Product Analytics and AI

Product Analytics and AI

This course is part of multiple programs.

Instructor: Alex Cowan

Access provided by FutureX

38,903 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

504 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

504 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

What you'll learn

How to naturally, habitually tie your team’s work to actionable analytics that help you drive to user value.
How to pair your hypotheses on customer personas and problem with analytics.
How to test propositions (a la Lean Startup) so you don’t build features no one wants.
How to instrument actionable observation into everything you build (a la Lean UX).

Skills you'll gain

Details to know

Shareable certificate

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Assessments

14 assignments¹

AI Graded see disclaimer

Taught in English

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There are 4 modules in this course

Few capabilities focus agile like a strong analytics program. Such a program determines where a team should focus from one agile iteration (sprint) to the next. Successful analytics are rarely hard to understand and are often startling in their clarity. In this course, developed at the Darden School of Business at the University of Virginia, you'll learn how to build a strong analytics infrastructure for your team, integrating it with the core of your drive to value.

Without an actionable view of who your customer is and what problems/jobs/habits they have, you’re operating on a shaky foundation. This week, we’ll look at how to pair your qualitative analytics on customer hypotheses with testable analytics.

What's included

12 videos1 reading2 assignments3 discussion prompts

12 videos Total 65 minutes

Intentional Iteration 4 minutes
Science for the Win 4 minutes
Recap: Hypothesis-Driven Development 6 minutes
Getting Outside the Building with Trent the Technician 3 minutes
Describing the Customer Journey for Testability: Trent the Technician 5 minutes
Focal Point: The User Journey 6 minutes
Your Analytics Portfolio 3 minutes
Designing Actionable Inferences: DV's, IV's, and Causality 8 minutes
Testing with Retrospective Experiment Patterns 5 minutes
Testing with Prospective Experiment Patterns 6 minutes
Understanding Enough about Statistics for Now 6 minutes
Separating Laggards vs. Innovators: The Two Ways to be Wrong and the Two Ways to be Right 8 minutes

1 reading Total 10 minutes

Course Overview & Requirements 10 minutes

2 assignments Total 45 minutes

Introduction and Customer Analytics 30 minutes
Customer Analytics 15 minutes

3 discussion prompts Total 30 minutes

Tools & Tips for Story Mapping 10 minutes
Focal Points: Trent the Technician 10 minutes
Looking Forward to Upcoming Topics 10 minutes

Why build something no one wants? It seems like an obvious question, yet a lot (probably >50%) of software ends up lightly used or not used at all. This week, we’ll look at how to run fast but definitive experiments to test demand.

What's included

14 videos4 assignments1 discussion prompt

14 videos Total 78 minutes

Lean Startup and the Demand Hypothesis 7 minutes
Demand Testing at Enable Quiz 7 minutes
Designing Experiments 5 minutes
Experiment Design with MVPs 5 minutes
Designing User Habits: The Hook Framework 5 minutes
Five Experiment Charters 3 minutes
The Fake Feature Test 5 minutes
Testing Features: Running the Experiment 4 minutes
Testing Funnels 7 minutes
Testing Cohorts 7 minutes
Experiment Design: Testing a Coding Course for Designers & Managers 5 minutes
Experiment Execution: Testing a Coding Course for Designers & Managers 6 minutes
Diverging Your Ideas with Generative AI 4 minutes
Interview: Laura Klein on Practice of Lean UX 10 minutes

4 assignments Total 90 minutes

Testing Demand and Experiment Patterns 30 minutes
Testing Motivations with MVPs 30 minutes
Experiments 30 minutes
Testing Features 0 minutes

1 discussion prompt Total 10 minutes

Fake Feature Tests & Your Experience 10 minutes

Strong usability most often comes from ongoing diligence as opposed to big redesigns. Teams that do the hard work of consistently testing usability are rewarded with a consistent stream of customer wins and a culture of experimentation that makes work more enjoyable and rewarding.

What's included

15 videos4 assignments1 peer review2 discussion prompts

15 videos Total 97 minutes

Analytics All the Time 5 minutes
Qualitative Usability Testing 9 minutes
The Test You Already Have 2 minutes
Pairing Your User Stories with Analytics: Trent the Technician 8 minutes
Getting Outside the Building With Ivan the Inside Salesperson 7 minutes
Pairing Your User Stories with Analytics: Ivan the Inside Salesperson 10 minutes
Analyzing Dependent Variables with Google Analytics 5 minutes
Google Analytics: The Littlest Overview 11 minutes
From Design to Code: Trent the Technician 5 minutes
From Code to Analytics: Trent the Technician 5 minutes
A/B Testing 8 minutes
Mapping Analytics: Ivan the Inside Salesperson 6 minutes
Designing, Coding, and Testing: Ivan the Inside Salesperson 6 minutes
From Inference to Product Priorities: Four Sprints with HinH 8 minutes
Pushing Yourself on Comparables with Generative AI 2 minutes

4 assignments Total 90 minutes

Qualitative and Quantitative Analytics 30 minutes
Testing Analytics 30 minutes
User Stories & Analytics 30 minutes
A/B Testing 0 minutes

1 peer review Total 150 minutes

Creating a Testable Solution 150 minutes

2 discussion prompts Total 20 minutes

Google Analytics & Alex's Website 10 minutes
Tools & Tips for A/B Testing 10 minutes

The availability of big data and the ascendance of machine learning can supercharge the way you approach analytics. This week, we're going to learn how data science is changing analytics and how you can create a focused, productive interfaces to a data science capability.

What's included

18 videos4 assignments2 discussion prompts

18 videos Total 107 minutes

What is Data Science? 4 minutes
Interview: Drew Conway on Data Science 9 minutes
Interview: Drew Conway’s Data Science Journey 9 minutes
Data Science and Generative AI 2 minutes
Predictive AI vs. Generative AI 7 minutes
Interview: Casey Lichtendahl: Data Science and You 6 minutes
Interview: Casey Lichtendahl: Closer Look at the Work of Data Science 11 minutes
Maturing Your Analytics & AI Capability 4 minutes
Product Jobs-to-be-Done and Your AI Portfolio 6 minutes
Finding Your Ground Truth Supremacy with AI 3 minutes
From Easy Wins to Durable wins with AI 5 minutes
Facilitating Collaboration with Your Data Science Team 4 minutes
Interview: Casey Lichtendahl: Data at Rest vs. Data in Motion 7 minutes
Generative AI IRL: the Jedburgh App's 2 minutes
Data Science IRL: Intro to the Casino Jack Case 8 minutes
Data Science IRL: Data Wrangling and Exploratory Analysis 9 minutes
Data Science IRL: Testing Hypotheses and Designing Interventions 5 minutes
Course Close 5 minutes

4 assignments Total 105 minutes

Analytics and Data Science 30 minutes
Data Science 30 minutes
Data Executions 30 minutes
Data Science IRL 15 minutes

2 discussion prompts Total 20 minutes

Data Science & Your Experience 10 minutes
Tips for Working with a Data Science Team 10 minutes

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Instructor

Instructor ratings

(128 ratings)

Alex Cowan

University of Virginia

28 Courses 533,592 learners

Offered by

University of Virginia

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Learner reviews

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Reviewed on Sep 29, 2020

Thanks Prof. Cowan and team for this course. Not only refreshed many fundamental but learned many new things.

Reviewed on Aug 18, 2020

At the beginning I though it was going to be really difficult, but Alex makes the course fun, dynamic and easy to understand! Thank you!

Reviewed on Feb 14, 2021

I didn't get as much out of this as the other courses, but I already work with a lot of analytics so the material wasn't really new to me. The interviews with industry professionals were good.

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