The module of Interactive Computer Graphics, prepares the students to present ideas through graphics design and animation. Throughout this module, students will describe and interact with computer graphics and digital imaging, understand computer graphics pipeline, graphics generating algorithms, graphics transformation, manipulate raster and vector graphics, manipulate audio and video in multimedia. At the end of this module, students will be able to master OpenGel and WebGel for generating Computer Graphics, master adobe Photoshop, adobe InDesin, adobe Illustrator, Macromedia flash and create graphics and multimedia content such as design Logos, Business cards, Invitations, Advertisements, good looking web interfaces and simulated environments.

The primary objective of the course is to provide students without formal training in computer science, a solid background in the key design principles leading to user-friendly computer systems that are easy to use or learn without prior strong experience in similar systems.  This module covers ways in which humans interact with computers and teaches students how to design a more interactive system respects principles from computer science, behavioural sciences, design, and media studies.

Software Engineering is a year three module in the department of computer science. This course achieves thorough understanding of the all software development life cycle and all phases under which the cycles belong. in addition to the course overview, the course clarifies requirements, design, implementation, testing, maintenance, and project management.

Requirements phase, in other words means a problem domain where we clearly understand the real and exact problem in order to derive a suitable solution. The course delivers the development of use cases and use case scenario towards a finite problem solving step by step strategy.

Next to that, the we move our focus to the semi-solution phase which is the design phase. This phase connects different units (entities) that communicate while the system executes the solution. We focus on data flow diagram, HIPO diagrams, IPO diagrams as well as object oriented design. While the design is at hand, we implement the system. This exercise is accomplished through source code writing where best guidelines are proposed and different paradigms are discussed. For every code written, we need to test whether it accomplishes its task or not. It goes further than that, it tests system regression as well as the system acceptance. For the system continuous reliability, it requires maintenance. It can be correcting, adapting, or prevention. 

Finally, when a system is started to be built-up, this is in other words referred to as a project. Project Management is the course that matters on every phase. This phase is the overall process through resources, people, and processes are managed.

Objectives: At the end of this course, the student will be able to:

1. Understand different process models used to develop software.

2. Gather and validate software requirements.

3. Design the system in such way that all requirements elicited are captured.

4. Adapt to the best coding styles and choose the relevant programming methods.

Instructors:

Name: Dr. Innocent Kabandana

E-mail: kabandanainnocent2020@gmail.com

Tel: 0788437932

Name: Mr. Celestin Mbonabucya

E-mail: cembonace@gmail.com

Tel: 0788695862

Computer Networking Module introduces students to network devices and connectivity, fundamentals of computer communication, building blocks of computer networks starting from topology, standards, network design, network addressing, network protocols. Upon completion of this module, students will be able to build local area network and extend it to Internet, network  cabling, network devices configuration, network monitoring and management, Quality of Service (QoS)and network security issues. Students will be comfortable working with PackTracer Simulation and working on real devices.

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