Course curriculum

  • 1

    The Powers and Limitations of R

    • Where R came from and why you should care

    • Where Excel stops and R begins

    • What R does best

    • What R doesn't Do

    • The career value of learning R
  • 2

    A Package is Worth a Thousand Insights

    • Finding the right R package

    • Investigating packages for problem/question fit

    • Implementing the package you have chosen

    • A few examples of frequently used packages

    • The power (and tedium) of cleaning your data
  • 3

    R for Marketing and Finance

    • Terminology differences between statistics and computer/data science

    • My average day on the job

    • Marketing use cases for R

    • Diving into specific models
  • 4

    Random Forest

    • Getting a lot of power out of a little experience

    • Concept of a decision tree - surviving the Titanic

    • A worked example: solving a regression problem

    • A worked example: taking model training to the next level

    • When your Stack Overflows
  • 5

    Customer Lifetime Value

    • The power and potential of CLV

    • Data input requirements

    • A worked example: predicting the net present value of each customer - the BTYD R package

    • A worked example: walkthrough of BTYD R package

    • A worked example: Base R vs Tidyverse

    • A note on what to do with larger and smaller companies

    • Case Study/Story - “win-back” returns
  • 6

    The Analytics Conversation in an Organization

    • Who’s talking about analytics these days?

    • Coming in as a consultant

    • Navigating a career in data analytics

    • What's next?