21 Reading Recommendations for 2021

Check out the 21 books you need to add to your reading list in 2021.

We love books. In that spirit, here are 21 reading recommendations for 2021, broken into 3 groups of 7 (you’re welcome).

7 Books for Fun and Learning

Friend of a Friend by David Burkus

In Friend of a Friend David Burkus explains graph concepts without using the word “graph.” He drew us in with this: “Introduction or How I Learned to Stop Networking and Love Network Science.”

The Book of Why by Judea Pearl and Dana Mackenzie

The Book of Why is about cause and effect. Although the authors don’t discuss graph theory specifically, there are graph crossover concepts that will expand the ways you think about using graph data.

Brains on Fire by Robbin Phillips, Greg Cordell, Geno Church and Spike Jones

Learn how to ignite a word of mouth movement around your product with Brains on Fire. We’re thankful to our community for exactly this reason.

Linked by Albert-László Barabási

The subtitle of Linked says it all: “How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life.”

World of Wonders by Aimee Nezhukumatathil

World of Wonders is a beautiful book that weaves wisdom from nature with wisdom from experience.

Weapons of Math Destruction by Cathy O’Neil

Cathy O’Neil’s videos posit that algorithms are opinions embedded in code, and her book Weapons of Math Destruction (love that title) dives deeper into their social impact.

Connected by Nicholas Christakis and James Fowler

Many friends of friends have read this book Connected, and if you haven’t you should.

7 Books for Learning about Graphs
(Because You Love Them)

Graph Databases For Dummies by Dr. Jim Webber and Rik Van Bruggen

Graph Databases For Dummies is a great place to start your graph journey. The book introduces you to the basics of graph database technology from building a rich graph data model to deploying your first graph-powered application. Get the book for free.

Graph Data Science For Dummies by Amy Hodler and Mark Needham

Graph Data Science For Dummies walks you through the foundations of graph data science – from defining graph analytics and algorithms to showing you how to use them for machine learning and solve real-world problems. Get the book for free.

Fullstack GraphQL Applications with GRANDstack by William Lyon

GraphQL, React, Apollo, and the Neo4j database: That’s the GRANDstack, and it gives developers everything they need to build graph-powered applications. Get the free excerpt.

Graph-Powered Machine Learning by Alessandro Negro

Modern machine learning demands new approaches. Graph-Powered Machine Learning explores the new way of looking at machine learning through the lens of graph technology. In particular, this three-chapter excerpt, available for free, takes a closer look at how graph-powered ML can be used to build hybrid, real-time recommendation engines.

Graph Algorithms: Practical Examples in Apache Spark & Neo4j by Mark Needham & Amy E. Hodler

Well-written, detailed and accessible, this book on graph algorithms gives you the grounding you need for hands-on graph data science. Get the book for free.

AI on Trial by Mark Deem and Peter Warren

Organized true to its title, AI on Trial comes complete with opening arguments, evidence, closing arguments and yes, finally, judgment. (Amy Hodler contributed Chapter 2 to this forthcoming book, slated for release on 25 March 2021.)

The Rise of the Data Cloud by Frank Slootman and Steve Hamm

Data strategy is being written and rewritten each year and business leaders are struggling to keep up. That’s the premise of the Rise of the Data Cloud podcast, launched in June 2020, and of this new book.

7 Books about AI
(Because AI Is Fun)

You Look Like a Thing and I Love You by Janelle Shane

From You Look Like a Thing and I Love You, you’ll learn about how AI works and laugh at the same time.

The Algorithm Design Manual by Steven Skiena

Now in its third edition, The Algorithm Design Manual is Amy Hodler’s all-time favorite book on algorithms.

Reprogramming the American Dream by Kevin Scott and Greg Shaw

Many worry about AI’s economic impact, especially on rural areas. In Reprogramming the American Dream, Microsoft CTO Kevin Scott and Senior Director in the Office of the CEO Greg Shaw, share their experiences and vision.

Deep Medicine by Eric Topol

Dr. Eric Topol documents how AI and machine learning have the potential to transform “shallow medicine” into Deep Medicine and make healthcare human again.

Hello World: Being Human in the Age of Algorithms by Hannah Fry

Hello World is a thought-provoking and entertaining read, with great notes for further reading on humans and algorithms.

The AI Book by Susanne Chishti, Ivana Bartoletti, Anne Leslie and Shân M. Millie

The AI Book is a crowdsourced book from global thought leaders in financial services.

AI is a Waste of Money by Arijit Sengupta

Ignore the contrarian title; this free book explains AI principles through stories as a data scientist and her mentor tackle business problems using AI and machine learning.

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