Recently I had a nice chat with a fellow data scientist who works at LinkedIn. After bouncing some ideas off him, I decided to make another video, based on a topic of mutual interest, partly for demonstrating to him how straight-forward the process is, once you have done the research on the topic. This video is now published on Safari here (subscription required). Enjoy!
With so many options for publishing videos online nowadays, someone may wonder “why would I want to go through hoops to get something published on Safari?” This is a valid question, and it’s equivalent to asking “why should I get published through a publishing house when I can self-publish on Amazon, or some other platform?” Although there is merit in self-publishing, there are two main issues with it: quality assurance (QA), and marketing.
Before I get into the details of all this, let me inform you that I've been down the self-publishing path and it wasn't as glamorous as people make it out to be. I published not just 1, but 3 e-books, created a website for them, and even hired people to help promote them. A few years later the only real benefit I've seen through all this was the experience I’d gained through the whole process. So, if this is your sole motivation, that’s fine. If you however want to make enough money to make the whole thing worthwhile, then there are better options out there.
Getting published on Safari (or any other professional video platform) ensures a certain quality standard. Of course not all videos there are great, but at least you won’t find many that are a total waste of time or riddled with inaccurate information, like you would on YouTube, for example. The reason is that for a video to get on the Safari site, it first goes through some QA process. If there is an issue about it, you will need to revise it. This doesn't happen often, if you know what you are doing, but it’s a good fail-safe.
Marketing is another matter where platforms like Safari excel. If something is on Safari, people will see it and may watch/read it. If you have a video on YouTube, few people will notice it and even fewer will watch the whole thing. Especially now with the new strict policies that YouTube has adopted, content creators have it hard. Unless you create a lot of content regularly, your exposure on YouTube is bound to be very limited. Of course, if you create a lot of content, the quality is bound to drop, but YouTube doesn't seem to care much about this. As long as they get lots of people watching the videos they host, and keep the ad money rolling, they are fine. And if your vid gets flagged because some oversensitive person finds it problematic for whatever reason, that’s your problem, not YouTube’s.
I’m not trying to say that YouTube is bad. Every video hosting platform has its use cases. However, for quality content that you expect to at least pay for the effort you've put into creating it, a more professional platform like Safari makes more sense. You can create a promo video and put it on YouTube, or Vimeo. But if you spend a week creating a data science or A.I. video, you are better off publishing it through proper channels, like Safari.
To give you an idea of the profits that a Safari video can yield, last year I published a book. I spent about 9 months writing it and editing it. It was considered successful and helped me get some traction in the field, while also promote the programming language it was about. One of the videos I created and published for Safari yielded about the same revenue. It had taken me about a week to create it and edit it, while I also enjoyed it more, since it felt more like a creative endeavor, rather than work. Since I don’t have a huge following, I doubt that the same video could yield the same revenue if it were published on YouTube or some other open platform.
If you find that you have content you wish to share with the world, in a professional manner, I’d recommend you consider Safari as an option. If you find that it entails too much work and you are unsure as to where you need to start, you can always go through a publisher, like Technics Publications, like I did. As Nelson Mandela eloquently said, “it always seems impossible until it's done.”
After several days being in limbo, the video "Remaining Relevant in Data Science" that I've made recently, is now online on Safari (link). If you have a subscription to that platform, do check it out. If you prefer to access this kind of knowledge through a different medium, feel free to check out the last chapter of my latest book, Data Science Mindset, Methodologies, and Misconceptions. Enjoy!
So, my latest video is now available online at the Safari portal. I didn't post this yesterday, as I had already published an article for the blog. As I have been writing more articles that I can get published on DSP, I had to resort to this blog again. Also, I am not currently working on a book, so I have more time for writing for other channels (e.g. this blog, beBee, etc.).
Anyway, if you have a subscription for Safari, check out my video. I’m certain it would be worth your time. As always, I’m open to feedback via the “contact” page of this blog.
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.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.