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Product Analytics

This course provides an introduction to one of the fastest growing specialized sub-domains of analytics and data science - product analytics. It will cover the three key areas of the domain over four weeks of study: introduction to metrics frameworks, product data collection principles and standards, and building a healthy experimentation program. This course is not aiming to provide a one-size-fits-all template to be reused across students’ companies, but rather to equip students with foundational ability to select appropriate frameworks and adapt them to their industry, growth stage, and data maturity levels. The primary audience for this course are “generalist” data analysts/scientists looking to get the most out of their technical skills with their targeted application to the highest leverage decision-making points and reduce their work’s time to value. However, the course is also made accessible from a technical standpoint to make it a good fit for product managers operating without consistent data support on their quest to be more efficient and data-driven.

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Elena Dyachkova
Sr Manager, Data at
US$ 400
or included with membership
4 weeks
Space is limited

Course taught by expert instructors

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Elena Dyachkova

Sr Manager, Data at

Elena Dyachkova is an experienced product analytics and data science leader. She has an educational background in Microeconomics via an MS degree from the Lomonosov Moscow State University. After spending early career in sports business and audience measurement industries, in 2018 she transitioned into product analytics. Elena joined Peloton in 2018 as the first data person on the product team. Over four years, she had grown the team, built out the measurement and experimentation frameworks, data collection practices, and product strategy decision support systems in the company encompassing the entire software and hardware engagement portfolio. Since 2022, Elena is building a product data science practice at a hyper growth stage mental health startup Spring Health.

The course

Learn and apply skills with real-world projects.

Who is it for?
  • Data analysts and data scientists looking to get started in the field of product analytics and set their organizations up for success in leveraging data for optimal decision making within the product development process.

  • Product managers looking to uplevel their analytical thinking and/or to collaborate more effectively with their data analytics counterparts.

  • Understanding of key descriptive statistics concepts (mean, median, percentile,variation), as well as notions of correlation and causality.

  • Technical/programming experience is NOT required for this course. However, technical project extensions will be provided for those who would like to implement course concepts with SQL/Python/R

Not ready?

Try these prep courses first

  • What is Product Analytics? How does it fit into the Product Development process?
  • From business metrics to product metrics: guiding effective macro-level decision-making with KPI frameworks
  • Cohort analysis: retention curves
  • (Optional bonus): define a habit moment metric via correlation/regression analysis
  • North Star metric and input metrics: trees-based and flywheel-based approaches
  • Key ‘product plays’
  • Perfect ideas imperfectly measured: bringing stakeholders to the table
  • Sketching and operationalizing a dashboard
  • Using ‘tree’ model and/or ‘enablers-constraints’ concept to define a product metrics framework
  • Define a product North Star and two levels of inputs for your product of choice
  • Technical bonus: sketch a product KPI dashboard
  • User interaction data: use cases and sources
  • Approaches to taxonomy standards
  • Approaches to operationalizing telemetry & collaborating with engineering
  • SaaS landscape overview: product analytics SaaS/CDP/specialized taxonomy tools
  • Feature launch metrics
  • Define instrumentation requirements for a feature
  • Pick a part of the product experience & define key health metrics
  • Define instrumentation requirements
  • Technical bonus: “sessionization” coding.
  • Why and when to use experimentation?
  • Hypothesis testing stats 101
  • Experiment design: from metrics to duration
  • Common decisions: to test or not to test, sequential or multivariate
  • Design an experiment
  • Brainstorm a product improvement based on an outcome metric that can be A/B tested
  • Define experiment metrics & design
  • Technical bonus: A/B test analysis

A course you'll actually complete. AI-powered learning that drives results.

AI-powered learning

Transform your learning programs with personalized learning. Real-time feedback, hints at just the right moment, and the support for learners when they need it, driving 15x engagement.

Live courses by leading experts

Our instructors are renowned experts in AI, data, engineering, product, and business. Deep dive through always-current live sessions and round-the-clock support.

Practice on the cutting edge

Accelerate your learning with projects that mirror the work done at industry-leading tech companies. Put your skills to the test and start applying them today.

Flexible schedule for busy professionals

We know you’re busy, so we made it flexible. Attend live events or review the materials at your own pace. Our course team and global community will support you every step of the way.


Completion certificates

Each course comes with a certificate for learners to add to their resume.

Best-in-class outcomes

15-20x engagement compared to async courses

Support & accountability

You are never alone, we provide support throughout the course.

Get reimbursed by your company

More than half of learners get their Courses and Memberships reimbursed by their company.

Hundreds of companies have dedicated L&D and education budgets that have covered the costs.


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