Sentiment Analysis is a popular NLP topic that I've been involved in for a while now. I even wrote an article about it for a friend of mine, who is an editor at a marketing blog. Anyway, after I finally finished my latest book (Technics Publications, ETA: Fall 2017), I had some time to work on a video for Safari Books Online. This video is now online at Safari and is probably going to be followed by similar ones on NLP and NLU related topics. Any suggestions are welcomed!

Bugs are terrible and high-level mistakes are even worse! Yet, most data science books out there don't say much about them, or how we can deal with them when they arise in our data science work. Reading these books may give someone the impression that everything in the data science world is smooth and filled with rainbows, something that is (sadly) far from the truth! So, instead of being in denial about this very important matter, we can choose to tackle it calmly and intelligently. This is why I made this video, which is now available on Safari Books Online for everyone interested in having a better and more bug-free data science life. Enjoy!

Why is it important to ask questions in data science? How can you answer these questions? Where do hypotheses fit in? How does all that relate to the know-how you have? So many questions! For some answers to them, feel free to check out my latest video on Safari Books Online. As always, your feedback is always welcome...

They are here. They mingle with us. They are luring more and more eyeballs towards their direction. Don't worry, I'm not talking about any of the malign A.I. creatures that Hollywood films tend to protray. I'm referring to the DS videos I'm making and publishing to Safari Books Online, via Technics Publications. The latest one, "Data Science and A.I. - What's the Difference?" is now available on O'Reilly's digital media platform. Check it out when you have a moment.

Just a heads-up. My second video for Technics Publications, "Becoming a Data Scientist, in a Nutshell" is now available at Safari Books Online (link to site). Based on my first book "Data Scientist: the definite guide to becoming a data scientist", this video covers some key aspects of the data science role and provides some practical advice on what skills you need to develop in order to pursue a career in data science.

This post is not about the talk on this topic that I gave at Galvanize a couple of months ago. This was for the few who happened to be around the Seattle area and who didn’t have any other commitments at that time. I’m referring to the video based on this talk which I created afterwards and that found its way to the SafariBooksOnline website, via Technics Publications.
 
This 20+ minute video covers some of the basics of Julia (so that you don’t have to read a book on it to learn them), as well as some more data science specific topics, illustrating how it can be a useful tool in your toolkit. I am not making the argument that Julia is the next best thing since sliced bread, like other passionate coders often do, particularly when talking about Python in relation to R, or vice versa. Everyone has options and Julia is just one of them. Since it is the option I am qualified to talk about more than any of the other ones, I choose to do so in this video. My hope is that people will start using it more, probably in combination with Python, or whatever else they are using (even the C language). Because at the end of the day, what’s important is not the tool itself, but what you do with it.
 
However, how useful a tool is greatly depends on the know-how around it. Even though you won’t be an expert in Julia by watching this video, you will get a good understanding of what it is about and why it can be a useful technology to know if you are doing data science. The better you are at data science, the better your chances of finding it useful. This is probably why many people use Julia for other applications (e.g. academic research, simulations, etc.). There is nothing wrong with that, since Julia was developed to be a versatile tool. The reason why this video is special is that it demonstrates a certain angle that many Julians may not be so aware of: Julia’s usefulness in data science. So, if you are intrigued by this possibility, here is my recommendation: improve your data science know-how, examine where you can use Julia in your data science pipeline, and start experimenting with it for specific data science problems that you are trying to solve. Hopefully this video can be an asset towards this objective.
 
 
Disclaimer: I’m not poised to promote Julia because someone told me so, or because it’s a niche technology that I happened to be an expert in, at least for data science applications. The reason I’m promoting this new tech is because right now it appears to be the optimum choice for doing data science, particularly the hard parts of it. If Dr. X of university Y comes out tomorrow with a new programming platform that outperforms Julia overall, you can be sure that I’ll be looking into it with the same zest as I now have for Julia.