Syllabus (COMPUTER APPLICATION)

Course Type: ME-6

Semester: 5

Course Code: BBCAMEB35C

Course Title: Business Intelligence

(L-P-Tu): 3-1-0

Credit: 4

Practical/Theory: Combined

Course Objective: Course Objectives: The main objective of this course is to: • Become familiar with the ethics and basics of Business Intelligence and Decision Support Systems • Define mathematical models, data mining and data preparation • Describe classification problems and clustering methods • Study marketing models, Logistic and production models and Data envelopment analysis • Be able to grasp the objectives of knowledge management

Learning Outcome: Course Outcomes: On successful completion of the course, students will be able to: • Become familiar with the role of mathematical models, Business intelligence architectures, representation of the decision-making process, evolution of information systems • Define development of a model, representation of input data, data mining process, analysis methodologies, data validation, data transformation, data reduction • evaluate classification models, Bayesian methods, Clustering methods, Partition methods, Hierarchical methods • study relational marketing, sales force management, optimization models for logistics planning, efficiency measures, efficient frontier, The CCR model • Be well-versed with Organizational Learning and Transformation, Knowledge Management Activities

Syllabus:

Unit 1: Theory Credit: 3 [L 45]

Introduction to Business Intelligence: Effective and timely decisions, Data, information and knowledge, the role of mathematical models, Business Intelligence architectures, Ethics and Business Intelligence. [L 6]

Decision support systems: Definition of system, Representation of the decision-making process, Evolution of information systems, Definition of decision support system, Development of a decision support system. [L 6]

Mathematical models for decision making: Structure of mathematical models, Development of a model, Classes of models Data mining: Definition of data mining, Representation of input data, Data mining process, Analysis methodologies. [L 6]

Data preparation: Data validation, Data transformation, Data reduction. [L 4]

Classification: Classification problems, Evaluation of classification models, Bayesian methods, Logistic regression, Neural networks, Support vector machines. [L 4]

Clustering: Clustering methods, Partition methods, Hierarchical methods, Evaluation of clustering models. [L 4]

Business Intelligence applications: Marketing models: Relational marketing, Sales force management, Logistic and production models: Supply chain optimization, Optimization models for logistics planning, Revenue management systems. [L 5]

Data envelopment analysis: Efficiency measures, Efficient frontier, The CCR model, Identification of good operating practices. [L 4]

Knowledge Management: Introduction to Knowledge Management, Organizational Learning and Transformation, Knowledge Management Activities, Approaches to Knowledge Management, Information Technology (IT) In Knowledge Management, Knowledge Management Systems Implementation, Roles of People in Knowledge Management. [L 6]

Unit II: Business Intelligence Lab Credit: 1 (L 30)

Practical will be conducted as per the topic covered in the theoretical parts.

Reading References:

1. Business Intelligence: Data Mining and Optimization for Decision Making by Carlo Vercellis, Wiley.
2. Decision support and Business Intelligence Systems by Efraim Turban, Ramesh Sharda, Dursun Delen, Pearson.
3. Fundamental of Business Intelligence by Grossmann W, Rinderle-Ma, Springer.
4. Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R by Nataraj Dasgupta, Packt.