anhnguyendepocen / ca

Customer Analytics with R

Customer Analytics Course

This repository contains all necessary inputs to run the course hands-on labs.

Repository contents

Each session may contain:

• keynotes
• technical notes
• exercises
• solutions
• homeworks
• articles
• books

Vimeo Channel

In this channel, you can find all the public videos: https://vimeo.com/channels/1573371

Software Installation

We will use R and Rstudio. In some sessions, we will use additional packages for R.

Steps

Install R

• Download R from: https://www.r-project.org (in our case version 4.0.0): Windows, Mac
• Choose any mirror for downloading (for example cloud.r)
• Install and follow the instructions
• [Mac] Download clang 8.0.0 and gfortran 6.1 as well from here

Install Rstudio

We recommend Rstudio, an Interactive Development Environment (IDE) with lots of features that makes R easier to use:

• Download Rstudio Desktop from https://www.rstudio.com/products/rstudio/download/#download (in our case version 1.2.5042)

Optional

If you want to knit PDF files, it is recommended to install MikTex or MacTex, distributions for LaTex.

• [Mac] You can download the distribution for Windows from https://miktex.org/.
• [Windows] You can download the distribution for Mac from https://www.tug.org/mactex/.
• In case you don't need all the features of a complete Tex instalation, you can install only the TinyTex package.

In case you need a multipurpose text editor, I recommend Atom.

References

• Analyze Core
• Data Apple
• BTYD: Implementing Buy 'Til You Die Models
• BTYDplus: R package for Customer Behavior Analysis
• BCA: Business and Customer Analytics
• bayesm: Bayesian Inference for Marketing/Micro-Econometrics

DataCamp

This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.