Try Hack Me – Cyber Security Challenges

Try Hack Me – Cyber Security Challenges

Sometimes I just stumble across these random resources that I immediately want to share with fellow geeks. If you like computers and programming, you should definitely have a look at…

TryHackMe started in 2018 by two cyber security enthusiasts, Ashu Savani and Ben Spring, who met at a summer internship. When getting started with in the field, they found learning security to be a fragmented, inaccessable and difficult experience; often being given a vulnerable machine’s IP with no additional resources is not the most efficient way to learn, especially when you don’t have any prior knowledge. When Ben returned back to University he created a way to deploy machines and sent it to Ashu, who suggested uploading all the notes they’d made over the summer onto a centralised platform for others to learn, for free.

To allow users to share their knowledge, TryHackMe allows other users (at no charge) to create a virtual room, which contains a combination of theoretical and practical learning components.. In early 2019, Jon Peters started creating rooms and suggested the platform build up a community, a task he took on and succeeded in.

The platform has never raised any capital and is entirely bootstrapped.

I don’t have any affiliation or whatever with the platform, but I just think it’s a super cool resource if you want to learn more about hands-on computer stuff.

Here’s a nice demo on an advanced programmer taking on one of the first challenges. I definitely still have a long way to go, but it’s fun to watch someone sneak into a (dummy) server and look for clues! Like a proper detective, but then an extra nerdy one!

There are many “hacktivities” you can try on the platform.

And if you’re serious about learning this stuff, there are learning paths set out for you!

If you like their content, do consider taking a paid subscription and share this great initiative!

ppsr live on CRAN!

ppsr live on CRAN!

Finding predictive patterns in your dataset with one line of code!

Today — March 2nd 2021 — my first R package was published on the comprehensive R archive network (CRAN).

ppsr is the R implementation of the Predictive Power Score (PPS).

The PPS is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. You can read more about the concept in earlier blog posts (here and here), or here on Github, or via Medium.

With the ppsr package live on CRAN, it is now super easy to install the package and examine the predictive relationships in your dataset:

Here’s the official ppsr CRAN page for those interested:

Awesome R Shiny Resources & Extensions

Awesome R Shiny Resources & Extensions

Rob Gilmore curates a github repo listing resources for working with Shiny, the R web framework and dashboarding tool.

Nan Xiao curates a second repository, listing awesome R packages offer that extensions to Shiny, like extended UI or server components.

They should be your go-to resources when looking for anything Shiny!

Shiny Resources


Become a Data Science Professional

Become a Data Science Professional

Amit Ness gathered an impressive list of learning resources for becoming a data scientist.

It’s great to see that he shares them publicly on his github so that others may follow along.

But beware, this learning guideline covers a multi-year process.

Amit’s personal motto seems to be “Becoming better at data science every day“.

Completing the hyperlinked list below will take you several hundreds days at the least!

Learning Philosophy:


People Analytics vs. HR Analytics Google trends

People Analytics vs. HR Analytics Google trends

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.

If you want to get such data yourself, have a look at the Google Trends project.

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.

Want to learn more about people analytics? Have a look at this reading list I compiled.

How a File Format Exposed a Crossword Scandal

Vincent Warmerdam shared this Youtube video which I thoroughly enjoyed watched. It’s about Saul Pwanson, a software engineer whose hobby project got a little out of hand.

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.

Ultimately, 538 even covered the scandal:

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!