This course introduces students to the fundamentals of programming using Python, one of the most popular and beginner-friendly programming languages. By the end, students will be able to write, test, and debug Python programs and apply problem-solving strategies to real-life situations in education and beyond.

Software Engineering is a core year-three module in the Department of Computer Science that equips students with comprehensive knowledge and practical skills in the software development life cycle (SDLC). The course introduces you to the fundamental principles and phases of software engineering, ranging from requirements analysis, design, implementation, testing, and maintenance to project management. By blending theoretical understanding with practical application, the module prepares students to approach software development systematically, ensuring they can identify problems accurately, design effective solutions, implement reliable systems, and manage projects efficiently within real-world contexts.

This module is intended to impart the learners the modern concepts of data mining and data ware housing with good practical skills. The automated extraction of hidden predictive information from databases can be done using the special software tools included in the lab work. Learners will also be trained to be familiar and skilled in existing software.

1. COURSE SUMMARY

This module is intended to impart the learners the modern concepts of data mining and data ware housing with good practical skills. The automated extraction of hidden predictive information from databases can be done using the special software tools included in the lab work. Learners will also be trained to be familiar and skilled in existing software.

2. Learning Outcomes

A. Knowledge and Understanding

    Having successfully completed the module, students should be able to demonstrate  knowledge and understanding of:

1. Understand the basic concepts of data mining
2. Preprocess the data for mining applications
3. Have a basic knowledge on data warehouse and OLAP technology
4. Apply the association rules for mining the data
5. Design and deploy appropriate classification techniques
6. Cluster the high dimensional data for better organization of the data and Be able to detect anomalies from data

B. Cognitive/Intellectual skills/Application of Knowledge

     Having successfully completed the module, students should be able to:
     1-select relevant statistical methods for modelling data bases
     2-use data mining principle in development of solutions to specific computing problems involving enormous data
     3-apply knowledge and computing standards of Data warehousing to produce novel designs of software systems and data mining components
     4-critically assess design and research work done by other software professionals
     5-analyse failure in Data warehousing and take preventive measures

C. Communication/ICT/Numeracy/Analytic Techniques/Practical Skills

Having successfully completed the module, students should be able to:
    1-plan, manage conduct and report software research projects in data mining
    2-prepare technical report and deliver technical presentations on software Development/testing using data mining techniques
   3-Develop standards for Data warehousing and data mining software
   4-crtically asses research work done on Data manipulation
   5- Detect Data base failures and devise solutions
   6-demostrate practical applications of data mining

D. General transferable skills

Having successfully completed the module, students should be able to:
    1-Do life-long research on data
    2-Efficiently manage time and human resources in the manipulation of data
    3-Communicate effectively with other skilled data mining professionals/experts
    4-demonstrate numerical skills and problem solving techniques with new research work

3. INDICATIVE CONTENT

Data Mining: Introduction, Data preprocessing, Classification, Decision trees, Bayesian, Rulebased classification, Back propagation, Evaluating, Ensemble, KNN, Clustering, Partitioning, Hierarchical clustering, Density-based methods, Cluster evaluation, Association rule mining, Apriori, FP-growth, Eclat, , Web mining Applications of data mining , Data ,mining softwares. Case studies on WEKA, TANAGRA and similar softwares.

Data Warehousing concept: Definition Operational Data, Common Characteristics of Data Warehouse, Knowledge discovery and Decision Making, Knowledge discovery and Data Mining, Application of Data Warehouse.

Find User Data Access Tools: Data Warehouse Query Tools, Data Modeling Strategy – Star schema, Multi Fact Table Star Schema, Star with the Original Entry Relationship Model, Dimensional Model, OLAP, Relational OLAP, Multidimensional Database, Data Cube presentation of Fact Tables.

Data Warehouse, Architecture and Optimization: 3 Tier Architecture, Components of Warehouse, Classical Data Warehouse, Transportation of Data into the Data Warehouse, Data created in the Data Warehouse, Presentation of Data to End Users, Object Oriented System Architecture Definitions, Object Modeling Techniques. Implementing of the Application Design, Necessity of Data warehouse Metadata, Performance optimization, Data administration techniques.

4. LEARNING AND TEACHING STRATEGY

The module will be delivered through lectures, tutorial/practice sessions and group discussions.
In addition to the taught element, students will be expected to undertake practical case studies and do a mini project.

5. ASSESSMENT STRATEGY

Assessment on the programme is undertaken in accordance with the current Academic Regulations of the Institute.
Assessment Criteria:

•  For the examination setting and marking the UR-CST generic marking criteria will be used.
• For the assessment of the laboratory work, the CE&IT Laboratory assessment criteria will be used
•  For the assignment, criteria will be drawn up appropriate to the topic, based on the UR-CST generic marking criteria

6. STRATEGY FOR FEEDBACK AND STUDENT SUPPORT DURING MODULE

•  Interactive lecturing style, with opportunities for questions, and requirement to work on simple problems.
•  Peer marking of tutorial questions for formative feedback.
• Tutorial classes where students can ask questions and be lead through solutions as required.
•  Marked summative assessments (laboratory report and assignment) handed back to students, with comments.
•  Opportunities to consult lecturer and/or tutorial assistant in office hours.

7. INDICATIVE RESOURCES

• Jiawei Han and Micheline Kamber. (2011). Data Mining: Concepts and Techniques, Third Edition
• Thomas C. Hammergren. (2009).Data Warehousing For Dummies
•  Daniel T. Larose and Chantal D. Larose. (2015).Data Mining and Predictive Analytics
•  Online materials uploaded on the Learning Portal
•  Background Texts (include number in library or URL)
•  Journals8.

8. TEACHING TEAM :

Mrs. ALPHONSINE MUKABUNANI

1. In this course, we examine how humans respond and adapt machines to their everyday routines. 
   
2. This course will teach about the importance of the human-computer interface in the design and development of things people use. 
   
3. It will discuss the capabilities and limits of computers and other related systems, and discuss how that affects design and implementation decisions. 
   
4. The course will be a balance of perceptual/psychological and computer science elements. 
   
5. This course includes the design, improve, implementation and evaluation of user interfaces for computers and other complex, electronic equipment. 
   
6. Topics covered will include interface design, human factors, cognitive psychology, robotics, and wearable technologies.
   

This module of study identifies the essential knowledge, skills and attitude that all students need to be active lifelong learners in an information technology intensive environment. The curriculum is designed to form the foundation for continuous learning with introduction to emerging trends such as collaborative tools, ICT fundamentals, advanced MS Office/OpenOffice and security of the data. The students will be able to adapt to ever changing innovations and use ICT skills in long-life learning. The computer skills standard course of study involves the development of skills over time. These skills become building blocks with which to meet the challenges of personal and professional life.

This module of study identifies the essential knowledge, skills and attitude that all students need to be active lifelong learners in an information technology intensive environment. The curriculum is designed to form the foundation for continuous learning with introduction to emerging trends such as collaborative tools, ICT fundamentals, advanced MS Office/OpenOffice and security of the data. The students will be able to adapt to ever changing innovations and use ICT skills in long-life learning. The computer skills standard course of study involves the development of skills over time. These skills become building blocks with which to meet the challenges of personal and professional life.

This module is aimed to introduce students to basic building blocks of the computer system and practical knowledge about computer maintenance; to programming language and normal programming constructs.

This module addresses advanced issues in information system design and software engineering. It is aimed at helping students to analyze real-life problems and explore the possibilities of how a computerised system can help in solving the problems.

Module facilitators' contacts

Dr Mathias Nduwingoma, Tel: 0788 897 814, E-mail: ndumathias2001@yahoo.com

Albert NGIRUWONSANGA,Tel: 0788 471 881, E-mail: ngiruwonsanga.rw@gmail.com

The aim of this module is to provide the students with information technology  concepts and practical skills  of Microsoft Office programs.

Dear students you are welcome to real world programming, In this module, students will learn Object Oriented Programming Concepts. They will learn how data abstraction, reusability, inheritance and modularity of code can be enhanced using C++ . Students will also implement common data structures like Arrays, Stacks, Queue, Link List, Tree, Graph in C++ Language. Facilitator: Mr Mwumvaneza Evariste CelPhone:+250788630986 Email: e.mwumvaneza@ur.ac.rw

                                                                                                         Dr. HABIMANA Olivier  CelPhone +250788743498  

Dear students you are welcome to real world programming, In this module, students will learn Object Oriented Programming Concepts. They will learn how data abstraction, reusability, inheritance and modularity of code can be enhanced using C++ . Students will also implement common data structures like Arrays, Stacks, Queue, Link List, Tree, Graph in C++ Language.

Facilitator: Mr Mwumvaneza Evariste CelPhone:+250788630986

                 Dr Habimana Olivier CelPhone:+250788743498 

Database Management Systems (DBMS) aim to efficiently organize and retrieve data. They facilitate data storage, manipulation, and retrieval while ensuring security and integrity. DBMS covers concepts like data modelling, SQL queries, normalization, database design principles and relational algebra and relational calculus. Students learn to use DBMS tools to create, manage, and optimize databases, preparing them to handle real-world data challenges in various industries.

Module facilitators' contacts

Albert NGIRUWONSANGA,Tel: 0788 471 881, E-mail: ngiruwonsanga.rw@gmail.com

Evariste MWUMVANEZA, Tel: 0788 630 986, E-mail: evaristemw@gmail.com

This module covers computer systems fundamentals, including components like CPU, storage, binary systems, and digital logic. It also provides hands-on training in computer maintenance, encompassing hardware/software diagnosis, BIOS management, assembly/disassembly, partitioning, and safety and ethical considerations in computer usage and maintenance.

Module facilitators' contacts

Albert NGIRUWONSANGA,Tel: 0788 471 881, E-mail: ngiruwonsanga.rw@gmail.com

Marie Claire UWERA, Tel: 0788 745 194, E-mail: uweramclaire@gmail.com

Having successfully completed the module, students should be able to:

 To understand the concept of programming

Differentiate Data types

Declare and  Name variables

use  control structures, functions, arrays, files, and the mechanics of running, testing, and debugging.

INDICATIVE CONTENT

         :

• Introduction to the historical and social context of computing; Overview of computer science

as a discipline

• Data types, control structures, functions, arrays, files, and the mechanics of running, testing,

and debugging

• Fundamental programming constructs: Syntax and semantics of a higher-level language;

variables, types, expressions, and assignment; simple I/O; conditional and iterative control

structures; functions and parameter passing; structured decomposition

• Fundamental data structures: Primitive types; arrays; records; strings and string processing
• Advanced concepts in C- Pointers, Files, and Structure

Dear Students,

Introduce yourself, please  tell us your full names

Having successfully completed the module, students should be able to demonstrate knowledge and understanding of: - Application of mathematics in programming. - Demonstrating Practical application of computer programming. - Significance of the C language in computing - Gaining an appreciation of the technology and the software and compiler tools currently available for the C programming language - The utilization of the new skills acquired to analyze and solve problems using the C - Reviewing the importance of standards by means of studying the specifications of ANSI standard C

This module is aimed to deal with the internal organization of computer system.