Semester 2

1.     Module Code: BIT 3231                                              School: Business

2.     Module Title: Business Intelligence and Innovations

3.     Year3  Trimester 2     

4.     Credits: 15

5.     Administering School: School of Business

6.     Department: Business Information Technology        

7.     Year of Presentation: 2018-2019

8.     Pre-requisite or co-requisite modules: N/A

9.     Allocation of study and teaching hours

	Student Hours	Staff hours
Lectures	60	55
Seminars/Workshops	10	10
Practical classes/laboratory	50	35
Structured exercises	20	15
Set reading etc.	20	15
Self-directed study	20	10
Assignments – preparation and writing	10	5
Examination – revision and attendance	10	5
Total hours	150	100

10.  Description

This module will offer to the students with opportunity to gain an enhanced understanding of business analytics and how they do help in decision making. Understand the importance of knowledge as an intellectual asset for the organization and how it is used for value creation

11.1 Knowledge & Understanding: Successful completion of this module will enhance the student's ability to:

 

a.     Define all possible tools used in current business organization for business intelligence and analytics

b.     Master processes for knowledge creation and sharing in organizations

c.     Understand the utility of data mining in business

d.     Be able to understand to importance of innovative ideas in business

11.2 Cognitive/Intellectual skills/application of knowledge: Successful completion of this course will enhance the student's ability to:

1. Identify appropriate business intelligent tools
2. Explain relevant data mining techniques

to use the systems development lifecycle to build or acquire business computing

11.3  General transferable skills : Having successfully completed the module, students should be able to:

 

a.     Explain data mining concepts

b.     Explain processes of Designing the data storage using data warehousing techniques

c.     Interpret the results using Knowledge management theories

d.     Deciding according the information got and quickly responding to the market

e.     Forecasting the market and be a forerunner on the field

Unit1: Business Intelligence

I.               Introduction to Business Intelligence

A Framework for Business Intelligence (key definitions)

Transaction Processing versus Analytic Processing

Successful BI Implementation

Major Tools and Techniques of BI

II.              Data Warehousing

Definitions and concepts

Data warehousing Process overview

Data warehousing architectures

Data warehouse Development

Data warehousing Implementation Issues

Real Time data warehousing

Data warehouse administration, Security Issues and future Trends

III.            Data Mining for Business Intelligence

Introduction/Historical Perspective,

Fundamental concepts, Overview of data mining 

Expanding universe of data Production factor

Knowledge discovery process

Data pre-processing - Measurement and Data

Visualizing and Exploring data

Data Analysis and Uncertainty - algorithms

Data Cube – OLAP – Technology

Classification and Prediction, Regression 

Cluster Analysis – Mining Frequent Pattern Association -

Visualization Technique - k nearest neighbor – Decision trees – Association rules – Neural Networks – Genetic Algorithms – Reporting - Commercial data mining software applications

Text and Web Mining

Unit II: Knowledge Management & Business Innovations

 Introduction – concepts and Theories

KM Technologies and Strategies

Knowledge Sharing

Community of Practices

Knowledge Applications

Knowledge systems

Knowledge Management Cycle

Business Innovations

Managing Technological Transitions

 Competing in Technology Intensive Industries

Creating and Managing Innovative Organization

Applying the Ideas

 

 

 

Development of the learning outcomes is promoted through the following teaching and learning methods:

a.     Lectures are used throughout the program in order to impart essential knowledge relating to above aims and outcomes.

b.     Emphasis is given on Practical oriented Approach by giving more exercises in lab, field visits and  seminars

c.     Student centered approach is taken whereby students are expected to do lot of exercises by taking real life examples.

d.     Independent study is necessary to both assimilate and further clarification material obtained from lectures, preparation for seminars, preparation for written assessments, and the broader development of knowledge of the field of study.

e.     Group work is an important part of some modules in the program and it provides an opportunity for teamwork participation, the development of interpersonal skills and the reconciliation of different points of view.

 

 

The main principles underlying assessment are that understanding, interpretation and application are the crucial issues. 

 

S.No	Type of Assessment	Learning objectives covered
1	In-course Assessment (50%)	All the mentioned above in section 11.1, 11.2
2	Final Exam (50%)	All mentioned in 11

Assessment Strategy

The Assessment can take many forms such as group discussions, Assignments, Quizzes, objective questions, Continuous Assessment Tests, Practical Exercises, Presentations, to name a few. The Assessment differs from Module to module and is dependent on the module and its learning outcomes. The Module team headed by the Module Leader, come together and prepare a Module Handbook detailing the module, its learning outcomes, indicative content, the type of Assessments and its weightage, before the start of the module. 

 

 

A.    Reference Books:

 

Core texts:    

 

1.     Efraim T.,Ramesh S.,Darsum D.,David K.,(2011). Business Intelligence. A Managerial Approach. 2nd Ed.,Pearson, 312 p

2.     Dalkir K.,(2011). Knowledge Management in Theory and Practice. 2nd Ed.,MIT, 485p

3.     J. Hass and M. Kamber (2006) Data Mining Concepts and techniques, Morgan Kauffman Publishers, Elsevier Inc

4.     Paulraj Ponniah (2001) Data Warehousing Fundamentals: A Comprehensive Guide For IT Professionals, Wiley Interscience Publications

5.     D. J. Hand, Heikki Mannila, Padhraic Smyth (2001) Principles of Data Mining, MIT

Background texts:

6.     Herwig Rollett (2003), Knowledge Management Process and Technologies, Kluwer Academic Publishers

7.     Ronald Maier (2004), Information and Communication Technologies for Knowledge Management,Springer Verlak, Newyork

8.     Jean-Philippe Deschamps (2008) Innovation Leaders: How Senior Executives Stimulate, Steer and Sustain Innovation, John Wiley & Sons Publishers

9.     Pieter Adriana’s, Dolf Zantinge (1998) Data Mining, Addison Wesley.

10.  Alex Berson and Stephen J. Smith (2004), Data Warehousing, Data Mining, & OLAP, McGraw-    Hill

11.  Krogh, G. and Roos, J. (Eds) (1996) Managing Knowledge: Perspectives on Cooperation and Competition. Sage, London

 

Mr. PAVALAM S.M.

Mr. MUGABE Nzarama Gabriel

 

 

 

 

Unit Approval

 

Dean of Faculty and the Head of department implementing the program to confirm agreement

Unit	Names /Designation	Signature	Date
Business Information Technology	Ms. S.M. Pavalam; HOD	.............................	.............................
School of Business	Dr. Jonas Barayandema, Acting Dean	.............................	.............................
College of Business And Economics	Dr. Gasheja Faustin Principal	………………………	……………………
Teaching and Learning Enhancement	Dr. Niragire Francois, Acting Director	……………………..	……………………
Library	Acting Director	………………………..	………………….
ICT	Director	……………………..	…………………….