Computerphile is a Youtube sister channel of Numberphile. Where Numberphile’s videos are about the magic behind match and numbers, Computerphile’s videos are all about computers and computer stuff. I recommend both channels in general, and have watched many of their videos already.
Yet, over the past weeks I specifically enjoyed what seems to be several series of videos on Cyber Security related topics.
What makes a good password?
One series is all about passwords.
What are strong passwords, which are bad? How can hackers crack yours? And how do websites secure user passwords?
The videos below are in somewhat of the right order and they make for an interesting insight in the world of password management. They give you advice on how to pick you password, and even a nice tool to check whether your password has ever been leaked.
Probably, you will want to change your password afterwards!
Hacking and attacking
If you are up to no good, please do not watch this second series, which revolves all around hacks and computer attacks.
How do people get access to a websites database? How can we prevent it? How can we recognize security dangers?
You might know of SQL injections, but do you know what a slow loris attack is? Or how ransomware works? Or what exploitX is?
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!
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:
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.