Customer Analytics in R
In this course you will work through a customer analytics project from beginning to end, using R.
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
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
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.
Our Instructors
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
2024
Instructors: Mr. Karolis Urbonas
Instructors: Mr. Karolis Urbonas
2025
Instructors: Mr. Karolis Urbonas
Instructors: Mr. Karolis Urbonas
Prerequisites
Familiarity with R (including the package ggplot2 and dplyer) is needed.
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Analysis of Survey Data from Complex Sample Designs
Additional 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.
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
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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.
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