3 books I read in 2020

2020, a year like no other before, has led to drastic changes in many people’s lives. As I’m getting ready to pivot my career and enter the buzzing field of Data Science, I wanted to read how different practitioners were approaching their jobs and responsibilities.

Unfortunately, as many of us experienced during quarantine, I didn’t stick to my goal of one book a week, and fell into a black hole of one TV show season a day 😅. Still, I managed to read a few of the books in my list, and here are the ones that left the strongest impression.

The Elements of Data Analytics Style, by Jeff Leek

Author of the John Hopkins Data Science specialization course on Coursera, Jeff Leek wrote this one in 2015 as a guide for people who want to analyze data.

It’s a practical guide full of best practices to use when exploring, cleaning and validating data, along with the most common errors when running analysis of your data and how to avoid them.

All the advice here is directly actionable, I’d consider it a must-have in any data practitioner’s toolbox.

The Elements of Data Analytics Style is available as a pay-what-you-want ebook on LeanPub.

What are Algorithms Dreaming Of? by Dominique Cardon

Dominique Cardon is a French sociologist who made a name for himself as the leading specialist on digital and internet topics in France.

In his 2016 book he details how the algorithms initially implemented by the GAFAM to measure our activity on the internet are now shaping our actions and transforming our reality.

I would assume he was one of the first ones to vulgarize the concept of the self-validating bubble we’re building around ourselves via our digital and social interactions, that’s leading to an increased polarization of our societies.

He makes a compelling point for regulation, government-led regulation, as we can’t afford to leave the algorithm makers regulate themselves, and in the end dictate their own rules.

What are Algorithms Dreaming Of? is available only in French (as far as I know).

Ethics and Data Science, by Mike Loukides, Hilary Mason and DJ Patil

Among the co-authors, DJ Patil can boast of the impressive credentials of Former Chief Data Science Officer for the Obama administration. This is one of the main reasons I started reading this book.

This report is the most-likely to shape my future approach in building data products or tackling data projects. It is a perfect follow-up to Dominique Cardon’s book, as it investigates practical ways to infuse ethical standards in Data Scientists and Analysts work. It leaves us with framing guidelines and principles we should all adhere to if we’re aiming to avoid replicating nightmare scenarii like what we’ve seen with the Cambridge Analytica scandal.

The report provides us with a step-by-step checklist to ensure Data projects follow the 5Cs of ethical data products: Consent, Clarity, Consistency, Control and Consequences.

Ethics and Data Science is published by O’Reilly and available as a free ebook on Amazon.

The Social Dilemma, by Jeff Orlowski

This one is a bonus. I know it’s not a book. But hey, if there’s one year when everything is forgiven (aside from not wearing a mask) it’s 2020, right?

The Social Dilemma is a documentary from Netflix laying out how the apps we consume today are built upon an all-encompassing data foundation designed to keep us hooked, addicted to the oh so subtle dopamine hit that comes along that (ding) notification. If you don’t feel like reading the 120 pages of Cardon’s book, watch this and you’ll get the gist of it.

In typical Netflix documentary fashion it alternates interviews and facts with mockumentary recreations of things that could be. The end-result is often informative, sometimes thought-provoking, always scary (some may even go as far as calling it fear-mongering).

The Social Dilemma can be watched on Netflix.

Photo by Xavier Rosée on Unsplash