Books for the modern, data-driven HR professional (incl. People Analytics)

With great pleasure I’ve studied and worked in the field of people analytics, where we seek to leverage employee, management-, and business information to better organize and manage our personnel. Here, data has proven valuable itself indispensible for the organization of the future.

Data and analytics have not traditionally been high on the list of HR professionals. Fortunately, there is an increased awareness that the 21st century (HR) manager has to be data-savvy. But where to start learning? The plentiful available resources can be daunting…

Have a look at these 100+ amazing books
for (starting) people analytics specialists.
My personal recommendations are included as pictures,
but feel free to ask for more detailed suggestions!

Categories (clickable)

Behavioural Psychology: focus on behavioural psychology and economics, including decision-making and the biases therein.
Technology: focus on the implications of new technology….
- Ethics: … on society and humanity, and what can go wrong.
- Digital & Data-driven HR: … for the future of work, workforce, and organization. Includes people analytics case studies.
Management: focus on industrial and organizational psychology, HR, leadership, and business strategy.
Statistics: focus on the technical books explaining statistical concepts and applied data analysis.
- People analytics: …. more technical books on how to conduct people analytics studies step-by-step in (statistical) software.
- Programming: … technical books specifically aimed at (statistical) programming and data analysis.
Communication: focus on information exchange, presentation, and data visualization.

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Behavioural Psychology books

Nudge: Improving Decision About Health, Wealth, and Happiness — by Richard Thaler
Outliers: The Story of Success — by Malcolm Gladwell
Predictably Irrational: The Hidden Forces That Shape Our Decisions — by Dan Ariely
The Halo Effect: . . . and the Eight Other Business Delusions That Deceive Managers — by Phil Rosenzweig
The Honest Truth About Dishonesty: How We Lie to Everyone — Especially Ourselves — by Dan Ariely
The Person You Mean to Be: How Good People Fight Bias — by Dolly Chugh
The Undoing Project: A Friendship That Changed Our Minds — by Michael Lewis
Thinking, Fast and Slow — by Daniel Kahneman
Superforecasters: The Art and Science of Prediction — by Philip Tetlock and Dan Gardner

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Technology books

Algorithms to Live By: The Computer Science of Human Decisions — by Brian Christian and Tom Griffeth
Business Analytics for Managers: Taking Business Intelligence Beyond Reporting — by Gert Laursen
Competing on Analytics: The Science of Winning — by Thomas Davenport and Jeanne Harris
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking — by Foster Provost and Tom Fawcett
Moneyball: The Art of Winning At An Unfair Game — by Michael Lewis
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines — by Thomas Davenport
Predicting Business Success: Using Smarter Analytics to Drive Results — by Scott Mondore, Hannah Spell, Matt Betts, and Shane Douthitt
Predictive Analytics for Human Resources — by Jac Fitz-Enz and John Mattox
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die — by Eric Siegel
Rise of the Robots: Technology and the Threat of a Jobless Future — by Martin Ford
The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future — by Kevin Kelly
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World — by Pedro Domingos
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies — by Erik Brynjolfsson and Andrew McAfee
The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism — by Arun Sundararajan
What Algorithms Want: Imagination in the Age of Computing — by Ed Finn

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Ethics in Data & Machine Learning

Algorithms of Oppression: How Search Engines Reinforce Racism — by Safiya Umoja Noble
Automate This: How Algorithms Took Over Our Markets, Our Jobs, and the World — by Chistopher Steiner
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor — by Virginia Eubanks
Programmed Inequality: How Britain Discarded Women Technologists and Lost Its Edge in Computing (History of Computing) — by Marie Hicks
Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech — by Sara Wachter-Boettcher
The Black Box Society — by Frank Pasquale
The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement — by Andrew Guthrie Ferguson
Weapons of Math Destruction — by Cathy O’Neill

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Digital & Data-driven HR

Calculating Success: How the New Workplace Analytics Will Revitalize Your Organization — by Carl Hoffmann, Eric Lesser, and Tim Ringo
Data-Driven HR: How to Use Analytics and Metrics to Drive Performance — by Bernard Marr
Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments — by Gene Pease, Barbara Beresford, and Lew Walker
Digital HR: A Guide to Technology-Enabled Human Resources — by Deborah Waddill
Employee Surveys That Work: Improving Design, Use, and Organizational Impact — by Alec Levenson
HR Analytics Handbook — by Laurie Bassi, Rob Carpenter, and Dan McMurrer
Learning Analytics: Measurement Innovations to Support Employee Development — by John Mattox II, Mark van Buren and Jean Martin
Making Human Capital Analytics Work: Measuring the ROI of Human Capital Processes and Outcomes — by Jack Phillips and Patricia Pulliam Phillips
Optimize Your Greatest Asset – Your People: How to Apply Analytics to Big Data to Improve Your Human Capital Investments — by Gene Pease
People Analytics in the Era of Big Data: Changing the Way You Attract, Acquire, Develop, and Retain Talent — by Jean Paul Isson and Jesse Harriot
People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us about the Future of Work — by Ben Waber
Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness — by Alec Levenson
Strategic Workforce Planning: Developing Optimized Talent Strategies for Future Growth — by Ross Sparkman
The Basics of People Analytics — by Erik van Vulpen
The Power of People: How Successful Organizations Use Workforce Analytics to Improve Business Performance — by Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig
Winning on HR Analytics: Leveraging Data for Competitive Advantage — by Ramesh Soundararajan and Kuldeep Singh

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Management books

Adaptive Space: How GM and Other Companies are Positively Disrupting Themselves and Transforming into Agile Organizations — by Michael Arena
Evidence-Based Management: Making Better Decisions in Organizations — by Eric Barends and Denise Rousseau
Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management — by Jeffrey Pfeffer and Robert Sutton
HR from the Outside In: Six Competencies for the Future of Human Resources — by Dave Ulrich
Human Resource Excellence: An Assessment of Strategies and Trends — by Lawler & Boudreau
Key Management Ratios — by Ciaran Walsh
Optimize Human Capital Investments: Make the “Hard” Business Case — by Frank DiBernardino
Powerful: Building a Culture of Freedom and Responsibility — by Patty McCord
Predicting Success: Evidence-Based Strategies to Hire the Right People and Build the Best Team — by David Lahey
Sensemaking: The Power of the Humanities in the Age of the Algorithm — by Christian Madsbjerg
The Alliance: Manageing Talent in the Networked Age — by Reid Hoffman
The Data-Driven Leader: A Powerful Approach to Delivering Measurable Business Impact through People Analytics — by Jenny Dearborn and David Swanson
The Decoded Company: Know Your Talent Better Than You Know Your Customers — by Leerom Segal
The Employee Experience Advantage: How to Win the War for Talent by Giving Employees the Workspaces they Want, the Tools they Need, and a Culture They Can Celebrat — by Jacob Morgan
The Future of Work: Attract New Talent, Build Better Leaders, and Create a Competitive Organization — by Jacob Morgan
The HR Scorecard — by Dave Ulrich, Mark Huselid, and Brian Becker
The Industries of the Future — by Alec Ross
The Talent Delusion: Why Data, Not Intuition, Is the Key to Unlocking Human Potential — by Tomas Chamorro-Premuzic
Time, Talent, Energy: Overcome Organizational Drag and Unleash Your Team’s Productive Power — by Michael Mankins and Eric Garton
Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage — by John Boudreau and Ravin Jesuthasan
Victory Through Organization: Why the War for Talent in Failing Your Company and What You can Do About It — by Dave and Mike Ulrich
Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead — by Laszlo Bock
The Best is Yet to Come: Expert Advice on Building the New HR — by Tracey Smith
Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness — by Alec Levenson

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Statistics books

A Field Guide to Lies and Statistics: A Neuroscientist on How to Make Sense of A Complex World — by Daniel Levitin
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences — by Cohen and Cohen
Designing Experiments and Analyzing Data: A Model Comparison Perspective — by Maxwell, Delaney, & Kelly
How to Lie with Statistics — by Darrell Huff
Naked Statistics: Stripping the Dread from the Data — by Charles Wheelan
The Signal and the Noise: Why So Many Predictions Fail — But Some Don’t — by Nate Silver
Weaponized Lies: How to Think Critically in the Post-Truth Era — by Daniel Levitin
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets — by Nassim Nicholas Taleb
The Black Swan: The Impact of the Highly Improbable — by Nassim Nicholas Taleb

Applied People Analytics

Doing HR Analytics – A Practitioner’s Handbook With R Examples — by Lyndon Sundmark
Predictive HR Analytics: Mastering the HR Metric — by Martin Edwards and Kirsten Edwards
People Analytics for Dummies — by Mike West
Predictive HR Analytics — by Cedric Ng Mong Shen
People Analytics & Text Mining with R — by Cedric Ng Mong Shen
Predictive HR Analytics, Text Mining & Organizational Network Analysis: with Excel — by Cedric Ng Mong Shen
Investing in People: Financial Impact of Human Resource Initiatives — by Wayne Cascio and John Bourdreau

Programming

You can find an overview of 20+ free programming books here.

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Data Visualization books

#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time — by Andy Kreibel
Better Presentations: A Guide for Scholars, Researchers, and Wonks — by Jon Schwabish
D3.js in Action: Data Visualization with Javascript — by Elijah Meeks
Data Visualization: A Practical Introduction — by Kieran Healy
Data Visualization: Charts, Maps, and Interactive Graphics — by Robert Grant
Fundamentals of Data Visualization — by Claus Wilke
How to Win Friends and Influence People — by Dale Carnegie
Information is Beautiful — by David McCandless
Interactive Data Visualization for the Web: An Introduction to Designing with D3 — by Scott Murray
Knowledge is Beautiful — by David McCandless
Storytelling with Data — by Cole Nussbaumer Knaflic
The Functional Art: An introduction to information graphics and visualization (Voices That Matter) — by Alberto Cairo
The Truthful Art: Data, Charts, and Maps for Communication — by Alberto Cairo
Visual Miscellaneum — by David McCandless
Visualization Analysis and Design — by Tamara Munzner

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A note of thanks

I want to thank the active people analytics community, publishing in management journals, but also on social media. I knew Littral Shemer Haim already hosted a people analytics reading list, and so did Analytics in HR (Erik van Vulpen) and Workplaceif (Manoj Kumar). After Jared Valdron called for book recommendation on people analytics on LinkedIn, and nearly 60 people replied, I thought let’s merge these overviews.

Hence, a big thank you and acknowledgement to all those who’ve contributed directly or indirectly. I hope this comprehensive merged overview is helpful.

Join 385 other subscribers

Circular Map Cutouts in R

Katie Jolly wanted to surprise a friend with a nice geeky gift: a custom-made map cutout. Using R and some visual finetuning in Inkscape, she was able to made the below.

A detailed write-up of how Katie got to this product is posted here.

Basically, the R’s tigris package included all data on roads, and the ArcGIS Open Data Hub provided the neighborhood boundaries. Fortunately, the sf package is great for transforming and manipulating geospatial data, and includes some functions to retrieve a subset of roads based on their distance to a centroid. With this subset, Katie could then build these wonderful plots in no time with ggplot2.

GoalKicker: Free Programming Books

This specific link has been on my to-do list for so-long now that I’ve decided to just share it without any further ado.

The people behind GoalKicker, for whatever reason, decided to compile nearly 100 books on different programming languages based on among others StackOverflow entries. Their open access library contains books on languages from Latex to Linux, from Java to JavaScript, from SQL to MySQL, and from C, to C++, C#, and objective-C.

Basically, they host it all. Have a look yourself: https://books.goalkicker.com/

Two years of paulvanderlaken.com

Yesterday was the second anniversary of my website. I also reflected on this moment last year, and I thought to continue the tradition in 2019.

Let me start with a great, big
THANK YOU
to all my readers for continuing to visit my website!

You are the reason I continue to write down what I read. And maybe even the reason I continued reading and learning last year, despite all other distractions [my “real” job and my PhD : )].

Also a big thank you to all my followers on Twitter and LinkedIn, and those who have taken the time to comment or like my blogs. All of you make that I gain energy from writing this blog!

With that said, let’s start the review of the past year on my blog.

Biggest failures of 2018

Where there’s success, there’s failure. Some of my posts did not get a lot of attention by my readership. That’s unfortunate, as I really only take the time to blog about the stuff that I deem interesting enough. Were these failed blog posts just unlucky, or am I biased and were they simply really bad and uninteresting?

You be the judge! Here are some of the least read posts of 2018:

A/B Testing a New Look received a meagre 4 views per month.
Interactive Explanation of Network and Graph Principles received 5 views/month.
Dragonflies and neural networks and Bellwoods: A procedurally generated game both received just 6 views per month.
7 tips for writing cleaner JavaScript code, translated to 3.5 tips for R programming only received 8 views/month, as did What to consider when choosing colors for data visualization
dygraphs and chatterplots, both new types of visualizations, received 9 views/month.
Finally, How to Design Your First Programs and Computers decode what humans see both received 10 views per month.

General statistics

Now, let’s move to some general statistics: in 2018, paulvanderlaken.com received 85.614 views, by 57.594 unique visitors. I posted 61 new blogs, consisting of a total of 31.598 words. Fifty-one visitors liked one of my posts, and 24 visitors took the time to post a comment of their own (my replies included, probably).

Compared to last year, my website did pretty well!

	2017	2018	Δ
Views	38490	85614	122%
Unique visitors	26949	57594	114%
Posts	100	61	-39%
Words / post	625	518	-17%
Likes	35	51	46%
Comments	99	24	-76%

However, the above statistics do not properly reflect the development of my website. For instance, I only really started generating traffic after my first viral post (i.e., Harry Plotter). The below graph takes that into account and better reflects the development of the traffic to my website.

The upward trend in traffic looks promising!

All time favorites

Looking back to the start of paulvanderlaken.com, let’s also examine which blogs have been performing well ever since their conception.

Clearly, most people have been coming for the R resources overview, as demonstrated by the visual below. Moreover, the majority of blog posts has not been visited much — only a handful ever cross the 1000 views mark.

The blogs that attracted a large public in 2017 (such as the original Harry Plotter and its sequal, and the Kaggle 2017 DS survey) have phased out a bit.

Fortunately, the introductory guide for newcomers to R is still kickstarting many programming careers! And on an additional positive note, more and more visitors seem to inspect the homepage and archives.

Redirected visitors

Finally , let’s have a closer look as to what brought people to my website.The below visualizes the main domains that redirected visitors.

Search engines provided the majority of traffic in both 2017 and 2018 –
mainly Google; to a lesser extent, DuckDuckGo and Bing (who in his right mind uses Norton Safe Search?!). My Twitter visitors increased in 2018 as compared to 2017, as did my traffic from this specific Quora page.

And that concludes my two year anniversary of paulvanderlaken.com review. I hope you enjoyed it, and that you will return to my website for the many more years to come : )

I end with a big shout out to my most loyal readers!
104 people have subscribed to my website (as of 2019-01-22)
and receive an update wherener I post a new blog.

Thank you for your continued support!

Want to join this group of elite followers?
Press the Follow button
in the right toolbar, or at the bottom of this blog post.

Screeps: An AI colony simulation game for programmers

A while back I discovered this free game called Screeps: an RTS colony-simulation game specifically directed AI programmers. I was immediately intrigued by the concept, but it took me a while to find the time and courage to play. When I finally got to playing though, I lost myself in the game for several days on end.

Screeps means “scripting creeps.”
It’s an open-source sandbox MMO RTS game for programmers, wherein the core mechanic is programming your units’ AI. You control your colony by writing JavaScript which operate 24/7 in the single persistent real-time world filled by other players on par with you.
https://screeps.com/

Basically, screeps is very little game. You start with in a randomly generated canyon of some 400 by 400 pixels, with nothing more than some basic resources and your base. Nothing fun will happen. Even better, nothing at all will happen. Unless you program it yourself.

As a player, it is your job to “script” your own creeps’ AI. And your buildings AI for that matter. You will need to write a program that makes your base spawn workers. And next those workers will need to be programmed to actually work. You need to direct them to go to the resources, explain them how to mine the resources, when to stop mining, and how to return the mined resources to your base. You will probably also want some soldiers and some other defenses, so those need to be spawned with their own special instructions as well.

Everything needs to be scripted well, as the game (and thus your screeps) runs on special servers, 24/7, so also when you are not playing yourself. Truly your personal, virtual, mini-AI colony.

The programming mostly occurs in JavaScript. This can be difficult for those like myself who do not know JavaScript, but even I managed to have some basic workers running up and down my screen in a matter of hours. Step by step, you will learn (be forced) to create different worker types (harvesters, builders, repairmen, and even some stupid soldiers) and even some basic colony management scripts (spawning workers, spending resources, upgrading stuff). In the mean time, you will silently learn some JavaScript while playing. As I put in more and more hours, I could even see how to improve on my earlier scripts. This makes screeps a fun and rewarding gaming and learning experience.

Do expect to run into frustrations though! If you’re no JavaScript expert you will personally create a lot of bugs. Of which the game by default send you messages, as your colony will get stuck overnight. Moreover, you will likely need to Google every single thing you want to do at the start. I found great help in this YouTube tutorial to get me started. Finally, you are only under nooby-protection for the first so-many hours, after which you will quickly get slaughtered by all the advanced multi-CPU players on the servers.

Heck, it was fun while it lasted : )

PS. I read here that, using WebAssembly, one could also compile code written in different languages and run it in Screeps: C/C++ or Rust code, as well as other supported languages.

#100DaysOfCode: Machine Learning & Data Visualization

2018 seemed to be the year of challenges going viral on the web. Most of them were plain stupid and/or dangerous. However, one viral challenge I did like: #100DaysOfCode

1. Code minimum an hour every day for the next 100 days.
2. Tweet your progress every day with the #100DaysOfCode hashtag.
3. Each day, reach out to at least two people on Twitter who are also doing the challenge
100 Days of Code rulebook

Many (aspiring) programming professionals competed in this challenge, sharing their learning journeys in domains from web development, machine learning, or data visualization.

With this blog, I wanted to share two of those learning journeys that stood out for me.

Machine learning

First, there’s Avik Jain’s 100 days of Machine Learning code repository on Github. Avik’s repository contains all learning activities he followed during the 53 days of programming he completed. Some of Avik’s entries really stood out, and I particularly liked his educational infographics:

Just look at the wonderful design and visual aids on this decision tree for dummies infographic, pseudocode and all:

Day 23: Decision trees for dummies. This just looks fabulous right?!

Apart from the infographics, Avik also links to many very well produced tutorials that helped him improve his machine learning skills. Such as the free Python for Data Science Handbook Avik worked through, or this Youtube tutorial on deep learning in Python with Tensorflow and Keras:

Although Avik didn’t seem to have completed the full 100 days, many others did.

Data visualization

I have blogged about Hannah Yan Han‘s 100 days of code project before, but she definately deserves another mention here. Her 100 days revolved around data science, data visualization, and storytelling using both R and Python. You can find her #100DaysOfCode Medium page here, and her associated Github repository here.

For example, one day Hannah explored where instant noodles come from, how they are served, and whether people like them or not.

A different day she would examine which sports are the thoughest:

Or how scientific researchers migrate across the globe:

Hannah used many different plot types in those 100 days. Also some lesser known ones, like these upset plots on TED talk data:

Heck, she even made her own R package to generate Mondriaan-like paintings on one of the days:

What I found so great about Hannah’s project is that she picked a novel dataset every couple of days. Moreover, she used a extremely large variety of different visualization formats. All visuals were equally beautiful, but Hannah made sure to pick the right one for the purpose she was trying to serve. If you are interested in data visualization, you seriously should check out Hannah’s 100DaysOfCode Medium page.