Data-driven Product Management with R

A thought-provoking and practical course that transforms the way you drive influence with data.

5.00 (2 reviews)
Data & Analytics
5 hours
Feb 2022
last update
regular price

What you will learn

Drive cross-functional collaboration by combining diverse datasets for better decision making.

Tell compelling stories with layered and clean data visualization.

Hone your instincts about customers and markets by exploring your data and spotting trends and anomalies.

Democratize data for effective decision making by setting up a reproducible data environment.

Understand customer behavior and predict outcomes like retention through customer segmentation and cohort analysis

Spot and study outliers to better understand corner cases and potential red flags.

Boost your productivity and grow your influence by automating report generation


What's this course about?

This is the first part of a series of analytics courses that are fine-tuned for product managers. It covers a carefully-curated list of topics like outlier analysis, exploratory data analysis (EDA) and cohort analysis. These topics are taught using product management specific use cases for immediate application to your daily work. The overarching goal of the course is to enable product mangers influence customers and stakeholders using data.

What’s unique about this course? Why should I care?

First, this is not a programming language course. This course teaches you to use R as a tool to advance your career and business goals. The R notebooks provided with this course are meant to be run with minimal training. They have been rigorously tested on Windows and Mac. Updates if any, will be posted in a timely manner. This was done so learners can focus on modifying the notebooks for their specific needs.

How will it benefit me?

This is a highly curated course with a very narrow target learner - the product manager. With this course, product managers will save months of time they would spend learning R and applying it to their work. The curriculum focuses on practical implementation so the material is concise and precise. You will not be bombarded with hours of lectures and hundreds of source files only to find yourself confused about what's next.

Why R?

R is a scripting language that is widely used by research scientists and statisticians - not software programmers. It is easy to learn and master and you get results instantaneously. If scientists, with little or no programming skills can master R, so can you. R has a very active and solid community that maintains existing functionality and regularly introduces new innovation. As R is statistical software, you will find several excellent packages for every statistical procedure imaginable. What's more, you can also write powerful ML models in R easily.

The best part is that R is free and secure. It is not as CPU-hungry as most spreadsheet tools and can be run in the cloud as well. It also works seamlessly with popular IDEs like VS Code.

How do I use it at work?

Remember, this course has been created by a product manager for other product managers. The code samples can be run straight out of the box and modified endlessly.

The R Notebooks contain all the code being taught in the class. They can be run on any Windows or Mac laptop. It is highly recommended that you take a hands-on and curious approach to this course. Modify the files to suit your needs.

What does this course cover?

This course focuses on descriptive analytic techniques to facilitate data-driven decision making and cross-functional collaboration. In addition, this course covers entry-level data engineering topics like EDA and data management. These topics are introduced early to serve as foundations for the rest of the course. Three key career skills are addressed for product managers:

Cross Functional Collaboration

  1. Creating a Reproducible Data Analysis Environment

  2. Creating a Master Dataset for Inter-departmental Collaboration

Storytelling with Data

  1. Building Customer Profiles

  2. Segmentation Using Indicators

  3. Translating Feature Usage to Retention

  4. Learning from Extreme Customers

Automation of Data Analysis Tasks

  1. Automating the Data Curation Process

  2. Creating Reproducible Reports

In future courses, I will cover prescriptive and predictive techniques.

Are there any copyright issues

R is a very popular language and there are thousands of free and paid resources available on the internet. To avoid copyright infringement, I have developed the data set used in this course. It is not copied from any paid or free repository. All the code in this course has been developed by me.

What if I have problems?

If you have questions about the course, send me a note in the course and I will respond within 24 hours.

How long will it take me?

The total course duration is approximately 5 hours spanning 9 sections. By blocking off 1-hour or so a day, you can finish the course in 10 days. You can also go at in Boot Camp style and finish it over a weekend.

To get the most out of this course, prioritize your learning time and stick to the plan. There is no shame is copy-pasting code and there are no brownie points for memorizing the function and parameter names. If you obsess over them, you will not do yourself any justice. Just understand the overall flow of each lesson and how the code is organized. Focus on running the notebook and studying the results. Then modify the code to suit your needs, run the notebooks, and study the results again. Rinse and repeat.

What kind of machine do I need?

In comparison to traditional spreadsheet software like Microsoft Excel, R is not a resource-intensive software. A Windows or Mac laptop with 8 GB of RAM is more than sufficient to run the exercises in this course. Check the R and R Studio sites for detailed system requirements.


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Welcome to the Course

Getting Started with R and R Studio

Getting Started with R and R Studio
Using R Scripts
Managing R Packages
Projects in R Studio

Facilitating Collaborative Decision Making

Facilitating Collaborative Decision Making
Reading Data from Spreadsheets
Combining Inter-departmental Data
Data Visualization 101

Preparing Data for Analysis

Introduction to Data Preparation Using Tidyverse
Standardizing Variable Names
Formatting Variables and Data
Slicing and Dicing - Extracting Columns
Slicing and Dicing - Extracting Rows

Hone your Instincts with Data

Honing your instincts
Introduction to EDA
Enumerating all Variables
Listing all Variable Properties
Studying Correlations
Summarizing Data
Creating Pivot Tables
Preparing a summary table

Studying Customer Segments

Introduction to Customer Segmentation
Creating Business Indicators
Visualize Customer Segments

Studying Customer Cohorts

Introduction to Cohort Analysis
Generating a Cohort Table
Creating a Cohort Chart

Studying Outliers

Introduction to Outlier Analysis
Identifying Outliers Using the IQR Method
Visualizing Outliers Using Histograms
Visualizing Outliers Using Boxplots
Adding Labels to Box Plots

Telling Stories with Data

Introduction to Grammar of Graphics
Words to Charts
Preparing Data for Visualization
Introduction to Layers in ggplot
Building a 2-layer Plot - Part 1
Building a 2-layer Plot - Part 2
Labeling a Stacked Bar Chart
Using non-traditional Aesthetics
Standardize visualizations with themes

Creating Reports using Notebooks

Introduction to R Notebooks
Basic Text Formatting
Setting up and using R Notebooks



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