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Product Analytics and AI

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Product Analytics and AI

This course is part of multiple programs.

Instructor: Alex Cowan

39,486 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

507 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.

507 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).

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

14 assignments¹

AI Graded see disclaimer

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Earn a shareable career certificate

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.

Module details

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 videosTotal 65 minutes

Intentional Iteration4 minutes
Science for the Win4 minutes
Recap: Hypothesis-Driven Development6 minutes
Getting Outside the Building with Trent the Technician3 minutes
Describing the Customer Journey for Testability: Trent the Technician5 minutes
Focal Point: The User Journey6 minutes
Your Analytics Portfolio3 minutes
Designing Actionable Inferences: DV's, IV's, and Causality8 minutes
Testing with Retrospective Experiment Patterns5 minutes
Testing with Prospective Experiment Patterns6 minutes
Understanding Enough about Statistics for Now6 minutes
Separating Laggards vs. Innovators: The Two Ways to be Wrong and the Two Ways to be Right8 minutes

1 readingTotal 10 minutes

Course Overview & Requirements10 minutes

2 assignmentsTotal 45 minutes

Introduction and Customer Analytics30 minutes
Customer Analytics15 minutes

3 discussion promptsTotal 30 minutes

Tools & Tips for Story Mapping10 minutes
Focal Points: Trent the Technician10 minutes
Looking Forward to Upcoming Topics10 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 videosTotal 78 minutes

Lean Startup and the Demand Hypothesis7 minutes
Demand Testing at Enable Quiz7 minutes
Designing Experiments5 minutes
Experiment Design with MVPs5 minutes
Designing User Habits: The Hook Framework5 minutes
Five Experiment Charters3 minutes
The Fake Feature Test5 minutes
Testing Features: Running the Experiment4 minutes
Testing Funnels7 minutes
Testing Cohorts7 minutes
Experiment Design: Testing a Coding Course for Designers & Managers5 minutes
Experiment Execution: Testing a Coding Course for Designers & Managers6 minutes
Diverging Your Ideas with Generative AI4 minutes
Interview: Laura Klein on Practice of Lean UX10 minutes

4 assignmentsTotal 90 minutes

Testing Demand and Experiment Patterns30 minutes
Testing Motivations with MVPs30 minutes
Experiments30 minutes
Testing Features0 minutes

1 discussion promptTotal 10 minutes

Fake Feature Tests & Your Experience10 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 videosTotal 97 minutes

Analytics All the Time5 minutes
Qualitative Usability Testing9 minutes
The Test You Already Have2 minutes
Pairing Your User Stories with Analytics: Trent the Technician8 minutes
Getting Outside the Building With Ivan the Inside Salesperson7 minutes
Pairing Your User Stories with Analytics: Ivan the Inside Salesperson10 minutes
Analyzing Dependent Variables with Google Analytics5 minutes
Google Analytics: The Littlest Overview11 minutes
From Design to Code: Trent the Technician5 minutes
From Code to Analytics: Trent the Technician5 minutes
A/B Testing8 minutes
Mapping Analytics: Ivan the Inside Salesperson6 minutes
Designing, Coding, and Testing: Ivan the Inside Salesperson6 minutes
From Inference to Product Priorities: Four Sprints with HinH8 minutes
Pushing Yourself on Comparables with Generative AI2 minutes

4 assignmentsTotal 90 minutes

Qualitative and Quantitative Analytics30 minutes
Testing Analytics30 minutes
User Stories & Analytics30 minutes
A/B Testing0 minutes

1 peer reviewTotal 150 minutes

Creating a Testable Solution150 minutes

2 discussion promptsTotal 20 minutes

Google Analytics & Alex's Website10 minutes
Tools & Tips for A/B Testing10 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.