represent and solve problems in Mathematics.
  • Ability to represent and analyse data using the right techniques.


Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:
  • Basic College Mathematics by Elayn Matin-Gay, New Jersey, Pearson Prentice Hall.
  • College Mathematics for Business, Economics, Life Sciences & Social Sciences (11th Edition) by Raymond A. Bernet, Michael R, Ziegler, & Karl E. Byleen. New Jersey, Pearson Prence Hall.
  • Algebra & Trigonometry (Sixth Edition) by Michael Sullivan. Prentice Hall, Upper Saddle River, New Jersey 07458.
  • Any other mathematical textbook that covers any of the topics.

Code: PHY107
Lecturer: Babangida Babaji Abdullahi
Unit: 1
Prerequisite: No Prerequisite
Overview:

General Physics 1 practical is the laboratory section that cover all the topics taught in General Physics 1 (PHY101).

Aims:

The aim of this module is to assist students with the practical of all the topics (mechanics, heat and optics)

Syllabus:

The experiments include: Mechanics: timing experiments, simple pendulum, compound pendulum, measurement of g, moments, determination of moment of inertia, measurement of viscosity, use of force board, law of momentum. Optics: reflection using plane mirror, convex/concave mirror, concave/convex lens, refraction using a prism, critical angle, apparent depth/real depth, simple microscope, compound microscope.Heat: measurement of specific heat capacity of water and a solid, expansion of gas experiment using a long capillary tube, Joule’s law.

Teaching and learning methods:

This module is a purely experimental. Each experiment will be accompanied with laboratory manual. Students will be taken through the lab sections by Technologists and the module instructors. The students will then submit their laboratory reports for assessment.

Intended learning outcomes:

At the end of the module, students will be equipped with report writing skill. They will also understand the practical of what have been discussed in PHY101 class.Fundamentals of Physics by David Halliday, Robert Resnick and Jearl Walker, Vol. 1 8th Ed. Wiley (2007)
University Physics by Young Freedman, vol. 1 13th Ed. Addison-Wesley

Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:
  • Fundamentals of Physics by David Halliday, Robert Resnick and Jearl Walker, Vol. 1 8th Ed. Wiley (2007)
  • University Physics by Young Freedman, vol. 1 13th Ed. Addison-Wesley

Code: COM112
Lecturer: Florence Peter
Unit: 3
Prerequisite: No Prerequisite
Overview: NIL
Aims: NIL
Syllabus: NIL
Teaching and learning methods: NIL
Intended learning outcomes: NIL
Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list: NIL
Code: GEN107
Lecturer: James Daniel
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: MTH102
Lecturer: Dr. Samson Bolarinwa Bolaji
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: PHY101
Lecturer: Shehu, Muhammad Shafi'u
Unit: 3
Prerequisite: No Prerequisite
Overview: General overview of the module, module description and students - instructor introduction.
Aims: To aid students to understand the broad-based fundamental principles of the physical world. This module will on the practical applications of everyday experience and industrial processes. 
Syllabus:
  • Measurement in physical world
  • One dimensional kinematics - distance, displacement, speed, velocity, acceleration, uniform, motion, free fall.
  • Vector and scalar - vector addition, subtraction, division, multiplication and applications.
  • Problem solving section.
  • Two-dimensional kinematics - position, displacement, velocity, acceleration and projectile.
  • Fundamental laws of Mechanics.
  • Problem solving and mid-term exam
  • Work, energy and power.
  • Temperature and heat.
  • Introduction to thermodynamics.
  • Hydrostatics.
  • Problem solving.
  • Elasticity.
  • Problem solving
Teaching and learning methods: Lectures: This will be used to introduce the module and explain major concepts of the fundamentals to students. The theories (equations) and their applications will be illustrated in this section.

Interactive Lectures: This section of the teaching will allow active student - instructor interactions. The instructor and students ask more questions and solve more examples.

Classes/Tutorials: Tutorial sections will encourage you (students) to begin to gain confidence in solving difficult problems. The students are required to prepare any difficult problems they are unable to solve on their own for discussion.

Class-work/Homework: Class-work and Homework will be assigned regularly. Students' answers to class-work and homework should be clear, concise and correct. Students will receive feedback on the homework and class-work.
Intended learning outcomes: Students are expected to develop the necessary skills required to solve fundamental problems in physics. This will enable them prepare for further studies in respective field.
Assessment:
Exams: 60%
Test: 25%
Quiz: 5%
Coursework: 10%
Recommended reading list:
  • Fundamentals of Physics by David Halliday, Robert Resnick and Jearl Walker, Vol. 1 (8th Ed.) Wiley (2007)
  • University Physics by Young Freedman, vol 1 (13th Ed.) Addison - Wesley
Code: GEN101
Lecturer: Andrew Bula
Unit: 3
Prerequisite: No Prerequisite
Overview:

NIL

Aims:

NIL

Syllabus:

NIL

Teaching and learning methods:

NIL

Intended learning outcomes:

NIL

Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:

NIL

Year 1 | Semester 2
Code: PHY108
Lecturer: Babangida Babaji Abdullahi
Unit: 1
Prerequisite: No Prerequisite
Overview:

General Physics 2 practical is the laboratory section that cover all the topics taught in General Physics 2 (PHY102).

Aims:

The aim of this module is to assist students with the practical of all the topics (Electricity, magnetism, vibration and waves)

Syllabus:

Electricity: Ohm’s law, heating effect of a current, internal resistance of a cell, meter/Wheatstone Bridge, potentiometer measurement of ece, plotting of magnetic field. Sound: resonance tube, sonometer.

Teaching and learning methods:

This module is a purely experimental. Each experiment will be accompanied with laboratory manual. Students will be taken through the lab sections by Technologists and the module instructors. The students will then submit their laboratory reports for assessment.

Intended learning outcomes:

At the end of the module, students will be equipped with report writing skill. They will also understand the practical of what have been discussed in PHY101 class.

Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:
  • Fundamentals of Physics by David Halliday, Robert Resnick and Jearl Walker, Vol. 1 8th Ed. Wiley (2007)
  • University Physics by Young Freedman, vol. 1 13th Ed. Addison-Wesley

Code: GEN108
Lecturer: Mercy Johnson
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: COM102
Lecturer: Chandrasekhar Uppin
Unit: 3
Prerequisite: No Prerequisite
Overview:

The purpose of this module is to provide students with a coherent knowledge of problem solving techniques and tools for designing defined and undefined problem structure. Practicing with the various kinds of problems by applying appropriate logic, strategies and suitable techniques to find computational/logical problems solutions in academic environment. 

Aims:

Aim to introduce this module to build the problem solving skills with creativity, self-analysis ability  and smakes the students a better problem solver in general irrespective of their stream of study. designed this course in hopes of 

  • Creative, Self-understanding ability 
  • Ability to design and plan for desired solutions  
  • Build the analytical skill and cogent abilities
  • Understand how student operates as individual problem solver in academic and personal. 
  • Recognize limitations and pitfalls.
  • Learn the various techniques that can apply to solve problems.

Eventually, Improve ability of successful problem solver in the life.  


Syllabus:

Introduction to problem solving, problem solving strategies, problem reduction strategies, problem Solving process stages, various kinds of problems and examples. Defined and undefined problem structure, Instructions and operators involved in problem solving process, distinguish between exercises solving and problem solving. Characteristics of good and successful problem solvers. Mental blocks and overcome strategies with good practices. Problem solving techniques and tools (Algorithms and Flow-Charts), Selection (decision) and Iteration constructs. Analogies and Logic and invariant type of problems with examples.         

Teaching and learning methods:

  • In learning Process incorporate various kinds of problems to solve regularly in the classroom so students become comfortable with it.
  • Teach problem solving process, steps and strategies.
  • Use real-life situation with data and integrate other content areas into the problems as much as possible.
  • In Interactive lectures ample use  of word problems, charts, tables algorithms, flowcharts to introduce units of study and practices not just at the end.
  • Understanding and practices of problem solving process stages using structured problem solving with various techniques. (Algorithms and Flowcharts are practiced). 
  • In Workshops proving ample hands on work (Individual/Group) and assignments will be practiced regularly to test student creativity and memory managing abilities. 


Intended learning outcomes:

On the successful completion of this module the student should understand and be able to:-

  • Understand the areas need to be improving within.
  • Learn about self and work towards improving self-management.
  • Developing creative, innovative practical solutions
  • Learn problem-solving techniques using appropriate tools. Applying a range of strategies to problem solving
  • Solve a wide variety of problems, so as to learn how to apply the techniques
  • Understand distinguish between Exercise solving and Problem solving.
  • Identify skills and personality traits of successful problem solvers. Applying problem solving strategies across a range of areas.


Assessment:
Exams: 70%
Test: 15%
Quiz: 5%
Coursework: 10%
Recommended reading list:

Gough, J. (1998). Devil's Advocacy as Critical Research Methodology: Spatial Thinking as a Case Study, "Sixth Contemporary Approaches to Research in Mathematics, Science, Health and Environmental Education", pp. 1-25, Melbourne. 

1st Edition: Crebert, G., Patrick, C.-J., & Cragnolini, V. (2004). 

2nd Edition: Crebert, G., Patrick, C.-J., Cragnolini, V., Smith, C., Worsfold, K., & Webb, F. (2011). Problem Solving Skills Toolkit. (Retrieved from the World Wide Web 4th April, 2011) http://www.griffith.edu.au/gihe/resources-support/graduate-attributes

Computer Science Vol-I by. C V Uppin  and Class Hand Outs (PPTs)

Web resources :

  • Indiana University - Kirkley, J. (n.d.). Principles for Teaching Problem Solving. (Retrieved from the World Wide Web on 1 September, 2004) http://www.plato.com/downloads/papers/paper_04.pdf 
  • University of New England - Malouff, J. (2010). Fifty Problem Solving Strategies Explained. (Retrieved from the World Wide Web on 22 December, 2010) http://www.une.edu.au/bcss/psychology/john-malouff/problem-solving.php 
  • Virtual Salt – Harris, R. (2002). Problem Solving Techniques. (Retrieved from the World Wide Web on 22 December, 2010) http://www.virtualsalt.com/crebook4.htm 
  • University of Delaware. (n.d.). Problem-based Learning.(Retrieved from the World Wide Web on 22 December, 2010) http://www.udel.edu/pbl/ .


Code: MTH103
Lecturer: Mmaduabuchi Okpala
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: PHY102
Lecturer: Joseph Asare
Unit: 3
Prerequisite: Physics 1 ,
Overview:

The subject of electromagnetism is a combination of electrostatics phenomena, magnetism, and current electricity. These must have seemed at one time to be entirely different phenomena until in 1829 when Oersted discovered that an electric current is surrounded by a magnetic field. The basic phenomena and the connections between these three disciplines were ultimately described by Maxwell towards the end of the nineteenth century in four famous equations called the Maxwell's Equations. The course acquaints the student with concepts of electric and magnetic fields associated with particles and how these are affected in the presence of other particles.

Aims:

The aim of this module is to aid students in understanding the broad-based fundamental principles of electricity and magnetism by emphasizing on applications associated to industrial processes and everyday experiences.

Syllabus:

Electrostatics.

Conductors and Currents.

Magnetism.

Maxwell's Equations.

Electromagnetic Waves and Oscillations.

Teaching and learning methods:

  • Lectures: This will be used to introduce the module and explain major concept of the fundamentals to students.
  • Interactive Lectures: This section of the teaching will allow active student-instructor interactions.
  • Classes/Tutorials: Tutorial sections will build confidence in students and encourage participation in problem solving.
  • Class-work/Homework: Class-work and Homework will be assigned regularly. Students will received feedback on the homework and class-work for improvement.

Intended learning outcomes:

The theories and their applications illustrated in this module should expose students to the required foundational knowledge in Electromagnetism required for higher education in the department. 

Assessment:
Exams: 60%
Test: 20%
Quiz: 5%
Coursework: 15%
Recommended reading list:

  1. Young, H. D., & Freedman, R. A. (2015). University Physics with Modern Physics and Mastering Physics. Academic Imports Sweden AB.
  2. Serway, R. A., Beichner, R. J., & Jewett, J. W. (2000). Physics for scientists and engineers with modern physics.
  3. Paul E. Tippens. (2007). Electricity and Magnetism Lecture Notes. Southern Polytechnic State University.
  4. Lisa Jardine-Wright. (2008). Introduction to Electricity and Magnetism. Cavendish Labrotory.

Code: GEN104
Lecturer: Omojuyigbe Abosede
Unit: 3
Prerequisite: Use of English 1 ,
Overview:

In this module, students will learn to write well structured essays, overcome speech anxiety, work effectively in groups , the art of public speaking and give well structured presentations

Aims:

The aim of the module is to teach students the rudiments of public speaking, team work  and  presentations.

Syllabus:

Reading comprehension, Literary appreciation, Writing skills, Presentation skills, Working in groups for a presentation, Preparing for assessed presentation.

Teaching and learning methods:
  • Lectures will be given through power point presentations to explain the topics contained in the syllabus.
  • Class discussions will also be used to enhance individual participation, self confidence and team work as the students will be required to give presentations fortnightly


Intended learning outcomes:

Students who have taken this module should be able to:

  • Read effectively
  • Write well structured essays
  • Work effectively in a group or team
  • Carry out researches independently
  • Give good presentations


Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:
  • Turner, Kathy et al., Essential Academic Skills,[ Oxford University Press,  Oxford ,2011]
  • Kathleen T. McWhorter,  Academic Reading,  [ HarperCollins College Publishers, 1994]
  • Seely, John, Oxford Guide to Effective Reading and Speaking, [ Oxford University Press, Oxford, 2005]

Year 2 | Semester 1
Code: COM211
Lecturer: Moses Obioma Ubaru
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: GEN203
Lecturer: Abdulmunin Saad
Unit: 15
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: GEN201
Lecturer: Shulammite Paul
Unit: 15
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: MTH201
Lecturer: Dr. Samson Bolarinwa Bolaji
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: COM201
Lecturer: Mubaraka Sani Ibrahim
Unit: 15
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: COM203
Lecturer: Omonayin Esther T.
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: STA204
Lecturer: Mmaduabuchi Okpala
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Year 2 | Semester 2
Code: COM207
Lecturer: Charles Isah Saidu
Unit: 3
Prerequisite: No Prerequisite
Overview: Fundamental algorithms, analysis techniques, efficiency of algorithms, discover and design of algorithms, basic data structures, hash tables, greedy algorithms, divide-and-conquer, sequential versus recursive algorithms, sorting algorithms, search algorithms, graph search, shortest path.
Aims: The aim of this module is to introduce the concepts of algorithms, algorithm design and analysis of algorithms and also to identify and describe different algorithm types with their examples.
Syllabus: Fundamentals of Algorithmic Problem Solving. Fundamental Data Structures. Fundamentals of the Analysis of Algorithm Efficiency. Divide and Conquer Algorithm. Greedy Algorithm
Teaching and learning methods:
  • Lectures will be used to introduce and explain major ideas, theories and to illustrate their wide-ranging applications.

  •  Interactive lectures will review materials by encouraging the student active participation - inviting questions, working through examples, giving short quizzes, discussing case studies etc. 

  •  Classes will encourage the student to apply the knowledge gained to real and hypothetical cases and will encourage students to gain confidence in presenting and defending their own ideas. Classes will usually require the student to read some material for discussion, or prepare answers, give some presentations, research a topic etc.

  •  Class-work and Homework will be assigned regularly. These will help the students to understand the material .The answers to the homework should be clear, concise and correct.
Intended learning outcomes: On the successful completion of this module the student should understand and be able to:-
  • Understand the concepts of different Algorithms. 
  • Understand the different algorithms and their features. 
  • Design and Analyse algorithms efficiently. 
  • Have a good knowledge on how to implement pseudo codes of these algorithms using a programming language. 
  • Understand the different applications of these algorithms.
Assessment:
Exams: 60%
Test: 20%
Quiz: 10%
Coursework: 10%
Recommended reading list:
  1. Levitin, A. (2011).Introduction to Design and Analysis of Algorithms.3 rd ed. Pearson.
  2.  Cormen, T., Leiserson,C., Rivest R.& Stein, C. (2009).Introduction to Algorithms. 3 rd ed. MIT Press. 
  3.  Cormen, T. (2013).Algorithms Unlocked. MIT Press. Cambridge. 
  4.  Storer,J. (2002).An Introduction to Data Structure and Algorithms.1 st ed. Springer-Verlag.
Code: PHY202
Lecturer: Shehu, Muhammad Shafi'u
Unit: 3
Prerequisite: General Physics 1 (Practical) , General Physics 2 (Practical) ,
Overview:

Students will be told of their expectations from instructor, while the rules and regulations will be highlighted.

Aims:

To aid students to understand the fundamentals of circuit analysis techniques. The emphasis in this module will be on the practical application to everyday experiences and industrial processes.

Syllabus:

• General overview of the module, module description and students -instructor introduction.
• Current, resistor, voltage and Ohm’s law. Source emf and current, practical
• Connection of resistors in circuits- Series and parallel connection of resistors and emf sources. Practical
• Kirchoff’s law – Kirchoff Current Law (KCL) and Kirchoff Voltage Law (KVL)
• Applications of Kirchhoff’s law in circuit. Practical
• D.C – network analysis and circuit theorems (Thevennin and Norton)
• Network analysis – Superposition and maximum power transfer theorems. Practical
• Inductance, capacitance and transformer. Practical
• D.C. sinusoidal waveform run and peak values, power, impedance and admittance series.
• Series R-L, R-C and RLC circuits. Practical
• A.C – network analysis and circuit theorems
• Semiconductors, p-n junction
• Field effect transistor and bipolar transistor
• Characteristics and equivalent circuits, amplifiers, feedback and oscillators

Teaching and learning methods:

• Lectures: The concepts of the fundamentals are described to students.
• Interactive Lectures: There will be active student-instructor interactions.
• Classes/Tutorials: Tutorial sections will encourage you (students) to begin to gain confidence in solving difficult problems.
• Class-work/Homework: Class-work and Homework will be assigned regularly. Students’ answers to class-work and homework should be clear, concise and correct. Students will receive feedback on the homework and class-work.
• Practical: Students will be taken through hands-on experiments that are related to the theories learnt in class.

Intended learning outcomes:

Students are expected to be able to design simple circuits and analysis complex circuits using different types of methods and theorems.

Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:

• Fundamental of Electric Circuits, 2012 by Charles Alexander and Matthew Sadiku.
• Introductory Circuit Analysis, 13th Edition, 2015, by Robert L. Boylestad.
• Basic Engineering Circuit Analysis, 2005 (8th Edition) by David Irwin and Mark Nelms.
• Electronics Fundamentals: Circuits, Devices and Applications, 8th Edition, 2009, by Thomas L. Floyd and David M. Buchla.

Code: COM205
Lecturer: Nasiru Aboki
Unit: 5
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: COM210
Lecturer: Julius Makinde
Unit: 2
Prerequisite: No Prerequisite
Overview:

Computer system is divided into software and hardware. Operations are built on systems. A system can be very much understood when the physical components are identified. This module will focus on the hardware of a computer.


Aims:

  • Provide students with a sound knowledge of the basic design of a computer system. 
  • To explore how the different parts of a computer system are connected together.
  • Equip students with the capabilities of assembling a computer (Desktop & Laptop).
  • Provide students with basic tools for troubleshooting and upgrading a computer.
  • Introduce students to system software


Syllabus:

Major components of a computer, Mother board design and form factors, Components of Motherboard, BIOS, Memory modules, Assembling the various components, Expansion cards, External components, Computer peripherals, Use of diagnostic and evaluation software.

Teaching and learning methods:
  • Lectures will be used to explain the content of the syllabus.
  • Class discussion will be used to stimulate students’ participation and sharing of knowledge to develop their communication skill. 
  • There will be hands on experience in the laboratory to enhance the understanding of most of the topics taught in the classroom

Intended learning outcomes:

  • On completion of this module, student should be able to:
  • Identify the different physical components of a computer
  • Explain different types of input and output devices of a computer 
  • Distinguish between system software and application software
  • Upgrade a computer to higher capability
  • Assemble and disassemble a computer
  • Troubleshoot and fix malfunctioning computer


Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:

PC Hardware: A Beginner’s Guide (RON GILSTER), IT Essentials: PC Hardware and Software Companion Guide Third Edition (David Anfinson • Ken Quamme), Complete CompTIA A+ Guide to IT Hardware and Software (Cheryl A. Schmidt)

Code: COM208
Lecturer: Ruqayya Mohammed
Unit: 9
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Year 3 | Semester 1
Code: COM312
Lecturer: Peter Ogedebe
Unit: 10
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: COM212
Lecturer: Moses Obioma Ubaru
Unit: 0
Prerequisite: No Prerequisite
Overview:
Aims:
Syllabus:
Teaching and learning methods:
Intended learning outcomes:
Assessment:
Exams: %
Test: %
Quiz: %
Coursework: %
Recommended reading list:
Code: COM321
Lecturer: Usman Abubakar Idris
Unit: 3
Prerequisite: No Prerequisite
Overview:

Data Communication and Networking has change the way we interact and share information. The positive impact of this module on the way we do business cannot be over emphasized. Information sharing can be local or remote (distance). In this module, students will learn more about distance communication. They will explore some basic theories of hardware (electronics devices) and software components of communication. 

Aims:

  • Expose the students to the role of each component of communication and their interconnection. 
  • Explore the different Network layout. 
  • Understand various forms of data that can be transmitted over a network.
Syllabus:

Introduction to Networking, Components of Communication, Open System Interconnection (OSI) Reference Model, Network Topology, Introduction to Network Addresses, Network Devices, Network standards. Transmission medium, Concept of packet and Circuit switching.

Teaching and learning methods:

  • Lectures will be used to explain the content of the syllabus.
  • Class discussion will be used to stimulate students’ participation and sharing of knowledge to develop their communication skill. 
  • There will be hands on experience in the laboratory to enhance the understanding of most of the topics taught in the classroom.
Intended learning outcomes:

  • On completion of this module, student should be able to:
  • Define the basic terminologies of Computer Networks
  • Terminate twisted pair cable
  • Use various communication devices to setup a network
  • Understand communication delays and how to eliminate it
  • Understand the best network solution for any given environment
Assessment:
Exams: 60%
Test: 15%
Quiz: 10%
Coursework: 15%
Recommended reading list:

Data Communications and Networking 5th Editon - 2012 (Behrouz A.)

Cabling: The Complete Guide to Network Wiring (David Barnett, David Groth and Jim McBee)

Computer Networking: A Top-Down Approach: Kurose (6th or 7th Edition)


Code: COM304
Lecturer: Sabiu Maikore Fatima
Unit: 3
Prerequisite: No Prerequisite
Overview:

This module will enable students to develop a critical and practical understanding of concepts in databases with a primary focus on data modelling and implementation. 

Aims:

Teaching, learning and assessment of the module will support these aims:

Basic knowledge of concepts in information systems 

An understanding of the concepts in database systems including the evolution from file system to database systems

Data modelling concepts, techniques and methods

An understanding of different types of data models

Skills in structured query language, and query optimization

An understanding of database administration and security issues as well as ethical concerns in data management


Syllabus:

Introduction to Information Systems, Introduction to Databases, Data Models, Relational Database Model, Data Modeling with ER diagrams, Extended ER modeling, Normalization, Structured Query Language (SQL), SQL queries, constraints and triggers,  Query optimization, Database administration, Database security, Interacting with databases through the web, NoSQL databases, Ethical issues in data management.


Teaching and learning methods:

There will be approximately 3 hours of lectures and 2 hours of workshop sessions every week. Lectures will be delivered in an engaging manner and students are expected to participate in discussions. The workshops will be designed to allow students practice the concepts taught during the lectures using appropriate tools such as MySQL Workbench, ERwin Data Modeller and MS SQL Server.

Intended learning outcomes:

In order to get a pass grade in this module, students must meet these learning outcomes:

Demonstrate knowledge of concepts in database systems

Be able to use data modelling tools and RDBMS

Demonstrate skills in structured query language

Reflect critically on how to handle database administration and security challenges


Assessment:
Exams: 60%
Test: 5%
Quiz: 5%
Coursework: 30%
Recommended reading list:

Coronel, C., Morris, S., & Rob, P. Database Principles: Fundamentals of Design, Implementation, and Management.

Code: COM301
Lecturer: Charles Isah Saidu
Unit: 3
Prerequisite: Algorithms ,
Overview:

The concepts of algorithm, role of abstraction in algorithm design, design and analysis of algorithms, divide and conquer algorithms including sorting and convex hull, greedy algorithms including job sequencing, shortest path and spanning trees, dynamic programming including knapsack and travelling salesman problem.

Aims:

The aim of this module is to explain the concepts of algorithms, algorithm design & analysis of algorithms and also to identify, describe and have an in depth knowledge of different algorithm types with their examples.

Syllabus:

Fundamentals of Algorithmic Problem Solving.

Fundamentals of the Analysis of Algorithm Efficiency.

Divide and Conquer Algorithm. Greedy Algorithm.

Dynamic Programming.

Teaching and learning methods:
  • Lectures will be used to introduce and explain major ideas, theories and to illustrate their wide- ranging applications
  • Interactive lectures will review materials by encouraging the student active participation - inviting questions, working through examples, giving short quizzes, discussing case studies etc.
  • Classes will encourage the student to apply the knowledge gained to real and hypothetical cases and will encourage students to gain confidence in presenting and defending their own ideas. Classes will usually require the student to read some material for discussion, or prepare answers, give some presentations, research a topic etc.
  • Class-work and Homework will be assigned regularly. These will help the students to understand the material .The answers to the homework should be clear, concise and correct.
  • Intended learning outcomes:

    On the successful completion of this module the student should understand and be able to:-

    • Understand the concepts of different Algorithms.
    • Understand the different algorithms and their features.
    • Design and Analyse algorithms efficiently.
    • Have a good knowledge on how to implement pseudo codes of these algorithms using a programming language.
    • Understand the different applications of these algorithms.

    Assessment:
    Exams: 60%
    Test: 20%
    Quiz: 10%
    Coursework: 10%
    Recommended reading list:
    1. Levitin, A. (2011).Introduction to Design and Analysis of Algorithms.3 rd ed. Pearson.
    2. Sedgewick,R. & Flajolet,P. (2013).An Introduction to the Analysis of algorithms.2nd ed.Addison-Wesley.
    3. Soltys,M. (2012).An Introduction to the analysis of algorithms.2 nd ed.World Scientific. Singapore.
    4. Skiena,S.(1997).The Algorithm designmanual.Springer-Verlag.Newyork.
    Code: COM331
    Lecturer: Sylvanus A. Ehikioya
    Unit: 10
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM309
    Lecturer: Sabiu Maikore Fatima
    Unit: 3
    Prerequisite: No Prerequisite
    Overview:

    This module will enable students to develop a critical and practical understanding of concepts, standards and frameworks for the semantic web with a primary focus on the semantic web technologies.

    Aims:

    Teaching, learning and assessment of the module will support these aims:

    Reflection on conceptual foundations of the Semantic Web

    Introduction to XML basics to ease students into further Semantic Web languages.

    Experience and exposure to Semantic Web languages, such as RDF(S) and OWL. The course, however, does not attempt to cover the languages comprehensively.

    Practical experience on data modelling through the development of simple ontologies using Protégé tool.

    An understanding of SPARQL. 

    Practical experience on querying semantic repositories using SPARQL.


    Syllabus:

    Introduction to the semantic web, The semantic web vision, XML basics, Resource description framework (RDF), RDF schema language, SPARQL infrastructure, Matching patterns, Construct queries , SPARQL update, Basics of Web Ontology Language (OWL),  OWL , Applications on the semantic web, Ontology engineering, Semantic web application architechture, Logic and inference: rules.


    Teaching and learning methods:

    There will be approximately 3 hours of lectures and 2 hours of workshop sessions every week. Lectures will be delivered in an engaging manner and students are expected to participate in discussions. The workshops will be designed to allow students practice the concepts taught during the lectures.

    Intended learning outcomes:

    In order to get a pass grade in this module, students must meet these learning outcomes:

    Reflect critically on how adding semantics can transform the traditional Web. 

    Demonstrate an awareness of the core concepts that provide a foundation for the Semantic Web.

    Be able to use appropriate tools to create simple ontologies to represent concepts in any domain.

    Demonstrate the ability to use appropriate tools to create and execute SPARQL queries. 


    Assessment:
    Exams: 60%
    Test: 10%
    Quiz: 5%
    Coursework: 25%
    Recommended reading list:

    Antoniou, G., Groth, P., van Harmelen, F., Hoekstra, R., A Semantic Web Primer.

    Code: COM307
    Lecturer: Samuel Ubaru
    Unit: 10
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM308
    Lecturer: Ruqayya Mohammed
    Unit: 17
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Year 3 | Semester 2
    Code: GEN301
    Lecturer: Ojeme Tope
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM313
    Lecturer: Essien Udoh Kevac
    Unit: 15
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM322
    Lecturer: Julius Makinde
    Unit: 3
    Prerequisite: Data Communication and Networking ,
    Overview: Protocols are guidelines that regulate the access method, types of cabling, physical topologies and speed of a Network. There must be agreement in the mode of communication between the communicating devices for efficient information delivery. In this module, students will learn about set of rules to be adhered to by the communicating devices. Design and implement network designs according to best practices. Learn how to troubleshoot and maintain a network for optimal performance.
    Aims:

    • Provide students with a sound knowledge of communication between different networks. 
    • Encourage students to consider the advantage of deploying a secure network. 
    • Equip students with the capabilities to building a reliable network.
    • Provide students with basic tools for troubleshooting a network.


    Syllabus:

    Internet Protocol (IP) address sub-netting, Routing protocols, Switching (VLANs & Trunking), VPN, Network design, Introduction to Network Security, Client and Server architecture, Network maintenance.

    Teaching and learning methods:

    • Lectures will be used to explain the content of the syllabus.
    • Class discussion will be used to stimulate students’ participation and sharing of knowledge to develop their communication skill. 
    • There will be hands on experience in the laboratory to enhance the understanding of most of the topics treated in the classroom.
    • Listen to Computer Network managers and researchers, from both industry and academia.


    Intended learning outcomes:

    On completion of this module, student should be able to:

    • Design a secure network
    • Set boundaries for communication between nodes/network
    • Troubleshoot and correct any abnormalities in a network


    Assessment:
    Exams: 60%
    Test: 5%
    Quiz: 5%
    Coursework: 30%
    Recommended reading list:

    • Software: Cisco Packet tracer.
    • Computer Networking: A Top-Down Approach: Kurose (6th or 7th Edition)
    • NETWORKS Design and Management (Steven T. Karris)
    • Routing Protocols and Concepts (Allan Johnson)


    Code: COM306
    Lecturer: Sylvanus A. Ehikioya
    Unit: 3
    Prerequisite: Computer Systems 2 ,
    Overview:

    Introduction to operating systems, core concepts of operating systems, types of operating systems, kernels, processes and threads, concurrency and synchronization, deadlocks and prevention, resource management – memory, storage units, file systems and CPU time.

    Aims:

    The aim of this module is to expose the students to the concepts of operating systems, its design and implementation techniques. It identifies and describes the major and common components of an operating system, and introduce to students the concepts of a process, threads, resource management (memory, storage units, file systems and CPU time).

    Syllabus:

    Operating Systems Structures. Process Management. 

    Memory Management. Storage Management. Protection and Security. Case studies of Windows and Linux Operating Systems.

    Teaching and learning methods:

    • Lectures will be used to introduce and explain major ideas, theories and to illustrate their wide- ranging applications.

    • Interactive lectures will review materials by encouraging the student active participation - inviting questions, working through examples, giving short quizzes, discussing case studies etc.
    • Classes will encourage the student to apply the knowledge gained to real and hypothetical cases and will encourage students to gain confidence in presenting and defending their own ideas. Classes will usually require the student to read some material for discussion, or prepare answers, give some presentations, research a topic etc.
    • Class-work and Homework will be assigned regularly. These will help the students to understand the material .The answers to the homework should be clear, concise and correct.

    Intended learning outcomes:

    On the successful completion of this module the student should understand and be able to:-


    • The students will have a working knowledge of operating systems’ theory and practice.
    • The students will have a knowledge of the factors that influence the design and type of operating systems.
    • The students will understand the concept of a kernel, process, process management, thread and the relationship between a process and a thread.
    •  The students will understand some Resource management techniques for memory, storage units etc

    Assessment:
    Exams: 60%
    Test: 20%
    Quiz: 10%
    Coursework: 10%
    Recommended reading list:

    1. Operating System Concepts, Silberschatz A., Galvin P.B.,(2013) John Wiley & Sons Inc., ISBN 978 118 06333 0, Ninth Edition

    2. Operating System Concepts with Java, Silberschatz A., Galvin P.B.,(2010) John Wiley& Sons Inc.,ISBN 978 0 470 50849 4 .Eight Edition

    3. Modern Operating Systems" by Tanenbaum, A.S., Prentice Hall, 3rd edition, 2009.

    4. Modern Operating Systems, Garridon J., Schlesinger R. & Hoganson K. Jones & Bartlett learning LLC. 2nd edition, 2013.

    5. Operating Systems: Internal and Design Principles" by Stallings, W., Prentice Hall, 7th edition, 2012

    Code: COM337
    Lecturer: Essien Udoh Kevac
    Unit: 3
    Prerequisite: No Prerequisite
    Overview:

    This course serves to introduce the students to the concept of R Programming and as well as its practical usage and how it relates to the other courses (Programming, Web Programming, Databases) preceeding it.

    Aims:

    . The singular aim of this course is to teach students R Programming. Not only in theory but also in practical application.

    Syllabus:

    IoT (Internet of Things) and the need for Big Data Analytics with Machine Learning. Basic R-Programming Terms and Syntax The “Hello Baze University “of R-Programming. Getting data in and out of R script. Accessing R packages, Writing R functions, Debugging, Profiling R code, Organization and selfdocumenting of R-program. Introduction to common statistical terms used in R-Programming. Introduction to basic types of plots (data visualizations) used in RProgramming. RStudio. Integration of guided R Data Analysis Project into web and mobile app using DeployR. Overview of Data Science and Big Data Analytics in the Context of IoT. The Lingo and Tools of Big Data Analytics. Overview of the Theory Behind Big Data Analytics. Big Data versus Small data Introduction to basic Big Data file formats. Techniques for filtering datasets. The Mechanics of Big Data Analytics using R. Guided Big Data Analytics Project using R. Machine Learning versus Artificial Intelligence. Introduction to Basic Machine Learning concept. Machine Learning with R-Programming. Scenario for usage of R-Programming in Final Year project  Module Retrospect.  

    Teaching and learning methods:

    The teaching approach will be light on theory and heavy on practical/hands-on work using real public domain data sourced from the Internet, etc. – such as Internet Media Database (@ http://www.imdb.com ; European statistics (@ http://ec.europa.eu/Eurostat ); Project Datasets as gathered and cleaned up by Petra Isenberg, Pierre Dragicevic and Yvonne Jansen; Opus Data (@ http://www.opusdata.com); Ohio Lottery data (copyright LottoStrategies.com) and as well as Baze University Student and Library data.  

    Students are expected to be familiar with Java/Eclipse and/or C#/Visual Studio programming environment.   

    Intended learning outcomes:

    At the end of this module, student will have: 

    • A clear motivation for learning R-Programming in the context of Internet of Things (IoT);  

    • A basic understanding of the R-Programming Language –syntax, grammar and range of vocabulary for use in data analysis  Knowledge of  some of the statistical terms and basic types of plots as used in R-programming for data analysis 

    • Invocation of R-program application from Java and C# 

    • Basic use of R-programming in Big Data Analytics 

    • Basic insights into Machine Learning with R-Programming. Knowledge of  some of the statistical terms and basic types of plots as used in R-programming for data analysis 

    • Invocation of R-program application from Java and C# • Basic use of R-programming in Big Data Analytics 

    • Basic insights into Machine Learning with R-Programming

    Assessment:
    Exams: 70%
    Test: 5%
    Quiz: 5%
    Coursework: 20%
    Recommended reading list:

    The class lecture notes will be comprehensive enough to support the module learning objectives. Students seeking to acquire more in-depth discussion on Big Data Analytics and Machine Learning can access great learning materials at Microsoft Virtual Academy @ https://mva.microsoft.com/training-topics/bigdata#!lang=1033 ; https://mva.microsoft.com/training-topics/advancedanalytics#!lang=1033 ; https://mva.microsoft.com/product-training/microsoftazure#!lang=1033 and   https://mva.microsoft.com/  If possible students will be provided with a digital media Package on R-Programming for data Science – which includes a book on R-Programming for Data Science, Lecture Videos, and Datasets plus R Code files. 

    Code: COM309
    Lecturer: Sabiu Maikore Fatima
    Unit: 3
    Prerequisite: No Prerequisite
    Overview:

    This module will enable students to develop a critical and practical understanding of concepts, standards and frameworks for the semantic web with a primary focus on the semantic web technologies.

    Aims:

    Teaching, learning and assessment of the module will support these aims:

    Reflection on conceptual foundations of the Semantic Web

    Introduction to XML basics to ease students into further Semantic Web languages.

    Experience and exposure to Semantic Web languages, such as RDF(S) and OWL. The course, however, does not attempt to cover the languages comprehensively.

    Practical experience on data modelling through the development of simple ontologies using Protégé tool.

    An understanding of SPARQL. 

    Practical experience on querying semantic repositories using SPARQL.


    Syllabus:

    Introduction to the semantic web, The semantic web vision, XML basics, Resource description framework (RDF), RDF schema language, SPARQL infrastructure, Matching patterns, Construct queries , SPARQL update, Basics of Web Ontology Language (OWL),  OWL , Applications on the semantic web, Ontology engineering, Semantic web application architechture, Logic and inference: rules.


    Teaching and learning methods:

    There will be approximately 3 hours of lectures and 2 hours of workshop sessions every week. Lectures will be delivered in an engaging manner and students are expected to participate in discussions. The workshops will be designed to allow students practice the concepts taught during the lectures.

    Intended learning outcomes:

    In order to get a pass grade in this module, students must meet these learning outcomes:

    Reflect critically on how adding semantics can transform the traditional Web. 

    Demonstrate an awareness of the core concepts that provide a foundation for the Semantic Web.

    Be able to use appropriate tools to create simple ontologies to represent concepts in any domain.

    Demonstrate the ability to use appropriate tools to create and execute SPARQL queries. 


    Assessment:
    Exams: 60%
    Test: 10%
    Quiz: 5%
    Coursework: 25%
    Recommended reading list:

    Antoniou, G., Groth, P., van Harmelen, F., Hoekstra, R., A Semantic Web Primer.

    Code: GEN300
    Lecturer: No Name
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM302
    Lecturer: Amit Mishra
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Year 4 | Semester 1
    Code: COM412
    Lecturer: Samuel Ubaru
    Unit: 3
    Prerequisite: Further Algorithms , Data Communication and Networking ,
    Overview:

    This course is an introduction to the broad field of computer, network, and information security. We will cover both computer security (including such topics as security policies, access control, viruses, etc.) and network security (such as protocols for maintaining confidentiality of email or for secure web transactions, and Internet security), along with some relevant background in basic cryptography.

    Aims:

    • To aid students to have sound understanding of the theories and concepts of Security as it applies to computers, networks, and application software and general electronic devices.


    • To aid students to understand and apply the C.I.A (Confidentiality, Integrity, and Availability) model in its diverse variations to network design, software development, Data or information management and service delivery.


    • To provide an appreciation of the application of Security policies and standards to real and hypothetical cases in technical terms while 

    • understanding the trade-offs involved in the application of security policies.

    Syllabus:

    General Introduction, Introduction to Cryptography, Crypto-systems and Cryptanalysis, Network Security and the concept of Access Control, Understanding Firewalls, IDSs and IPSs, Network Security Architectures, Wireless Networks, Internet Security, Security considerations in software development, IT Security Policy; design and implementation models

    Teaching and learning methods:

    • Lectures: Lectures will be used to introduce and explain major ideas, theories and to illustrate their wide-ranging applications. You will be handed the lecture folder and background reading materials. You are expected to have read these materials before the next class so we can discuss these in line with the weeks lecture.


    • Interactive lectures will review materials by encouraging your active participation - inviting questions, working through examples, giving short quizzes, discussing case studies, or showing a  video followed by a quiz, etc. Thus, this session forgoes the standard Socratic Method for a more conversational framework.


    • Classes: Classes will encourage you to begin to apply the knowledge gained to real and hypothetical cases and will encourage students to gain confidence in presenting and defending their own ideas. Classes will usually require you to read some material for discussion, or prepare answers, give some presentations, research a topic, take part in a debate, etc.  


    Intended learning outcomes:

    On Completion of the module the student should be able to:

    • Demonstrate an understanding of Computer and Network Security Concepts and best practices;


    • Be able to apply these concepts to real world scenarios and cases using ethical principles required of computer professionals;


    • Be able to identify and recommend appropriate tools for various security implementations while analysing requirements using best practices and current methodologies for security projects;


    • Demonstrate technical knowledge in Information Assurance necessary to prepare for an entry level position in the Computer and Network Security field;


    • Employ the process used to analyze, design, implement, test and deliver Information Security within Public and Private sector organizations;


    Assessment:
    Exams: 60%
    Test: 15%
    Quiz: 10%
    Coursework: 15%
    Recommended reading list:

    • CompTIA Security+ : Get Certified Get Ahead: SY0-401 study Guide; Darril Gibson (Primary Text)
    • Security in Computing, Fourth Edition, Pfleeger and Pfleeger, ISBN 0-13-239077-9. ( secondary text)
    • Network Security, Second Edition, Kaufman, Perlman, and Speciner, ISBN 0-13-046019-2. (or most recent edition)…..this Will only be used for a portion of the course (and will serve as our secondary text)
    • “Security Engineering”, Anderson. ( available online) Chapters 1-3 provide a good platform to start from in understanding the module


    Code: COM411
    Lecturer: Khadijah Mohammed Danjuma
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM399
    Lecturer: Dr Chollette C. Olisah
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: LAW519
    Lecturer: Dayo Godwin Ashonibare
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM401
    Lecturer: Mohammed Hammawa Baba
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM402
    Lecturer: Peter Ogedebe
    Unit: 3
    Prerequisite: No Prerequisite
    Overview:

    The Research Methods module introduces the students to the concept of research in computing, the different chapters present in the documentation and how to go about acquiring data and necessary information. This module does not only concentrate on the theoretical teachings of research but is also used to prepare the students for their final year documentation.

    Aims:
    To provide the students with an explicit understanding of their final project documentation deliverables. The project has five main chapters and each student should be able to aptly write content for each of these chapters in a concise and standard manner.
    To aid students apply the knowledge they have garnered in their previous years. This knowledge will then be practically transformed into their project (regardless of their discipline/majors).
    To apply the concept of research methods during the formation of the introduction, literature review, requirements, and design chapters of their documentation.

    Syllabus:

    Introduction to Research: Lecture on the definition, characteristics, and evaluation of academic research. Emphasis on the generation, development and refining of ideas into full-fledged project proposals. Research Methods Definition: Lecture on the definition of research methods and the types. Research Ethics: Lecture on the importance of ethics in research and the consequences of Plagiarism.

    Teaching and learning methods:

    Interactive Lecture – Ideas from both students and lecturer will be introduced and discussed in a discussion-oriented scenario. 

    Lecture – New concepts on research, the different forms of research, research motivations and the guidelines to follow when conducting research will be introduced.

    Thesis Compilation – Because the course is meant to help prepare the documentation for the final year project, students will not have assignments but will rather provide versions of the first three chapters of their documentation. Each completed chapter will be used as part of their continuous assessment to motivate them.


    Intended learning outcomes:

    • Students will have a profound understanding of the methods and approaches they will follow while conducting research projects.
    • Students will have completed the first three chapters of their final year project documentation

    Assessment:
    Exams: 70%
    Test: 10%
    Quiz: 5%
    Coursework: 15%
    Recommended reading list:


    Code: COM432
    Lecturer: Jamil Awwalu
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Year 4 | Semester 2
    Code: COM413
    Lecturer: Nasir Baba Ahmed
    Unit: 3
    Prerequisite: No Prerequisite
    Overview:

    At the end of this Module, it is expected that participants will appreciate the major role played by Information and Communication Technology (ICT) tools in investigating and producing acceptable forensic evidence in an organizational context and criminal breach of Laws. This module identifies the various offences committed using ICT tools and how they can be investigated given the various restrictions available in their contexts, noting that a breach of organizational policy might not necessarily culminate in a breach of law. This module addresses the question “that in the event a breach of law occurs or both, what technical, policy-driven, and legal procedures (such as E-voting and audits, etc.) should be taken to provide evidence and how?”

    Aims:
    • To aid students to have sound understanding of the theories and concepts of Forensic Investigations as it applies to computers, networks, and application software and general electronic devices.
    • To aid students to understand and apply Current Basic Information Technologies in its diverse variations to network design, software development, Data or information management investigating incidences, criminal acts conducted using Information technology
    • To provide an appreciation of the application of breach of Security policies and standards to real and hypothetical cases in technical and legal terms, given legal and organizational constraints, while understanding the trade-offs involved in the application of security policies.

    Syllabus:

    Understanding Computer Investigations, Current Computer Forensics Tools, Processing Crime and Incident Scenes, Data Acquisition, Working with Windows and DOS Systems, Macintosh and Linux Boot Processes and File Systems, Computer Forensics Analysis and Validation, Recovering Graphics Files, Virtual Machines, Network Forensics, and Live Acquisitions, Cell Phone and Mobile Devices Forensics, E-mail Investigations, Legal issues and Report Writing for High-Tech Investigations

    Teaching and learning methods:
    • Lectures: Lectures will be used to introduce and explain major ideas, theories and to illustrate their wide-ranging applications. You will be handed the lecture folder and background reading materials. You are expected to have read these materials before the next class so we can discuss these in line with the weeks lecture.
    • Interactive lectures will review materials by encouraging your active participation - inviting questions, working through examples, giving short quizzes, discussing case studies, or showing a  video followed by a quiz, etc. Thus, this session forgoes the standard Socratic Method for a more conversational framework.
    • Classes: Classes will encourage you to begin to apply the knowledge gained to real and hypothetical cases and will encourage students to gain confidence in presenting and defending their own ideas. Classes will usually require you to read some material for discussion, or prepare answers, give some presentations, research a topic, take part in a debate, etc. 

    Intended learning outcomes:
    On Completion of the module the student should be able to:

    • Demonstrate an understanding of Basic Computer Forensic Concepts and best practices;
    • Be able to apply these concepts to real world scenarios and cases using ethical principles required of computer professionals;
    • Be able to identify and recommend appropriate tools for various Forensic Investigations while analyzing  case-requirements using best practices and current methodologies;
    • Be able to apply technical skill set in carrying out forensic investigations on digital devices
    • Show sound understanding of existing laws and guidelines for admissibility of evidence discovered
      the concept of an expert witness and its requirement
    Assessment:
    Exams: 60%
    Test: 15%
    Quiz: 10%
    Coursework: 15%
    Recommended reading list:

    • Incidence Response and Computer Forensics; Second and Third Edition, Kevin Mandia, Mathew pepe et al, (Primary text)
    • Real Digital Forensics: Computer Security and Incident Response, Keith Jones, Curtis Rose et al. (Secondary Text)
    • And recommended Journals  on special topics

    Code: COM403
    Lecturer: Essien Udoh Kevac
    Unit: 0
    Prerequisite: No Prerequisite
    Overview:
    Aims:
    Syllabus:
    Teaching and learning methods:
    Intended learning outcomes:
    Assessment:
    Exams: %
    Test: %
    Quiz: %
    Coursework: %
    Recommended reading list:
    Code: COM421
    Lecturer: Chandrasekhar Uppin
    Unit: 3
    Prerequisite: Programming 1 , Programming 2 , Web Programming , Application Programming with Java/C# ,
    Overview:

    The purpose of this module is to provide students with a coherent knowledge of internal (low-level) structure of existing Programming Languages, how instructions are identified, translated and compiled/interprets during the execution. Understanding Semantics, lexical, and Syntactical structure of programming languages with its analysis.   Identifying the style of Programming, cconcept of computational  paradigm, understand the design criteria, to know more about Logic & Logic Programs and to understanding Lexics Vs Syntax Vs Semantics,  data types and control structures.

    Aims:

    Aim to introduce this module to know more about the internal structure of Programming Languages.  How Programming Languages are designed and created for various applications. 

    Course designed to intend :  

    • To understand the concept of computational  paradigm
    • To understand the design criteria
    • To understand functional programming
    • To know more about Logic & Logic Programs 
    • Lexical and Syntax analysis  
    • Abstract data type and Modules.
    • To understanding Lexical Vs Syntax Vs Semantics,  data types and control structures.


    Syllabus:

    Introduction to programming languages, compilers and interpreters, abstract machines, programming language grammar and syntax, compilers, the compilation process, parsing, semantics, pragmatics, implementation, expressiveness of programming languages, data structures, memory management, control structures, structured programming, recursion, subprograms, high-order functions, exceptions, data abstraction, object-oriented, functional and logic programming paradigms, historical perspective.

    Teaching and learning methods:

    • During the Lectures discussing the concepts of Imperative and Object Oriented Programming Language practices with its structure. 
    • Interactive lectures will review course materials by encouraging the student active participation - inviting questions / discussions, working through examples given in case studies.
    • In Leaning process students will be encourage to use and applied their existing knowledge to enhance the confidence level within. Organized case study presentation to encourage students to motive self-preparation and transformation of knowledge with various research approaches. 
    • To easy understanding the subject providing ample Hands on work (Assignments Direct/Applied) Case study practices which are helps the student to understand the more applied. As this course in 400 level. 

    Intended learning outcomes:

    On the successful completion of this module the student should understand :-

    • Understand the Low-level structure of programming languages.
    • Understand how Language instructions are identified, translated and compiled/interprets during the execution.
    • Understand the available style of programming languages in the market and current use.
    • Understanding Lexical Vs Syntax Vs Semantics,  data types and control structures of  its usage in programming languages with their  effects & efficiency.
    • Capable of choosing appropriate programming languages to design and build different kinds of applications.


    Assessment:
    Exams: 70%
    Test: 10%
    Quiz: 5%
    Coursework: 15%
    Recommended reading list:

    Kenneth C. Louden, Programming Languages: Principles and Practice, 3rd edition PWS Publishing (Boston), 1993.

    Other Sources:

    Paul Hudak, “Conception, Evolution, and Application of Functional Programming Languages,” ACM Computing Surveys 21/3, 1989, pp 359-411.

    Clocksin and Mellish, Programming in Prolog, Springer Verlag, 1987


    Entry requirements

    Home / UTME


    SSCE (WAEC, NECO, etc);
    JAMB;

    Home / Direct Entry


    A level / Diploma / IJMB / HND / First degree;
    JAMB DE Form;
    SSCE (WAEC, NECO, etc);

    Home / Direct Transfer


    SSCE (WAEC, NECO, etc);
    Academic transcript;
    Please note: Admission on transfer will only be issued after on campus interview;

    Foundation


    SSCE (WAEC, NECO, etc);

    International (Nigerian)


    O' level result;
    JAMB;
    Please note: You can get a conditional admission if you does not have JAMB, but you must provide it before you progress to 200 level;

    International (Foreign)


    O' level result;

    Staff

    There are no staff for this course