A few years back I completed my dissertation on data-driven Human Resource Management.
This specialized field is often dubbed HR analytics, for basically it’s the application of analytics to the topic of human resources.
Yet, as always in a specialized and hyped field, diifferent names started to emerge. The term People analytics arose, as did Workforce analytics, Talent analytics, and many others.
I addressed this topic in the introduction to my Ph.D. thesis and because I love data visualization, I decided to make a visual to go along with it.
So I gathered some Google Trends data, added a nice locally smoothed curve through it, and there you have it. As the original visual was so well received that it was even cited in this great handbook on HR analytics. With almost three years passed now, I decided it was time for an update. So here’s the 2021 version.
If you would compare this to the previous version, the trends look quite different. In the previous version, People Analytics had the dominant term since 2011 already.
Unfortunately, that’s not something I can help. Google indexes these search interest ratings behind the scenes, and every year or so, they change how they are calculated.
In my dissertation, I wrote the following on the topic:
This process of internally examining the impact of HRM activities goes by many different labels. Contemporary popular labels include people analytics (e.g., Green, 2017; Kane, 2015), HR analytics (e.g., Lawler, Levenson, & Boudreau, 2004; Levenson, 2005; Rasmussen & Ulrich, 2015; Paauwe & Farndale, 2017), workforce analytics (e.g., Carlson & Kavanagh, 2018; Hota & Ghosh, 2013; Simón & Ferreiro, 2017), talent analytics (e.g., Bersin, 2012; Davenport, Harris, & Shapiro, 2010), and human capital analytics (e.g., Andersen, 2017; Minbaeva, 2017a, 2017b; Levenson & Fink, 2017; Schiemann, Seibert, & Blankenship, 2017). Other variations including metrics or reporting are also common (Falletta, 2014) but there is consensus that these differ from the analytics-labels (Cascio & Boudreau, 2010; Lawler, Levenson, & Boudreau, 2004). While HR metrics would refer to descriptive statistics on a single construct, analytics involves exploring and quantifying relationships between multiple constructs.
Yet, even within analytics, a large variety of labels is used interchangeably. For instance, the label people analytics is favored in most countries globally, except for mainland Europe and India where HR analytics is used most (Google Trends, 2018). While human capital analytics seems to refer to the exact same concept, it is used almost exclusively in scientific discourse. Some argue that the lack of clear terminology is because of the emerging nature of the field (Marler & Boudreau, 2017). Others argue that differences beyond semantics exist, for instance, in terms of the accountabilities the labels suggest, and the connotations they invoke (Van den Heuvel & Bondarouk, 2017). In practice, HR, human capital, and people analytics are frequently used to refer to analytical projects covering the entire range of HRM themes whereas workforce and talent analytics are commonly used with more narrow scopes in mind: respectively (strategic) workforce planning initiatives and analytical projects in recruitment, selection, and development. Throughout this dissertation, I will stick to the label people analytics, as this is leading label globally, and in the US tech companies, and thus the most likely label to which I expect the general field to converge.
In 2016, Saul Pwanson designed a plain-text file format for crossword puzzle data, and then spent a couple of months building a micro-data-pipeline, scraping tens of thousands of crosswords from various sources.
After putting all these crosswords in a simple uniform format, Saul used some simple command line commands to check for common patterns and irregularities.
Surprisingly enough, after visualizing the results, Saul discovered egregious plagiarism by a major crossword editor that had gone on for years.
I thoroughly enjoyed watching this talk on Youtube.
Saul covers the file format, data pipeline, and the design choices that aided rapid exploration; the evidence for the scandal, from the initial anomalies to the final damning visualization; and what it’s like for a data project to get 15 minutes of fame.
I tried to localize the dataset online, but it seems Saul’s website has since gone offline. If you do happen to find it, please do share it in the comments!
The book covers the basic foundations up to advanced theory and algorithms. I copied the table of contents below. It’s kind of math heavy, but well explained with visual examples and pseudo-code.
Moreover, the book contains multiple exercises for you to internalize the knowledge and skills.
As an added bonus, the professors teach a number of machine learning courses, the lecture slides and materials of which you can also access for free via the book’s website.
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.