BI510 – Applied Data Analytics, Visualization, and Forecasting
Master of Science in Computer Science and Business Technologies
Core Course
BI510 – Applied Data Analytics, Visualization, and Forecasting
Course Unit Code: BI510
Type Of Unit: Elective
Level of Course Unit: Second cycle
Year of Study: First / Second year
Semester: On Demand
Number of ECTS Credits: 6
Class Contact Hours: 28
Mode of Delivery
Face to Face
Prerequisites
None
Course Objectives
The objective of this course is to teach students concepts and related state of the art software from the two stages of data exploitation i.e. business intelligence and business analytics. Business intelligence is related to reporting and visualization to support decision making by disseminating information throughout the organization. The decision support tool that will be used for this section is SAS Visual Analytics. Business analytics is related to mathematical and statistical techniques to support organizations make informed decisions. Business analytics is the union of two subfields that draw their roots from statistics and operations research. This course will focus on the intersection of the two subfields i.e. on data mining (pattern discovery and predictive analytics) and forecasting techniques. The decision support tool that will be used for this section is SAS Enterprise Miner along with a demonstration of SAS Forecast Server.
Learning Outcomes
- Learn the theory behind predictive analytics (predictive analytics concepts: prediction of new cases, input selection, complexity optimization, decision trees – CHAID, assessment of predictive models, predictive models deployment – scoring), pattern discovery (clustering, association rules).
- Apply data mining i.e. predictive modelling and pattern discovery techniques to real – life problems (customer segmentation, market basket analysis, customer response models, credit scoring) using SAS Enterprise Miner.
- Explore vast amounts of data to find patterns, trends and associations and create reports and dashboards using SAS Visual Analytics (Data Visualization Software).
- Apply Time Series and Econometric Forecasting methods using a state of the art solution i.e. SAS Forecast Server with applications in Demand Forecasting.
Course Content
Course Features
Planned learning activities and teaching methods
Lectures, case method, class discussion, syndicate group work/presentations, Computer based laboratory sessions (hands on use of the software: SAS Enterprise Miner, SAS Visual Analytics and demonstration of SAS Forecast Server)
Assessment methods and criteria
20% Class participation
80% Group Project
The final group project will be conducted in groups of two students. Students will be assessed on their knowledge on the theory of data mining and on their ability in using SAS Enterprise Miner. They will have to conduct three case studies related to predictive analytics, clustering and association rules with applications in churn prediction, , customer segmentation and market basket analysis (next best offer) respectively.
The class participation grade is related to the ability of the student to answer relevant questions during class, to comment on the material covered and to successfully complete the software exercises given during class.
(Assessment guidelines will be distributed on the first day of classes along with the course outline).
Language of Instruction
English
Work Placement(s)
Not applicable
Readings
Required Reading
1. Peter Christie et al, 2011. Applied Analytics Using SAS Enterprise Miner Course Notes. Cary: SAS Institute Inc.
2. Eric Rossland et al, 2014. SAS Visual Analytics: Getting Started Course Notes. Cary: SAS Institute Inc.
3. Fernandez George and Wellset Chip. Forecasting Using SAS Forecast Server Course Notes. Cary: SAS Institute Inc, 2013.
4. Course notes provided by the instructor.
Further Reading
5. Kattamuri S. Sarma. “Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications”. SAS Publishing, Third Edition.
6. Olivia Parr – Rud. “Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner: A Beginner’s Guide”. SAS Publishing, latest edition.
7. Gordon S. Linoff and Michael J. A. Berry. “Data Mining Techniques”. Wiley, latest edition.
8. Bart Baesens. “Analytics in a Big Data World”. Wiley, latest edition.
9. Chase Charlie W. Demand Driven Forecasting: A Structure Approach to Forecasting (2nd ed). Hoboken, NJ: Wiley, 2013.
10. Makridakis Spyros, Wheelwright Steven C. and Hyndman Rob J. Forecasting: Methods and Applications (3d ed). New York: Wiley, 1998.
English (UK) 
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