MSCD 661 - Business Intelligence: Syllabus

Instructor Information

Refer to Discussion Forum, Facilitator Introduction and Expectations

Course Title

MSCD 661 - Business Intelligence

Course Description

Business Intelligence (BI) is a set of architectures, theories, methodologies and technologies that transform structured, semi-structured and unstructured data into meaningful and useful information. Students will analyze enterprise data requirements to develop queries, reports and build OLAP cubes that use business analytics to answer complex business questions.

Prerequisite Courses

MSCD600 – Database Architecture

Course Outcomes

Upon completion of this course, learners should be able to:

Discuss the impact of Business Intelligence (BI) theories, architectures, and methodologies on the organizational decision making process.
Analyze the differences between the structured, semi-structured and unstructured data types to leverage the best technologies.
Explain how different data can be integrated for querying and reporting to improve the performance of marketing and sales strategies.
Explore the ACID and BASE theories for data storage and consistency.
Conduct enterprise-wide data requirements analysis to create a BI solution.
Use OLAP tools to import data into multi-dimensional data cubes.
Develop Ad hoc queries, reports, spreadsheets, dashboards and mobile BI applications using business analytics to answer complex business questions using data from a variety of sources, such as data files and relational/NoSQL databases.

Course Materials

Required Texts

Sharda, R., Delen, D., & Turban, E. (2015).Business Intelligence and Analytics: Systems for Decision Support (10th ed.). Upper Saddle River, NJ: Pearson. ISBN-13: 978-0-13- 305090-5.

Required Resources

Power, D. J. (2014) “What is ACID and BASE in database theory?”

Sheldon, R. (2014). “Oracle gets away from relational DB with NoSQL database architecture”

Technology Tools

CC&IS Virtual Lab: Login to your Citrix account using your Regisnet ID and password with the URL shown below. The browser that we recommend is Google Chrome or Firefox (avoid IE). The BI labs will provide detailed instructions for using the various tools that you will use during this course. http://myregisapp.regis.edu/Citrix/StoreWeb/

technical specifications

Optional Resources

Purdue Online Writing Lab (OWL), http://owl.english.purdue.edu/owl/resource/560/01/

American Psychological Association (current edition). APA Style, http://www.apastyle.org/

Pre-Assignment

See Assignments and Activities table below for Week 1.

Course Assignments and Activities

Assignments for Online Course
Week	Readings	Graded Assignments or Assessments (Percentage)
1: An overview of Business Intelligence, Analytics and Decision Support including Foundations and Technologies for Decision Making	Sharda, R., Delen, D., & Turban, E. (2014) Chapters 1, 2	Introductions – initial response required by Wednesday of Week 1 By the end of the week: • Respond to Week 1 Discussions • Write a Scholarly Response on the topics of DSS, BI and BI Analytics • Complete Lab 1 - Introduction to the Regis BI Lab Environment with Pivot Tables Begin working on the Annotated Bibliography that is due week 7
2: Big Data and Analytics	Sharda, R., Delen, D., & Turban, E. (2014) Chapter 13 Power, D. J. (2014) “What is ACID and BASE in database theory?” Sheldon, R. (2014). “Oracle gets away from relational DB with NoSQL database architecture”	Discussion Questions Scholarly Response 2 Lab 2 – Hortonworks Sandbox
3: Descriptive Analytics - Data Warehousing and Business Reporting	Sharda, R., Delen, D., & Turban, E. (2014) Chapters 3, 4 Kimball, R., & Ross, M. (2013). Big Data Analytics. The Data Warehouse Toolkit (527-542). Indianapolis, IN: John Wiley & Sons, Inc	Discussion Questions Scholarly Response 3 Lab 3 – Regis BI environment with Pivot Tables
4: Predictive Analytics - Data Mining and Predictive Modeling	Sharda, R., Delen, D., & Turban, E. (2014) Chapters 5, 6	Discussion Questions Scholarly Response 4 Lab 4 – Rapid Miner
5: Predictive Analytics (continued) - Text Analytics, Text Mining, Sentiment Analysis, Web Analytics, Web Mining and Social Analytics	Sharda, R., Delen, D., & Turban, E. (2014) Chapters 7, 8	Discussion Questions Scholarly Response 5 Lab 5 – Rapid Miner
6: Prescriptive Analytics - Model-Based Decision Making, Heuristic Search Methods and Simulation	Sharda, R., Delen, D., & Turban, E. (2014) Chapters 9, 10	Discussion Questions Scholarly Response 6 Lab 6 – Rapid Miner
7: Prescriptive Analytics (continued) - Automated Decision Systems, Expert Systems, Knowledge Management, and Collaborative Systems	Sharda, R., Delen, D., & Turban, E. (2014) Chapters 11, 12	Discussion Questions Scholarly Response 6 Lab 6 – Rapid Miner
8: Review of the BI Course Material, BI Future Trends	Sharda, R., Delen, D., & Turban, E. (2014) Chapter 14	Discussion Questions Scholarly Response 7 Lab 8 Final Exam
		TOTAL:

Summary of Assignments and Percentage Weight:

Assignments	Weighted Percentage
Discussion Questions/Class Participation (8 x 2.5%)	20%
Scholarly Response Papers (7 x 2.85%)	20%
Labs (8 x 3.125%)	25%
Annotated Bibliography	10%
Final Exam	25%
TOTAL	100 %

CCIS Policies

Review the CCIS Policies on the Regis University website.

OTHER INFORMATION

NOTE TO LEARNERS: On occasion, the course facilitator may, at his or her discretion, alter the Learning Activities shown in this Syllabus. The alteration of Learning Activities may not, in any way, change the Learner Outcomes or the grading scale for this course as contained in this syllabus. Examples of circumstances that could justify alterations in Learning Activities could include number of learners in the course; compelling current events; special facilitator experience or expertise; or unanticipated disruptions to class session schedule.