Product Data Goal

The goal of the data course is to support product teams to deliver more user and business value by understanding better how to collect, analyse, experiment with and make decisions with data to support the business in gaining velocity and executing on the right priorities.

Benefits for Product teams include….

  • Understand how data fits into the way products work

  • Determine how to measure the success of your product using key performance indicators

  • Strengthen our product teams’ ability to make data informed decisions

  • Respond to the common challenges that our teams face when working with data

  • Equip product managers to autonomously interpret their data and to better collaborate with their product analysts

  • Enable our product teams to run better experiments by using data to build their hypotheses and run AB tests that are statistically correct

Who is the course for?*

  • Product teams

  • Product Manager/Product Owner

  • Data Analyst

  • Developer/Engineering Manager

  • *Individuals who are not part of product teams can go through the self-serve content to support their ongoing development.

Content

  • 1

    Part I

    • Overview

    • Lesson 5.0: Why data is important for product

    • Lesson 5.1: Why Experimentation, Why AB testing?

    • Lesson 5.1: Quiz

    • Lesson 5.2: Scientific method

    • Lesson 5.2: Quiz

    • Lesson 5.3: What is an AB test?

    • Lesson 5.3: Quiz

    • Lesson 5.4: Necessary conditions to run AB testing

    • Lesson 5.4: Quiz

    • Lesson 5.5: Testable hypotheses

    • Lesson 5.5: Quiz

    • Lesson 5.6: Writing a good hypothesis

    • Lesson 5.6: Quiz

    • Lesson 5.7: Primary Metrics

    • Lesson 5.7: Quiz

    • Lesson 5.8: Choosing the right health metric

    • Lesson 5.8: Quiz

    • Lesson 5.9: What is a sample?

    • Lesson 5.9: Quiz

    • Lesson 5.10: Representativeness

    • Lesson 5.10: Quiz

    • Lesson 5.11: Let’s calculate the sample size

    • Lesson 5.11: Quiz

    • Lesson 5.12: Analysing the results of an AB test: Pre-test Analysis

    • Lesson 5.12: Quiz

    • Lesson 5.13: Analysing the results of an AB test: Results Analysis

    • Lesson 5.13: Quiz
  • 2

    Part II

    • Lesson 5.14: Testing big changes

    • Lesson 5.14: Quiz

    • Lesson 5.15: What an AB test cannot measure

    • Lesson 5.15: Quiz

    • Lesson 5.16: Incrementalism in Product Design

    • Lesson 5.16: Quiz

    • Rate your satisfaction on Module 5

    • What's next?

Learn more about the course


Instructors

Datawarehouse Project Manager

Javier Roldán

Javier is part of the Tech and Data Team with Adevinta Spain. With more than 15 years of experience as an analyst linked to product development, he is a "data lover", a specialist in Product Analytics and experimentation. He is currently the CRO of Adevinta Spain and also an Analytics Project Manager. His main objective is to boost experimentation and a data-driven culture in product development.

Enabler for Experimentation

Fabio Venni

Fabio is a Product Designer that has worked with data for informing his design decisions for over 15 years. Having worked for large corporations like Informa and Booking.com he had the opportunity of designing over 500 tests. Very passionate about sharing best practices and helping teams avoiding mistakes in Adevinta works close to the Houston team as Experimentation Enabler. He also designs and maintains the Research Portal, and previously has been UX lead for Trust.

Data Analyst for Houston

Anastasiia Chausova

With a background in theoretical and applied statistics, Anastasiia has an extensive background in implementing end to end analytical services and running analysis on complex datasets. She is helping the experimentation team to implement a rigorous precise and accurate scientific tool as well as consulting on ad hoc analysis across different teams and marketplaces, together with Fabio Venni she has run more than 30 hours of training in 2020 reaching 150 employees.