Customer Analytics in R

Customer Analytics in R

In this course you will work through a customer analytics project from beginning to end, using R.

In this course you will work through a customer analytics project from beginning to end, using R.

$799 | Enroll Now
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Overview

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.

Introductory Level Course
100% Online Courses
4-Week Course
ACE + CAP Credit Eligible
Expert Instructors
Teacher Assistant Support
Tution-Back Guarantee

Learning Outcomes

After completing this course you will be able to:

  • Explore and prepare a transactional database for analysis
  • Explore distribution of variables and build behavioral customer segments
  • Make business recommendations on basis of segmentation
  • Incorporate customer lifecycle analysis into planning
  • Apply best industry practices in plotting transactional data trends of customers with ggplot2

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.

Instructors

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Karolis Urbonas

Mr. Karolis Urbonas

Mr. Karolis Urbonas, currently Head of Data Science, Amazon Devices, is a passionate data leader who has vast hands-on experience in building data science projects that have shaped and disrupted the business strategies of global companies. Karolis has been working with data and its applications for more than 10 years now. Formerly he was leading the applied customer and business analytics unit in Western Union as its head of analytics.

As a data science executive, he has a demonstrated history of building high-performing data science teams and delivering strategic analytic projects.

See Instructor Bio

Course Syllabus

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

Class Dates

2023

May 12, 2023 to Jun 9, 2023

Nov 10, 2023 to Dec 8, 2023

2024

May 10, 2024 to Jun 7, 2024

Nov 8, 2024 to Dec 6, 2024

2025

No classes scheduled at this time.

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Prerequisites

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

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

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What Our Students Say​

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Left Square Qoute

I really enjoyed the format of the course. Wasn't by the book and you could carve your own path out. Really enjoyed working with a real life database and scenarios. Gave me great examples for my job.

Geoff Ogrin
Equifax
Right Square Qoute
Left Square Qoute

This course provided a realistic approach to the analysis of customer data. The steps involved in the preparation of the data prior to analysisunderscored the need for a solid understanding of the business problem to besolved and the programming skills required. The R code provided in the coursewas terrific especially the use of the tidyverse series of functions andoperators. The course was a strong introduction into that style of programming. Likewise, with so much of the R code provided it was easier to focus on solvingthe business problem.

Peter Mulready
System Architect at Boehringer Ingelheim Pharmaceutical
Right Square Qoute

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.
  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.
Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Topic: Analytics, Prediction/Forecasting | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Jul 7, 2023, Nov 10, 2023, Mar 9, 2024, Jul 5, 2024, Nov 8, 2024

Additional Course Information

Organization of 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 Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

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.

Course Text

All required study materials will be provided in the course.

Software

You must have a copy of R for the course.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
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.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is 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.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the upper-division baccalaureate degree, 3 semester hours in statistics, data mining, or programming. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

 

Chrome

 

Firefox

 

Safari

  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Miscellaneous

There is no additional information for this course.

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Customer Analytics in R
$799 | Enroll Now
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