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.
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This course is part of multiple programs
This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
What You Will 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).
Syllabus
WEEK 1
Introduction and Customer Analytics
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.
WEEK 2
Demand Analytics
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.
WEEK 3
UX Analytics
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.
WEEK 4
Analytics and Data Science
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.
