1. Writing Intensive/Engaged Learning Business Analytics Class
Mary Malliaris
Loyola University Chicago
Presentation for DSI 2016

2. Student & Class Levels Student: junior/senior level Type of Class:
Counts toward IS major
Not required (one of a number of electives),
All students would have had Statistics and Intro to Information Systems
Class is capped at 24

3. Why Writing Intensive/Engaged Learning?
Employers are asking for more communication skills (oral and written),
All students are required to take at least two writing intensive classes; one must be in their major.
The University has a requirement that all students complete at least one engaged learning class.

4. Major class requirements
Analytics techniques (we used IBM’s SPSS Modeler)
Read case & write response each week
2 short presentations in class about cases
Team research project
Symposium presentation

5. Team Project
A practical learning experience in developing and delivering a research project in data mining & analytics.
Students had to decide on a problem, describe their data collection and manipulation, analysis process, and the potential benefits of expected findings

6. The Final Project Write-up Included
Question that was driving the analysis
Data selection, origin and manipulation
Technique(s) used for the analysis and why
Results from the technique
The answer or action for the initial problem addressed
Discussion of how they would judge the effect of their solution if implemented

7. Weekly Template: Monday
Analytics technique and practice (class in the lab)
For example: Association Analysis, Cluster Analysis, Decision Trees, Neural Networks, Logistic Regression, Support Vector Machines

8. Weekly Template: Wednesday
Case paper using Monday’s technique
Each week: All students wrote a short criticism of the paper, as though they were a journal editor, focusing on both the strong and the weak points in the analysis and writing.
These were due before class on Wednesday.

9. Weekly Template: Wednesday
One person was designated as lead presenter and one as lead critic. Each gave a short overview in class.
The lead presenter: presentation of the research story and results; emphasis on the best of the writing style
The lead critic: the weak points with emphasis on the worst parts of the writing, research, and results.
After these, the class was opened for discussion of the paper overall and we talked about each section’s good and bad points.

10. Weekly Template: Friday
Team assignment day for work on their project
Identify problem
Collect data, clean, join, modify
Collect references
Write paper parts (abstract, introduction, literature review, data and model, results, conclusion, recommendation for future research)

11. Example papers: Technique: Association Analysis
Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool
Technique: Cluster Analysis
A cluster analysis of service utilization and incarceration among homeless youth

12. Example papers: Technique: Decision Trees
Financial profiling of public hospitals
Technique: Neural Networks
Neural Networks in Basketball Scouting
Technique: Regression
Childhood and Adolescent Television Viewing and Antisocial Behavior in Early Adulthood

13. Sample Student Projects:
A Neural Network Analysis of U.S. Voter Turnout Rates
Entrepreneurship and Business Success in Chicago
Association Analysis of Age, Race and Gender in Unemployment Rates across the United States
Crime in the Windy City: A Loyola Student’s Guide to Off‐Campus Crime

14. Student Responses Enjoyed being able to criticize other’s work
Felt more empowered
Liked working on a problem that had direct implications in the world
Thought they were more connected to Loyola after symposium presentation

15. Questions?