School of Engineering and Technology, (SET)
 
AT82.04 : Business Intelligence and Analytics  3(3-0)
 
Course objectives:

This course will give students an understanding of the principles and practices of business intelligence and data analytics to support organizations in conducting their business in a competitive environment, in order to support better business decision making and capture new business opportunities.
 
Learning Outcomes:

Students, on successful completion of the course, will be able to

  1. Explain the concepts characteristics of BI and data analytics
  2. Describe multiple business problem/decision making domains requiring BI and data analytics
  3. Apply BI and data analytic tools and technologies to develop BI applications
  4. Integrate BI applications with other information systems as part of a business process5. Manage a BI project for an organization
  5. Interpret big data analytics and applications
 
Pre-requisite(s):

Data Modeling and Management
 
Course Outline:

I. Introduction to Business Intelligence

1. BI Definition

2. BI Concepts

3. Business Intelligence, Analytics, and Data Science

4. Business Intelligence to Support Decisions

II. Data Warehousing for BI

1. DW design

2. Multidimensional data modelling and analysis

3. ETL process

III. Categories of Data analytics:

1. Descriptive Analytics

2. Predictive Analytics

3. Prescriptive Analytics

IV. Descriptive Analytics

1. Descriptive Statistics

2. Business Performance Management

3. Data Visualization and Dashboard Design

V. Predictive Analytics

1. Data Mining (Text Analytics and Text Mining, Web Analytics, Web Mining,

                and Social Analytics) 

2. Predictive Modeling

VI. Overview of Prescriptive Analytics

1. Optimization

2. Multi-Criteria Systems

VII. Technical Aspects

1. BI Architecture

2. BI Tools and Technologies

VIII. BI Applications

1. BI Maturity

2. BI Strategies

3. BI Project (case study)

IX. Overview of Big Data

1. Big Data Analytics

2. Example of Big Data Applications
 
Laboratory Sessions:

None.
 
Learning Resources:

Textbook:

Business intelligence, analytics, and data science, by Ramesh Sharda;Dursun Delen; Efraim Turban, Pearson Publisher, 2018
 
Reference Books:

Recommended books:

  1. Business Analytics (2nd Ed.) by James Evans, Pearson, 2017.
  2. Business Analysis for Business Intelligence (1st Ed) by Bert Brijs, Auerbach                   

Publications, 2013.

  1. Business Intelligence Guidebook (1st Ed) by Rick Sherman, Morgan Kaufmann, 2014.
  2. Fundamentals of Business Intelligence by Wilfried Grossmann and Stefanie Rinderle-Ma, Springer, 2015.
 
Journals and Magazines:

Decision Support Systems, Elsevier.

International Journal of Business Intelligence and Data Mining, Inderscience.

International Journal of Business Intelligence Research, IGI Global.

Journal of Big Data, Springer.
 
Time Distribution and Study Load:
  • Lectures: 45 hours.
  • Self study: 75 hours.
  • Homework: 30 hours.
  • Project work: 30 hours.
 
Teaching and Learning Methods:

1. Lectures

2. Real-world case studies

3. Homework: Several homework exercises requiring students to apply the knowledge acquired from lecture and discussion will be assigned and graded.

4. Project: Students will propose and execute a plan for a significant business intelligence project in groups. Students should execute their projects independently under the guidance of the instructor and make a formal presentation of the results.

5. Self learning

 
 
Evaluation Scheme:

1. Mid-term exam 20%

2. Final exam 20%

3. Homework assignments 20%

4. Course Project 40%

A grade of “A” indicates excellent and insightful understanding of the key concepts and ability to implement sophisticated systems; “B” indicates a good understanding of the key concepts and ability to implement basic techniques; “C” indicates barely acceptable understanding and implementation ability; and “D” indicates poor understanding and implementation ability.
 
Instructor(s):
SECTION NAME
A Dr. Vatcharaporn Esichaikul