BSc. Comp 121 – EGovernment
BACKGROUND AND RATIONALE
Electronic Government introduces leaners to new trends in public service delivery using information and communication technology. The module beings with an introduction on what Egovernment is, explains the concept of Egovernment in development models, examines the different regional and international policies on egovernance. The module further examines and explains the Egovernment Development Index, examines the Legislation and Policies on Egovernment Development and finally explains the Telecommunication Policy in Zambia.
LEARNING OUTCOMES
At the end of this module learners should be able to:
 Know and apply the principles, models and concepts of egovernance and its objectives.
 Know and explain the different types of service delivery in egovernance.
 To know and explain the objectives of egovernment.
 To know and explain the role of ICTs in egovernment.
 To know and explain the eGovernment Governance structure in Zambia.
 Discus the eGovernment Development Index
 Know and explain the Telecommunication Policy in Zambia and the globe
COURSE CONTENT

 Unit 1 Introduction to EGovernance

 Unit 2 EGovernment Development Models

 Unit 3: Regional and Global Trends

 Unit 4. The Egovernment Development Index

 Unit 5. Legislation and Policies on Egovernment Development

 Unit 6. Telecommunication Policy in Zambia
ASSESSMENT
Assignment 1 
15% 
Assignment 2 
15% 
Final exam 
70% 
Total 
100% 
PRESCRIBED READING
Heeks, Richard. (2005). Implementing and Managing eGovernment: An International Text. London: Sage, – 304 p. – ISBN: 9781847877208
M. Jae Moon. (2002). The Evolution of EGovernment among Municipalities: Rhetoric or Reality? Public Administration Review Vol. 62, No. 4 (Jul. – Aug., 2002), pp. 424433 (10 pages) Published By: Wiley
RECOMMENDED READING
Fang, Z., (2002). Egovernment in Digital Era: Concept, Practice, and Development. International Journal of the Computer, the Internet and management, 10(2), pp.122.
BSc. Comp 122  SOFTWARE ENGINEERING
BACKGROUND AND RATIONALE
Software Engineering module introduces learners to software engineering. The modules begins with explaining key concepts in software engineering, explains the concept of tools and environments, explains the process of project management, explains software process models, explains the process of software analysis and design using UML, explains software requirements, analysis, validation and management. The module further explains software design, design concepts, design patterns. It also explain software testing, testing approaches, testing levels (unit, integration, system, acceptance), it explains software analysis, program optimisation and correctness, static and dynamic analysis, explains software maintenance, corrective, adaptive, perfective and preventive maintenance, explains software quality management, assurance, planning, control, and ends with explaining agile software development, methods and practices.
LEARNING OUTCOMES
At the end of this unit, you should be able to:
– Know and apply key concepts of the analysis, design, development and maintenance of complex software systems and infrastructures.
– Know and apply knowledge on the software lifecycle and its stages and utilise these in the development of software process models.
– Know and knowledge in conducting software tests in order to evaluate and verify software products.
– Know and apply the knowledge in software analysis, program optimisation and correctness, static and dynamic analysis
– Know and apply the knowledge in software maintenance, corrective, adaptive, perfective and preventive maintenance
– Know and apply knowledge on software quality management, assurance, planning, control
– Know and apply the knowledge in agile software development methods and practices.
COURSE CONTENT
UNIT 1: Key concepts in Software Engineering.
UNIT 2: Tools and Environments.
UNIT 3: Project Management.
UNIT 4: Software Process Models.
UNIT5 : Software Analysis and Design using UML.
UNIT 6: Software Requirements, analysis, validation and management.
UNIT 7: Software Design, design concepts, design patterns.
UNIT 8: Software Testing, testing approaches, testing levels (unit, integration, system, acceptance).
UNIT 9: Software Analysis, program optimisation and correctness, static and dynamic analysis.
UNIT 10: Software Maintenance, corrective, adaptive, perfective and preventive maintenance.
UNIT 11: Software Quality Management, assurance, planning, control.
UNIT 12: Agile Software Development, methods and practices.
ASSESSMENT
Assignment 1 
15% 
Assignment 2 
15% 
Final exam 
70% 
Total 
100% 
PRESCRIBED TEXTBOOKS
Lethbridge T.C and Laganiere R. (2005) ObjectOriented Software Engineering: Practical software development using UML and Java. Berkshire: McGraw Hill Education
Olga Filipova and Rui Vilão (2018). Software Development From A to Z: A Deep Dive into all the Roles Involved in the Creation of Software. Berlin: Filipova and Vilao
Michael Keeling (2017). Design It!: From Programmer to Software Architect. North Carolina: The Pragmatic Programmer
RECOMMENDED TEXT BOOKS
Bovee, C.L. & Thill, J.V. (2014). Business communication essentials (6th). Boston: Pearson.
BSc. Comp 123  INTRODUCTION TO DATA SCIENCE
BACKGROUND AND RATIONALE
Data Science module introduces learners to data science. The module combines topics from Computer Science and Mathematics and has a significant presence in many areas of science and technology. The module begins with Introducing data science and its key concepts. It explains data points, datasets, data types. It describes the applications of data science. Explains the mathematical concepts underpinning data science. It describes statistical analysis, aggregate data. The module further describes statistical distributions. It explains the utilising of linear algebra for solving data science related problems. It explains the process of visualising data, design principles in data visualisation. It further explains visual storytelling in data science. Explains data clustering, analysis, algorithms and visualisation. The module explains the concept of connected data, introduction to graph theory, initialising and processing connected data, visualising networks and ends with explaining the process of machine learning, applications, algorithms, supervised, unsupervised learning.
LEARNING OUTCOMES
At the end of this module learners should be able to:
– Know and explain scope of data science and its key concepts, from academic, scientific and industrial point of view.
– Know and apply the mathematical concepts underpinning data science.
– Know and apply statistical analysis and aggregate data to solve data science related problems.
– Know and explain statistical distributions in data science.
– Know and apply linear algebra for solving data science related problems.
– Know and explain the process of visualizing data and design principles in data visualization. – Know and apply data science in visual storytelling.
– Know and explain the process of data clustering, analysis, algorithms and visualization.
– Know and explain the concept of connected data, graph theory, initializing and processing connected data and visualising networks.
– Know and explain the process of machine learning, applications, algorithms, supervised and unsupervised learning.
COURSE CONTENTS
 UNIT 1: Introduction to Data Science and its key concepts.
 UNIT 2: Data points, datasets, data types.
 UNIT 3: Applications of Data Science.
 UNIT 4: Mathematical concepts underpinning Data Science.
 UNIT 5: Statistical analysis, aggregate data.
 UNIT 6: Statistical distributions.
 UNIT 7: Utilizing Linear Algebra for solving Data Science related problems.
 UNIT 8: Visualizing data, design principles in Data Visualization.
 UNIT 9: Visual Storytelling in Data Science.
 UNIT 10: Data Clustering, analysis, algorithms and visualization.
 UNIT 11: Connected data, introduction to Graph Theory, initializing and processing connected data, visualizing networks.
 UNIT 12: Machine Learning, applications, algorithms, supervised, unsupervised learning.
ASSESSMENT
Assignment 1 
15% 
Assignment 2 
15% 
Final exam 
70% 
Total 
100% 
PRESCRIBED READING
Eric Goh Ming Hui (2019). Learn R for Applied Statistics With Data Visualizations, Regressions, and Statistics. Singapore: Eric Goh Ming Hui
Thomas Mailund (2017). Beginning Data Science in R Data Analysis, Visualization and Modelling for the Data Scientist. Aarhus, Denmark: Thomas Mailund
RECOMMENDED READING
Eric Pimpler (2017). Data Visualization and Exploration with R: A practical guide to using R, RStudio, and Tidyverse for data visualization, exploration, and data science applications. Boerne: Eric Pimpler
Manohar Swamynathan (2017). Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python. Bangalore: Manohar Swamynathan
BSc. Comp 124  MATHEMATICS FOR COMPUTER SCIENCE
BACKGROUND AND RATIONALE
Mathematics for Computer Science module introduces learners to mathematics for computer science. The module begins with explaining sets, Venn diagrams, etc. numbers, algebra and number bases, modular arithmetic, 2’s complement. Explains the concepts of logic, truth tables and proofs. Explains the process of pattern matching and unification. Explains the concept of graphs, functions, relations and mappings. Explains vectors, matrices and trigonometry and ends with explaining data visualization, introductory probability and introductory statistics.
LEARNING OUTCOMES
At the end of this this module learners should be able to:
– Demonstrate an understanding of sets, logic, graph theory and discrete mathematics algorithms and their applications in computing.
– Understand vectors and matrices and apply them to a range of problems and apply arithmetic and algebraic expressions in a range of number types and bases.
– Apply basic principles of statistics and probability and use software for data analysis and data visualization and be able to interpret results.
COURSE CONTENTS
 UNIT 1: Sets, Venn Diagrams, etc. Numbers, Algebra and Number bases, modular arithmetic, 2’s complement.
 UNIT 2: Introduction to Logic, truth tables, Proofs.
 UNIT 3: Introduction to pattern matching and unification.
 UNIT 4: Introduction to Graphs, Functions, Relations, Mappings.
 UNIT 5: Vectors & Matrices, trigonometry.
 UNIT 6: Data visualization, introductory probability, introductory statistics.
ASSESSMENT
Assignment 1 
15% 
Assignment 2 
15% 
Final exam 
70% 
Total 
100% 
PRESCRIBED READING
Judith L. Gersting (2014). Mathematical Structures for Computer Science: Discrete Mathematics and Its Applications. New York: W.H. Freeman and Company
Haggard. G, Schlipf. J and Whitesides S (2006). Discrete Mathematics for Computer Science. Belmont: Thomson Higher Education
RECOMMENDED READING
Y.N Singh (2005). Mathematical Foundation of Computer Science. New Delhi: New Age International Publishers
Malik D.S and Sen M.K (2004). Discrete Mathematical Structures: Theory and Applications. Boston: Thomson Learning