Customer Analytics in R

Customer Analytics in R

taught by Karolis Urbonas

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Aim of Course: In this course you will work through a customer analytics project from beginning to end, using R.  You will start by gaining an understanding of the problem and the context, and continue to clean, prepare and explore the relevant data.  You'll work on feature engineering, handling dates, summarization, and how to work with the customer lifecycle concept in data analysis.  The course culminates with a report that you will write, and a recommendation that you will prepare for a hypothetical company.
This course may be taken individually (one-off) or as part of a certificate program.
Course Program:

Week 1 - Exploring and preparing transactional dataset for analysis with R

  • Practical exploration of transactional retail industry dataset - understanding distributions and meaning of variables
  • Cleaning data
  • Summarizing data with dplyr
  • Preparing a customer summary table for initial analysis
  • Homework - finishing R code in the R Markdown

Week 2 - Analyzing customer summary table with R

  • Analyzing customers using the customer summary view built in week 1
  • Looking for outliers and dealing with them
  • Plotting data with ggplot2
  • Exploring distribution of variables and building behavioral customer segments
  • Writing your own R functions for dplyr & ggplot2 for faster analysis
  • Analyzing created segments and making business recommendations
  • Homework - create new segments on your own, build new features, make your own business recommendations

Week 3 - More advanced techniques for feature engineering and transactional data analysis with R

  • Introduction to customer lifecycle and how to think about it from data perspective
  • Advanced dplyr - introduction to window functions e.g. LAG, to build monthly customer summary data snapshots
  • Introduction to cross-joins in R to build monthly summary table
  • Extensive dealing with dates - learning about lubridate package
  • Creating new segments based on learnings from weeks 1 and 2
  • Homework – Detect outliers and make a decision how to define new monthly behavioral customer segments

Week 4 - Exploring trends in customer behavior with R and the Capstone project

  • Best industry practices in plotting transactional data trends of customers with ggplot2
  • Analyzing monthly summary data and making conclusions
  • Capstone project: Practical customer analytics case project where you will write a business recommendation for a hypothetical company


Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course capstone project.  Note: There will be a mid-week discussion exercise in the first week of the course.


Customer Analytics in R

Who Should Take This Course:

Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This course is especially useful if you want to understand customer analytics, undertake pilots with minimum setup costs, manage analytics, or work with consultants or technical experts.


You should be familiar with introductory statistics.  Try these self tests to check your knowledge.

Familiarity with R (including the package ggplot2 and dplyer) is needed.

Organization of the course

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirement:
About 15 hours per week, at times of  your choosing.

Options for credit and recognition

Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:

  1. No credit - You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. Certificate - You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. CEUs and/or proof of completion - You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course,  CEU's and a record of course completion will be issued by The Institute, upon request.
  4. Digital Badge - Courses evaluated by the American Council on Education have a digital badge available for successful completion of the course.  
  5. Other options - Specializations, INFORMS CAP recognition, and academic (college) credit are available for some courses

College credit:
Customer Analytics in R has been evaluated by the American Council on Education (ACE) and is recommended for the upper division baccalaureate degree category, 3 semester hours in statistics, programming or data mining. Note: The decision to accept specific credit recommendations is up to each institution. More info here.

This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP® analysts accrue Professional Development Units to maintain their certification .
Course Text:
All required study materials will be provided in the course.
You must have a copy of R for the course. Click here for information on obtaining a free copy.


May 15, 2020 to June 12, 2020 November 13, 2020 to December 11, 2020 May 14, 2021 to June 11, 2021 November 12, 2021 to December 10, 2021

Customer Analytics in R


May 15, 2020 to June 12, 2020 November 13, 2020 to December 11, 2020 May 14, 2021 to June 11, 2021 November 12, 2021 to December 10, 2021

Course Fee: $549.00

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

Group rates: Email jdobbins "at" to get information on group rates. 

First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

The Institute for Statistics Education is certified to operate by the State Council of Higher Education in Virginia (SCHEV).

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