I hope you are ready! The syllabus can be found here.
We will work with two books.
For the first half of the course, we will work with the Handbook of Regression Modelling for People Analytics with Examples in R, Python, and Julia
For the second half of the course, we will work with Forecasting: Principles and Practice, 3rd Edition
To hit the ground running, you will probably need two key bits of software: R and RStudio. To wit,
If you prefer something like VSCode or another IDE, that is your choice. I will heavily utilize
RMarkdownandquartoand both are extensively supported in RStudio. The most recent version of RStudio can be obtained hereIf you wish, the first half of the course can also be completed with either Python or Julia so you can defer
Rfor now. R can be obtained from this link. As an R user, I am no expert in Python or Julia and this limits my ability to support those that choose to deploy them.
The tentative reading plan is:
People Analytics
- Weeks 1 and 2: Review of Linear Models and Inferential Statistics [chapters 1-4]
- Week 3: Binomial Logistic Regression [chapter 5]
- Week 4: Ordered and Multinomial Logistic Regression [chapters 6 and 7]
- Week 5: Hierarchical Data [chapter 8]
- Week 6: Survival Analysis [chapter 9]
- Week 7: Power Analysis: How much data do I need? and Review [chapter 10]
Forecasting
- Weeks 8 and 9: The Basics, Time as a Variable, and Decomposition [chapters 1-5]
- Week 10: Judgemental Forecast and Regression
[chapters 6 and 7] - Week 11: Exponential Smoothing and ARIMA
[chapters 8 and 9] - Week 12: Dynamic Regression [chapter 10]
- Week 13: Hierarchies, advanced forecasting and related issues
[chapters 11-13] - Week 14: Presentations on a people analytics problem and a time series forecast Date TBA: We will have to reschedule this because it represents the cancelled class the first week.