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 0
- 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
- 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
- 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 â Detect outliers and make a decision how to define new monthly behavioral customer segments
- 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
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
2022
Instructors:
2023
Instructors:
Instructors:
2024
Instructors:
Instructors:
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
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.
Options for Credit and Recognition
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
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Analysis of Survey Data from Complex Sample Designs