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.

Course Structure

  • 5 Weeks

  • 10 /15 hrs over Q

  • 5 modules

Course curriculum

  • 1

    Module 1: Welcome to the Data Track

    • Lesson 1.1: Why data is important for product

    • Lesson 1.1: Quiz

    • Lesson 1.2: Introduction to the Data Track

    • Lesson 1.2: Quiz
  • 2

    Module 2: Measuring the impact with KPIs and metrics

    • Module 2 Introduction

    • Lesson 2.1: Introduction to KPIs

    • Lesson 2.1: Quiz

    • Lesson 2.2: Defining your KPIs being user-centric

    • Lesson 2.2: Quiz

    • Lesson 2.3: A framework to organize your KPIs

    • Lesson 2.3: Quiz

    • Rate your satisfaction on Module 2
  • 3

    Module 3: Data Collection done right

    • Lesson 3.1: Why do we need data for product development?

    • Lesson 3.1: Quiz

    • Lesson 3.2.1: How you can find, track and leverage this data today in Adevinta? Collect, Refine, Distribute, Enjoy

    • Lesson 3.2.1: Quiz

    • Lesson 3.2.2: How you can find, track and leverage this data today in Adevinta? The data lifecycle

    • Lesson 3.2.2: Quiz

    • Lesson 3.2.3: How you can find, track and leverage this data today in Adevinta? Discovering Data Catalogues

    • Lesson 3.2.3: Quiz

    • Lesson 3.3.1: What can you do to have top quality data? What happens when you don’t?

    • Lesson 3.3.1: Quiz

    • Lesson 3.3.2: Interview - Enric, Data Enabler in Adevinta Spain

    • Lesson 3.3.3: Interview - Redhouane, Data Scientist in Panda

    • Lesson 3.3.4: Interview - David, Data Engineer in Leboncoin

    • 3.3.5: Interview - Maria, Data Analyst in Global Markets

    • Lesson 3.3.6: What can you do to have top quality data and what happens when you don’t?

    • Rate your satisfaction on Module 3
  • 4

    Module 4: Product Discovery with Data

    • Lesson 4.1: Introduction to Product Discovery

    • Lesson 4.1: Quiz

    • Lesson 4.2: Key analysis techniques you should care about when doing Product Discovery

    • Lesson 4.2: Quiz

    • Lesson 4.3.1: Setting up the context of our Product Discovery

    • Lesson 4.3.1: Quiz

    • Lesson 4.3.2: Capturing opportunities in Product Discovery

    • Lesson 4.3.2: Quiz

    • Lesson 4.3.3: Exploring opportunities in Product Discovery

    • Lesson 4.3.3: Quiz

    • Lesson 4.3.4: Ideate & start validating assumptions in Product Discovery

    • Lesson 4.3.4: Quiz

    • Lesson 4.4: Data Visualization & Storytelling

    • Lesson 4.5: Storytelling: Understand the context

    • Lesson 4.5: Quiz

    • Lesson 4.6: Storytelling: Tell a Story

    • Lesson 4.6: Quiz

    • Lesson 4.7: Storytelling: Choose the appropriate Data Visualization

    • Lesson 4.7: Quiz

    • Lesson 4.8: Storytelling: practical example

    • Lesson 4.8: Quiz

    • Rate your satisfaction on Module 4
  • 5

    Module 5: Experimentation

    • 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

    • 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

    • Share overall feedback on the course

Learn more about the course


Instructors

Lead Visualization Analyst

Javier Perez

Javier is a data visualization and storytelling specialist, with 8+ years of experience in data analytics in the internet sector. He has been working in Adevinta Spain for 5 years, first as a Data Engineer, then Data Analyst, and finally as Head of Insights Cross. He is now the Lead Visualization Analyst for the Hub, acting as a reference in Tableau.

Data Engineer

Iker Martinez

Iker is a Data engineer focused on democratizing access to data and growing a data culture inside Adevinta. Iker works as the enabler lead for Data and Privacy with his team facilitating data sharing within any two teams in the company, and building impactful machine learning use cases. Among other initiatives, they have facilitated integrations between marketplaces like Leboncoin, RE Spain, Milanuncios, Aval, Jofogas, Subito… and central teams like Panda, Messaging, Personalisation, ..., with data flowing in both directions.

Agile Coach

Miquel Auba

Miquel is an Agile Coach working closely to Data and Privacy teams within Adevinta. He encourages teams to have a better visual management powered by metrics and data, and coaches them on how to set strategic initiatives, measure their impact and make decisions based on objective product metrics. Miquel has facilitated metric-definition workshops with several teams and he is sharing his learnings and tips in this course.

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.

Data Analyst in GMM

Maria Farrés

Maria is a data analyst with a great interest in product and marketing analytics. She lands in the Product Academy with expertise in collecting product and marketing insights for several teams across the Global Markets portfolio. Besides, Maria has actively contributed in data democratization initiatives to provide Global Markets business audiences with autonomy to access data.

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.

Alessandro Locatelli

With extensive experience driving Product Analytics and Experimentation programs for online companies such as Zalando, eBay Classifieds, and eDreams, Alessandro is passionate about enabling data-informed decision making in large organizations. As a Product Lead for Experimentation, he is in charge of scaling our AB testing capabilities and he is contributing to growing a company-wide data culture to enable our teams to use more and more data for faster and smarter decisions.