Hello everyone and happy new year! I hope you all had a good holiday break. I thought about it quite a bit and I've decided this year to go a different direction with the videos I make as I plan to focus more on courses. Stay tuned for more news on this matter in the weeks to come...

Throughout this blog, I've talked about all sorts of problems and how solving them can aid one's data science acumen as well as the development of the data science mindset. Problem-Solving skills rank high when it comes to the soft skills aspect of our craft, something I also mentioned in my latest video on O'Reilly. However, I haven't talked much about how you can hone this ability.

Enter Brilliant, a portal for all sorts of STEM-related courses and puzzles that can help you develop problem-solving, among other things. If you have even a vague interest in Math and the positive Sciences, Brilliant can help you grow this into a passion and even a skill-set in these disciplines. The most intriguing thing about all this is that it does so in a fun and engaging way.

Naturally, most of the stuff Brilliant offers comes with a price tag (if it didn't, I would be concerned!). However, the cost of using the resources this site offers is a quite reasonable one and overall good value for money. The best part is that by signing up there you can also help me cover some of the expenses of this blog, as long as you use this link here: www.brilliant.org/fds (FDS stands for Foxy Data Science, by the way). Also, if you are among the first 200 people to sign up you'll get a 20% discount, so time is definitely of the essence!

Note that I normally don't promote anything of this blog unless I'm certain about its quality standard. Also, out of respect for your time I refrain from posting any ads on the site. So, whenever I post something like this affiliate link here I do so after careful consideration, opting to find the best way to raise some revenue for the site all while providing you with something useful and relevant to it. I hope that you view this initiative the same way.

So, recently I decided to make a video on this topic, based on some things I've observed in data science candidates. The hope is that this may help them and anyone else who may be looking into becoming a more holistic data scientist, instead of just a data science technician. The video I made is now available online on O'Reilly and although it's a bit longer than others I've made (not counting the quiz ones), it's fairly easy to follow. Enjoy!

Alright, the quiz video fever is over for the time being, so I'm back to making conventional data science videos. This latest one on APIs, for example, just got published on O'Reilly.  It's more technical than others, but very useful, particularly if you know already a few things about data science. Anyway, I hope you enjoy it!

Note that although you can view the list of videos and books on O'Reilly's learning platform, you need to have a valid account in order to view them in their entirety. A pretty good investment, if you ask me, but before you commit to a monthly or a yearly subscription, you can always have a trial one which lasts for 10 days. Cheers!

So, the 7th quiz video I've created is finally online on O'Reilly. This is the longest one so far spanning over 51 minutes, meaning there are lots of explanations for the various questions. It covers a bunch of topics, such as A/B testing, ANOVA, and various statistical tests. I put a lot of thought in this, much like you'd put a lot of thought in designing a data science experiment. Hopefully, you'll find it as useful and enjoyable as I did.

Note that just like other videos published on O'Reilly, you'll need to have an active account (even if it's a trial one), in order to view it in its entirety. As a bonus, you'll be able to view other videos as well as books available on that platform. Enjoy!

Lately I've been down with a severe case of quiz fever! Combined with the fact that it was too hot to go outside (Mediterranean summer heat is no joke!), I was more focused on this task. As a result, I created a bunch of quizzes to publish on O'Reilly, two of which are now online. Namely, the Data Engineering and the Machine Learning Applications one are now available on the O'Reilly platform. Check them out when you have the chance.

Note that in order to have full access to the quizzes, you need an account with O'Reilly, a pretty good investment. If you are unsure whether you want to go for it, you can always create a trial account first (valid for 10 days) and check out the content of this great platform, without any strings attached. However, to maximize your benefit, I recommend you get a paid account. The plethora of quality content on this platform makes it worth it!

Furthermore, I recently noticed that my videos are receiving lots of hits and have fetched some very promising reviews. I'd like to thank you all for that. It's a relatively small thing for everyone of you to take the time out of your busy day and watch my work and you may not think much of it. However, it does make a difference to me, so I'm grateful for that. At a time when YouTube is the go-to option for many people, some of you choose quality over convenience and that's something I never take for granted. Cheers!

Since I'm in a quiz frame of mind these days, I've created yet another quiz video, which is now available on the O'Reilly platform. Namely, this quiz on Machine Learning vid explores a few key aspects of the subject, such as supervised, unsupervised and reinforcement learning, as well as the main model types and the hyper-parameters involved. Designed to be as inclusive as possible, this is a video that can benefit both the beginner to this topic and the more seasoned machine learning professional. Enjoy!

Note that O'Reilly is a subscription based platform (formerly known as Safari). So, in order to view this or any other video in its entirety, you'll need to have an account there. Definitely a worthwhile investment, if you ask me, particularly if you are a data science professional. I don't receive any benefits from saying this, btw, since I work with a different publisher (Technics Publications), who contributes these videos to this platform.

Just like week, during a business trip to London, I started working on this video, on my spare time, and now it's already online! In this 40 minute video, comprising of 3 clips, I explore the topic of Optimization, through a series of questions spanning across 5 categories. Whether you are an aspiring A.I. expert or a data scientist, you can learn a lot of useful things from this test of sorts and with the right mindset, even enjoy the whole process! You can find it on the O'Reilly platform, where you need to have an account (even a trial one will do) to watch it in its entirety. Cheers!

Being an expert in this topic since my PhD, I decided to create a video about it. The topic is a bit niche but it's very practical and useful in various data science tasks, particularly data engineering. Check out the video on O'Reilly and feel free to give me any feedback on it, especially regarding the I.D. metric once you look into it. Note that you will need an account on the O'Reilly platform in order to view the video (and any other material) in its entirety. However, considering the quality of the stuff there and the diversity of the content, it is a worthwhile investment. Also, you can have a free trial for 10 days to check it out, before you make a decision about it. Cheers!

Lately I created a video on Spark, its APIs, and how all this is useful as a data science tool. You can find it here though in order to watch it in its entirety, you need to have an account on O'Reilly (formerly known as Safari).

I understand that many of you came to this website today to check my weekly article on Data Science / A.I. and this announcement may be underwhelming. However, it is something that take precedence over any article, since my published work is more worthwhile to follow than the articles I write on this or any other blog. The former go through a certain editing process and are then published by a reputable 3rd party, while the latter are there just to offer a fresh perspective on various data science matters that I consider worth pondering about. Whatever the case, I am going to write an article this week, though it may be live later on, as I have other things going on these days. Thank you for your understanding!