Reference Materials


I have included some nice reference materials so that I can always get to this information quickly. These are pdf files that you can download.

  1. The Periodic Table of Alteryx Tools – Front Side
  2. The Periodic Table of Alteryx Tools – Back Side
  3. Regular Expressions Cheat Sheet



Click the image to go to the Alteryx community article that describes how Tara developed this great resource.

Online References

  1. Tableau Mapping
  2. Parsing Command Line Parameters (thanks to Joe Mako)

R-Based Data Science Curriculum


DataCamp Courses

DataCamp soundly believes in educating people to be the best data scientists possible.  As such, they allow students to take as many classes as they would like for free while enrolled…and there are a LOT to choose from, not only in R but in Python, SQL, and others.  Below is a comprehensive list of classes that were available in Jan 2017.

Make sure to register for Data Camp.

R Programming

  • Introduction to R (mostly working with data structures like vectors, matrices, factors, dataframes and lists)
  • Intermediate R (if/then, loops, functions, the apply family, functions and debugging, working with text via regular expressions and substitutions, working with dates)
  • Working With Dates and Times in R (using the lubridate package)
  • Writing Functions in R (uses the purr package) to help write functions and is the “dplyr” of function-writing; course covers handling errors, arguments, etc., and it a bit more advanced treatment.
  • Writing Efficient R Code (benchmarking/timing, profiling, parallel programming, very advanced stuff)
  • Reporting with R Markdown (those .Rmd files you’re always using…)

Reading in, Cleaning, and Manipulating Data

Working with and Summarizing (Structured) Data




Machine Learning and Data Mining (BAS 474 stuff!)

Time Series and Forecasting (BAS 475 stuff)

Probability and Statistics 

Spatial Analysis (geo-spatial statistics)

Network Analysis in R (e.g., social networks)

  • igraph is an amazing package in R that handles nearly every aspect of network analysis you might be interested in

Finance in R