Strategy, discovery, metrics, growth, pricing, design, delivery and leadership. Each entry says when it is the right one to pick up.
Burkov compresses the field into a book you can read in a weekend, with the mathematics present but held to what is genuinely needed. It covers supervised learning from linear and logistic regression through decision trees …
Olsen lays out a six step process for reaching product market fit. You identify the target customer, work out their underserved needs, define the value proposition, specify the minimum viable feature set, build the prototype, and test it with customers.
Ries applies manufacturing derived thinking to new products under uncertainty. The build measure learn loop, validated learning, innovation accounting and the pivot all originate or are popularised here.
Brooks drew on running the OS/360 project at IBM and wrote the essays that gave the field Brooks's law, the observation that adding people to a late project makes it later.
Busuttil writes from a practitioner's perspective on what the job involves day to day, including the parts that go wrong. Covers defining the role, understanding the market, working with development, launching, and the recurring reasons products fail.
The definition of Scrum by the two people who created it, running to about thirteen pages in the November 2020 revision. It sets out the theory of empiricism and lean thinking, the five values, the three accountabilities, the five events and the three artefacts with their commitments.
Blank and Dorf's expansion of customer development into a step by step manual, running to several hundred pages of checklists. Covers customer discovery, validation, creation and company building, split into separate physical and web or mobile channel tracks.
Kahneman's summary of decades of work on judgement and decision making. System 1 is fast, automatic and associative while System 2 is slow, effortful and deliberate.
O'Neil, a mathematician who worked in finance and advertising technology, examines algorithms that operate at scale, remain opaque to the people they judge, and cause damage that feeds back into their own training data.