1. Data Analytics at CNU Dmitriy Shaltayev
Associate Professor of Management
Christopher Newport University

2. Introduction to Data Analytics
BUSN 305
Undergraduate program only at the moment
Four majors: ACCT, FINC, MGMT, MKTG
Business core class (required for all majors, except ACCT)
Replaced general Management Information Systems class in fall 2017
Class received favorable evaluations after first semester
Material is still being tweaked around

3. Software Used Tried combination of JMP and R
Selection was done in favor of JMP for an introductory class
Tableau is used for visualizations
Continuation of JMP from business statistics class
Course is meant to be a “story telling with data” class
Focus is on both informing students of popular data analytics techniques and effectively value of communicating results

4. BUSN 305 Course Content
What is “big data”? After this introduction never using terms “big data”, working with “small data”
Descriptive/diagnostic/predictive/prescriptive
Focus is on the first three
Visualization:
Use JMP
Focus is on Tableau
Types of charts, focus on line, bar, bubble, boxplot, map (bubble and filled), treemap, tables/highlight tables/ heat maps
Why pie charts are bad?

5. BUSN 305 Course Content Pre-attentive attributes
Use of color in visualizations, color blindness
Invited speaker to discuss effective oral presentations delivery techniques
Supervised methods:
Classification/regression trees
Multiple regression
Logistic regression
Neural networks
Unsupervised methods:
Clustering (k-means, hierarchical)

7. Learning by Doing Four group projects (groups of 3)
Project 1 – build charts with Tableau (line, bar, map, treemap), create a fully interactive dashboard
Project 2 – use classification tree to predict delayed flights from IAD/DCA/BWI to JFK/LGA/EWR
Introduced to idea of binning continuous variable
Project 3 – predict movie box office based on budget, genre, cast composition. Choose three movies and predict their box office
Project 4 – choose data set, create presentation, deliver it to class (10-15 minutes)
Graded by both peers and instructor

8. Learning by Doing Each project should follow the structure:
What is the issue at hand and why is that important
Describe the data which was given and any data manipulations (binning)
Any preliminary insights from data visualization
Explain the choice of modeling tool selection
Discuss model properties
Which variables are important/significant
How accurate is the model?
Model applications examples

9. Advanced Data Analytics (MGMT 495)
Issues relevant to data analytics
How data are stored (database design principles)
How data are retrieved (SQL “by hand”)
What are the most popular data formats (.xlsx, .csv, .json, .xml)
Data quality issues
Outliers
Missing values and imputation
Skewed distributions

10. Advanced Data Analytics (MGMT 495)
Introduction to Python programming
Variables
Built-in functions
If-else
For loops and while loops
User-defined functions
Importing libraries
Most popular visualization libraries: pandas, seaborn, matplotlib

11. Advanced Data Analytics (MGMT 495)
Advanced modeling techniques
Random forest
A/B testing
Association rules
Text mining
Feature engineering

12. Where Is It Going?
Plans are to split a current management concentration and create a “HR/Leadership” and “Decision Analytics” concentrations
While introductory class is required for everyone, advanced analytics will be in “Decision Analytics” stream
Trying to develop partnerships with companies
Sentara Healthcare
Booz Allen Hamilton
Ferguson Enterprises