1. Workshop: JMP & R for Analytics Instruction
Stephen Hill
&
Barry Wray

2. JMP

3. Descriptive Analytics
Analyze/Distribution
Continuous
Box plots/outliers
Scale for X axis
Selecting a cell or many cells (by selecting a “portion” of a cell the user call learn something about how the data is dispersed within a range)
Ordinal – Be sure to use “Value Ordering”
Nominal – Detect data cleaning issues in labeling

4. Descriptive Analytics

5. Descriptive Analytics
Graph Builder – Freedom to choose and be creative

6. Descriptive Analytics
Scatter Plot 3D – See patterns

7. Predictive Analytics Ease of creating (Stratified) Validation sets
Regression (Fit model)
SLS and GLM
Stepwise
Nominal Logistic
Ease of adding Cross products and Factorial designs
Artificial Neural Networks
Flexibility of Hidden Layer (# nodes, activation method (Sigmoid, Tangent, Linear, Gaussian)
Penalty Method
Decision Trees – Recursive Partitioning
Classification/Regression
Kfold Validation
Bootstrap Forest, Boosted Tree, K nearest neighbors, Naïve Bayes

8. Data Cleaning Columns Rows (exclude) Tables Recode
Make indicator columns
Combine Columns
Explore Missing values – Multivariate Normal Imputation
Explore Outliers
Rows (exclude)
Tables
Concatenate
Transpose
Vlookup (like)
Split
Stack
Join

9. R & RStudio

10. Context Undergraduate “Big Data” Analytics Course
Required for BAN Concentration, Elective for Others
Only Prerequisite: Business Statistics
Topic Coverage:
Data Preparation
Descriptive Analytics
Visualization
Focus on Predictive Analytics

11. Unofficial Textbook
Free (Legally!)
r4ds.had.co.nz

12. The Tidyverse

13. The Tidyverse

14. Workflow Working Directory R Markdown Document R Project Data .Rproj
.Rmd
Data
.csv, etc.

15. R Markdown Code (shown in Markdown Pad)

16. R Markdown Code (shown in Markdown Pad) Detail View of R Code Chunk

17. Knitted HTML
Link

18. Lessons Learned Don’t Underestimate Learning Curve
Use Frequent Assignments and Feedback
Plenty of Online Resources Available
Be Prepared to Evolve