Before everyone heads out for summer breaks and vacations, I wanted to share a list of books from my personal Olga collection that can make you a better analyst or data scientist.
We’re not as lucky as some other fields. There are many books on data engineering and data management. There is even more literature on marketing, finance, and product development. But when it comes to analytics, the list is surprisingly small.
A large part of analytics books falls into academia and heavy statistics. That is still important, of course, but it is often not quite relevant to the work analysts actually do every day. There are very few books that sit at the intersection of data, statistics, and business or product.
Below is my collection of books that I believe are essential for becoming a stronger analyst or data scientist. I’ve read each of them, some more than 3–4 times, and I hope this list helps you find a few useful reads too.
Product Analytics for Data-Driven Decisions: Derive Insights from Web Analytics Data by Joanne Rodrigues.
Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue by Eric Benjamin Seufert. An old but priceless book that helped set my own journey into product analytics. Still one of the most practical books on segmentation, monetization, and freemium business models.
Actionable Gamification: Beyond Points, Badges, and Leaderboards by Yu-kai Chou
A great collection of frameworks for streaks, points, progress loops, rewards, and all the “gaming” mechanics apps use today.
Hooked: How to Build Habit-Forming Products by Nir Eyal. A good read for understanding user behavior, habit loops, engagement, and how successful companies build products people keep coming back to.
The Cold Start Problem by Andrew Chen. A great book on how network effects start and scale. This is not a typical analytics book, but it is very relevant for analysts who support growth at marketplace or network-based products.
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