Business Analytics for Product Managers is a course designed for an existing PM to skill up and build analytically smart products.

Analtyics for Product Managers is an advanced course designed for the existing PM to skill up and make Data Driven decisions

In today’s dynamic product environment, with data taking a center stage in all decision making, analytics becomes important in biting through the competition and building a rewarding product. If you aspire to become a product leader with expertise in trend analysis, predictive modeling, descriptive analytics & prescriptive analytics – this course is the right choice for you. In the final Capstone Project, you will build your own product using knowledge from data and also learn how to use the latest analytical tools.


04 April 2019




8* Weeks


$449* per month

Structure of the course

Self-paced course
Access to full learning material
Access to Case Studies
Quizzes on the study material
Assessment & feedback to be provided by the instructor
1-1 Mentorship sessions with industry experts
Group Critique sessions

How much time would you spend on the course?

12 hours= Curated study material
3 hours= Quizzes (30 min each for 6 sections)
30 hours= Case studies (3 hours each for 10 case studies)
7 hours= 1-1 mentorship sessions with industry experts (40 min each for 10 weeks)
10 hours= Group critique sessions (2 hours each for 5 sections) *optional

Total time= ~60 hours

We recommend that you follow a steady pace to spend these 60 hours over a period of 8 weeks & complete the course.


This Specialization Course is priced at $449 /mo. This includes 1-1 mentorship sessions, access to case-studies and full course material.

Early Access Price

The investment for the course is

$449 $379

But this is available only for a limited time. Reserve your seat before 31 March 2019 to avail this exclusive discounted price.


Analytics for Product managers course from Pragmatic Leaders is designed to address the problem of understanding number patterns and making decisions with them. With over 8 months of brainstorming & effort put in behind designing this unique pedagogy, we are here to make sure that you don’t fall behind in the race.

Your learning process has been scientifically developed keeping in mind the competencies you would need to be the best PM in the market with the most updated pragmatic knowledge of Analytics.


  • Existing Product Managers with min 2 years of experience

  • Entrepreneurs & founders who are looking to build data heavy

  • VP’s in Product/ Technology division of an organization, looking to advance their career in Analytics

  • Those who care about building a product in the new age

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Course Curriculum

Analytics 101
Data Driven Decision Making Details 00:03:00
What is Analytics & why is it important? Details 00:05:00
Solving a business problem using data Details 00:05:00
Making business defining decisions using data Details 00:05:00
Why is data and analytics framework needed? Details 00:03:00
Types of Data & Analytics Frameworks Details 00:10:00
Why is analytics important for product managers? Details 00:05:00
Choosing metrics for Product management analytics Details 00:10:00
Common Product Management Metrics Details 00:10:00
Transaction Metrics (MRR, GMV, CLV, CPA) Details 00:10:00
Engagement Metrics (ADU, Retention, Stickiness, Bounce Rates) Details 00:10:00
Case Study 1 Details 03:00:00
Case Study 2 Details 03:00:00
Quiz – Section 1 00:30:00
Sourcing & Understanding Data
Types of Data Details 00:03:00
Structured Data Details 00:05:00
Semi-Structured Data Details 00:05:00
Unstructured Data Details 00:05:00
How to source data for analysis Details 00:05:00
How to understand and find patterns in the various types of data Details 00:05:00
Role of excel in helping understand data Details 00:05:00
Basic Excel Functionalities Details 00:10:00
Do you need to change the data? Details 00:03:00
How will you connect the data? Details 00:03:00
Do you need to further consolidate the data? Details 00:03:00
Case Study 1 Details 03:00:00
Case Study 2 Details 03:00:00
Quiz- Section 2 00:30:00
Data Cleaning & Preparation
What is Data Cleaning? Details 00:05:00
Why is Data Cleaning important? Details 00:05:00
Steps to follow when cleaning the data Details 00:10:00
Various methods of Data Cleaning Details 00:10:00
Null/ Missing values Details 00:05:00
Outlier Treatment Details 00:05:00
Applying Mathematical Rules on the clean data Details 00:03:00
Probability Details 00:05:00
Bayes Rule Details 00:05:00
Binomial Distribution Details 00:05:00
Standardizing Details 00:05:00
Sampling of data Details 00:05:00
Confidence Interval Details 00:05:00
Hypothesis Testing Details 00:05:00
Testing out the clean data Details 00:05:00
Types of tests that can be performed on the clean data Details 00:03:00
T-test Details 00:05:00
A/B Testing Details 00:05:00
Case Study 1 Details 03:00:00
Case Study 2 Details 03:00:00
Quiz- Section 3 00:30:00
Data Analyzing
What are the different types of analysis? Details 00:10:00
What is the difference between these types of analysis? Details 00:10:00
Descriptive Analytics Details 00:05:00
Descriptive Data Collection -Survey Overview, NPS & Survey Design Details 00:10:00
Passive Data Collection Details 00:05:00
Hypothesis – driven analysis Details 00:00:05
Excel (data wrangling, lookups, pivot tables) Details 00:10:00
The 5 whys framework Details 00:05:00
Data visualization, and which chart to use Details 00:10:00
Causal Data Collection and tools Details 00:05:00
Predictive Analytics Details 00:10:00
Asking Predictive Questions Details 00:05:00
Regression Analysis – The Demand Curve, Making Predictions Details 00:10:00
Data Set Predictions Details 00:05:00
Identifying most & least profitable customers Details 00:10:00
Improve customer service using predictive algorithms Details 00:05:00
Price products differently using predictive analysis Details 00:05:00
Create personas and customize features using predictive solutions Details 00:05:00
Understanding how to use segmentation for the analysis Details 00:05:00
Probability Models Details 00:10:00
Implementation of the Model Details 00:10:00
Results and Predictions Details 00:05:00
Customer Lifetime Value Details 00:05:00
Cohort analysis Details 00:10:00
Modeling CLV in Excel Details 00:10:00
More SQL exercises, including aggregates, joins and date operation Details 00:00:00
Implementation of the Model Spreadsheet Details 00:00:00
Prescriptive Analytics Details 00:00:00
Using the Data to Maximize Revenue Details 00:00:00
Different cases of funnel analysis e.g. clickstream, campaign optimization Details 00:00:00
Hypothesis-driven thinking Details 00:05:00
Introduction to SQL Details 00:20:00
Intermediate SQL – Aggregates and Joins Details 00:20:00
Parameters of the Model Details 00:05:00
Market Structure Details 00:05:00
Competition and Online Advertising Models Details 00:05:00
Case Study 1 Details 03:00:00
Case Study 2 Details 03:00:00
Quiz- Section 4 00:30:00
Data Visualization
Distinguishing exploratory analysis from explanatory analysis Details 00:10:00
Design of Visualizations Details 00:05:00
Univariate Exploration of Data Details 00:10:00
Bar charts Details 00:05:00
Histograms Details 00:05:00
Axis Limits & Different Scales Details 00:03:00
Bivariate Exploration of Data Details 00:05:00
Scatterplots Details 00:05:00
Clustered bar charts Details 00:05:00
Violin bar charts Details 00:05:00
Faceting Details 00:05:00
Multivariate Exploration of Data Details 00:10:00
Plot Matrices Details 00:10:00
Encoding (size, shape, color) Details 00:05:00
Validating the outcome of Analysis; Is the output correct? Details 00:05:00
Case Study 1 Details 03:00:00
Case Study 2 Details 03:00:00
Quiz- Section 5 00:30:00
Tools for Analysis
What are the various tools available in the market to help with Data Analytics? Details 00:10:00
How to choose the right tool for presenting outcomes? Details 00:10:00
Breadth of Tracking Details 00:10:00
Integration with other tools Details 00:10:00
Resources needed to implement Details 00:10:00
Price Details 00:10:00
Support Details 00:10:00
Tool 1: MixPanel Details 00:30:00
Performing Segmentation on MixPanel Details 00:10:00
Performing Funnel Analysis on MIxPanel Details 00:10:00
Tool 2: Google Analytics Details 00:30:00
Funnel Analysis on GA Details 00:10:00
Dashboarding on GA Details 00:10:00
Tool 3: Amplitude Details 00:30:00
Marketing Analytics on Amplitude Details 00:10:00
Increasing conversion analysis on Amplitude Details 00:10:00
Tool 4: SQL Details 01:00:00
Querying & Data Extraction in SQL Details 00:15:00
Outlier Treatment in SQL Details 00:15:00
Revision Details 00:20:00
Quiz- Section 6 00:30:00
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