Lately there is a lot of talk about AIs potentially taking people’s jobs in the future and how this is either catastrophic, or some kind of utopia (or, less often, some other stance in between). Although we as data science and A.I. professionals have little to do with the high-level decisions that have some influence on this future, perhaps we are not so detached from the reality of the situation. I’m not talking about the A.I. choir that is happy to recite its fantasies about an A.I.-based future that is akin to the sci-fi films that monetize this idea. I’m talking about grounded professionals who have some experience in the development of A.I. systems, be it for data science or other fields of application.

The problem with business decisions is that they are by their nature related to quite complex problems. As such, it is practically impossible to solve them in a clear-cut manner that doesn't invite reactions, or at least some debate. That’s why those individuals who have the courage to make these decisions are paid so handsomely. It’s not the time they put in, but the responsibility they undertake, that makes their role of value. However, it is important to make these decision as future-proof as possible, something that these individuals may not be able to do on their own. That’s why they have advisors and consultants, after all. Besides, even if some of the decision-makers are technical and can understand the A.I. matters, they may lack the granularity of comprehension that an A.I. professional has.

People who make business decisions often see A.I. as a valuable resource that can help their organization in many ways (particularly cut down on some costs, via automation or increased efficiency in time-consuming or expensive processes). However, they may not always see the implications of these moves and the shortcomings of this, still not yet mature, technology. A.I. systems are not objective, nor immune to errors. After all, most of them are black boxes, so whatever processes they have in place for their outputs are usually beyond our reach, and oftentimes beyond our comprehension. Just like it is impossible to be sure what processes drive our decisions based on our brain patterns, it is perhaps equally challenging to pinpoint how exactly the decisions of an A.I. are forged. That’s something that is probably not properly communicated to the decision makers on A.I. matters, along with the fact that AIs cannot undertake responsibility for these decisions, no matter how sophisticated these marvels of computing are.

Perhaps some more education and investigation into the nature of A.I. and its limitations is essential for everyone who has a say in this matter. It would be irresponsible to expect one set of people to navigate through this on their own and then blame them if their decisions are not good enough or able to withstand the test of time. This is a matter that concerns us all and as such we all need to think about it and find ways to contribute to the corresponding decisions. A.I. can be a great technology and integrate well in the job market, if we approach it responsibly and with views based on facts rather than wishful thinking.

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...

It is often the case that we treat a new A.I. as a child that we need to teach and pay close attention to, in order for it to evolve into a mature and responsible entity. However, a fox-like approach to this matter would be to turn things around and see how we, as human beings, can learn from an A.I., particularly of a more advanced level.
 
Of course A.I. is still in a very rudimentary stage of its evolution so it doesn’t have that much to teach us that we can’t learn from another human being. However, that wise human who would be a great mentor is bound to be bound by his everyday commitments, personal and professional making him inaccessible. Also, finding him may take many years, assuming that it is even possible given our circumstances. So, learning from an A.I. may be the next best thing, plus we don’t have to deal with personality-related impediments that often plague human relationships, even the more professional ones.
 
An A.I., first and foremostly is unassuming. This is something that we can all develop more, no matter how objective we think we are. A.I. doesn’t have any prejudices so it deals with every situation anew, much like a child, making it more poised to finding the optimum solution to the problem at hand. That’s something that is encouraged and often practiced in scientific ecosystems, like research centers and R&D departments, where the objective is so important that all assumptions are set aside, at least long enough for this approach to yield some measurable results.
 
A.I.s also tend to be very efficient, minimizing waste and unnecessary tasks. They don’t care about politics or massaging our egos. Their only focus is maximizing an objective function, given a series of restraints and, whenever it is applicable, take actions based on all this. If we were to act like that we’d definitely cut our time overheads significantly since we’d be concentrating more on results rather than pleasing some person who may have some influence over us professionally or personally.
 
A third lesson we could get from A.I. is organization. Although we most certainly have organization in our lives to some extent, we have a lot to learn from the cool-headed A.I. that employs an organizational approach to things. An A.I. tends to model its knowledge (and data) in coherent logical structures, immune to emotional or otherwise irrational influences. It deals with the facts rather than its interpretations of them. It builds functional structures rather than pretty pictures, to deal with the inherent disorder that its inputs entail. It makes graphs and optimizes them, rather than graphics that are easy on the eyes (although there is value in those too, in a data science setting). Clearly we don’t have to abandon our sentimental aspects in order to imitate this highly efficient approach to problem-solving, but we can try to be more detached when dealing with our work, rather than let sentimental attachments and eye candy exercise influence over our process.
 
Perhaps if we were to treat A.I. as a potential teacher of sorts, in the stuff it does well, it wouldn’t seem so threatening. Maybe feeling scared of it is merely a projection of ours, an objectification of our inherent fear of our own minds, which is still largely uncharted territory. A.I. doesn’t have an agenda and is not there to get us. If we treat it as an educational tool, it may prove an asset that will bring about a mutually beneficial synergy. It’s up to us.

I have talked about the value of a mentor in data science in a previous post. The thing is that even the best mentor in the world is bound to be ineffective if she is working with someone who is not embodying the protege role to a decent degree. But what does it mean to be a protege and how is that relevant in the path of development as a data science professional?
 
Let’s start by what a protege is not, since that’s more straight-forward and it is often a misconception in people’s minds, regarding this topic. So, a protege is not someone who passively receives knowledge and know-how from a mentor, nor is it someone who obeys blindly the instructions of his guide. A protege doesn’t have to be a helper either to the person who is mentoring him, although it is not unheard of. Also, a protege is not bound to given mentor, since he may be learning different things in his life or career, requiring a number of mentors.
 
A protege is more of a person willing to learn, mainly through his own efforts, yet open to guidelines by people more experienced and more knowledgeable than himself. A protege teaches himself and makes use of his mentor’s suggestions through an intelligent assimilation of them and through a constantly refined comprehension of the stuff he is working on. The mentor is more of a leader figure, who inspires, rather than demands, leading by example. The protege is humble enough to listen to her before judging the validity of what he hears and makes an effort to understand before choosing to go with it, or discard it. We can think of a protege like a bee, bound to a goal, but with the freedom to go about it in the most efficient way he comes up with. Also, if he decides to be an assistant of sorts to the mentor (usually in a company setting, where there is a more formal work relationship between the two), it is out of free will, rather than obligation.
 
Finally, it is important to note that the mentor is not a know-it-all so if she is true to herself and values mentoring, she is also a protege. Also, the protege himself may also be a mentor to someone else, perhaps some intern in his team. And since no mentor is adept in everything, it is quite common for someone to have several mentors throughout one’s life. In data science, for example, you may have a mentor to guide you through the whole pipeline of insight-derivation and data product development. However, you may find that you want to delve deeper into programming and choose to have another mentor in that aspect of the craft. Also, you may be into other activities, like creative writing and find that you need a different mentor there. So, it’s good to keep an open mind about the whole mentor-protege relationship.
 
What is your experience in being a protege? What would you expect from a mentor to make the most of your time with them? Where do you see the most value in being a protege?

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.

Data science is a rapidly evolving field, there is no doubt about that. However, this doesn’t have to be a stress factor for those involved in it. In fact, you can benefit from this as a field like that is bound to maintain a sense of novelty for a longer period of time. To make the most of this situation and take better advantage of the fast pace of data science, it would be best to have a mentor.
 
The role of a mentor has been popularized in pop culture, particularly in movies. Perhaps there is something in our culture that makes it very relevant, if not necessary, particularly in career-related endeavors. That’s not to say that a mentor is just a career-booster. In fact, you can have a mentor in every aspect of human culture, be it your profession, the art you feel expresses you the most, or even the sport you enjoy. A mentor is basically someone who is more adept at a certain activity and is eager to share his experience and expertise with you, usually as a labor of love. However, this doesn’t mean that a mentor is someone at the apex of their evolutionary journey. Anyone can be a mentor, given that they know enough and are willing to share all that in a constructive manner with their peers.
 
In data science, having a mentor is crucial, since there are so many new technologies out there, along with many more mature ones, that it’s often confounding! Also, with so many people having conflicting views on where data science is heading, and the recent buzz about A.I., you could really use some guidance, even if you know enough to call yourself a data scientist on your business card. Even though anyone more experienced than you can qualify to be your mentor, usually it is best if that person has enough commitment to the role to be of any use. I may want Mr. X to be my mentor, but if she is too busy with her career or her family to help me out, this isn’t really going to work, is it? One great place to find people who are serious about undertaking this responsible role is Thinkful, a startup that aims to connect data science learners with mentors in this field. Think of it like Uber for data science professionals (or aspiring professionals). Of course, there are other places where you can seek mentorship for your data science learning, but this is the one I’ve found to be the most serious about the task at hand.
 
Whatever mentoring ecosystem you decide to go with, it is important to cultivate the following qualities in yourself, so that you benefit from this experience the most. First of all, you need to have an open mind and be willing to learn new things. This seems obvious but you’ll be surprised how many people lack this fundamental requirement (which is probably why they never have a mentor throughout their careers). Also, you need to be willing to investigate whatever the mentor shares with you. Mentorship is not a cult. You need to take whatever your mentor tells you with a healthy skepticism. Look into it before you accept it. This allows for better comprehension as well. Finally, you need to be willing to change yourself, by applying the new things you learn. Clearly, learning is enjoyable, especially if you are not tested on it afterwards! However, for it to be useful, you need to apply what you learn, after you assimilate it of course. So, if you are willing to do that, you are bound to not only benefit from this new know-how, but also encourage your mentor to share more stuff, perhaps going deeper into the secrets of the craft.
 
Finally, whatever you decide to do with mentorship, be aware that this is not a one-directional graph. You can connect with other people, less experienced and less knowledgeable than you, and help them too. You can do that on your own, or via a more organized platform, like Thinkful. Whatever the case, even if this seems like a lofty goal for now, it doesn’t hurt having it in mind as a potential. Because at the end of the day, what’s left when everything else fades away, is our legacy. Personally, I can’t think of a better legacy than helping others in one’s field accomplish their potential through mentoring. What about you?

You have seen them. They are everywhere these days. I’m not talking about just the YouTube ones, that are taken for granted these days. There are educational videos on the MOOC platforms (e.g. edX and Coursera), on Vimeo, and of course, the Safari Books Online platform. Many of these are not free, which may deter some people, but there is value in all of them, to some extent. This value may not be so readily accessible though, as it’s often hidden, just like the signal in the data we are summoned to analyze.
 
Why look into educational videos and not just focus on books though? Well, books are great but in today’s fast-paced world, they are unable to keep up with the times so much. Even publishing houses that have high throughput often fail to deliver books fast enough for them to remain relevant for long. Of course the eBooks movement tackles this issue, at least partly. However, even eBooks are not so engaging as videos, since the latter have more channels to convey information. Emulating lectures and workshops, educational videos manage to engage the viewers through both visual and audio stimuli, diverting their attention to the most essential parts of their topics. Books can do that as well, if they are well-written, but they require much more concentration. Even if you possess this level of focus, you may not do so everywhere. For example, when you are on a bus or a train, the myriad of distractions may make focusing on a book for long quite a challenge. A video, however, is easier to concentrate on, even under these adverse conditions.
 
Watching educational videos, however, is not the same as watching a documentary or some other non-fiction audiovisual. The latter are created to be very engaging and even entertaining, to some extent. Educational videos, on the other hand, tend to be more packed in terms of information. One technique that I’ve found useful is taking notes while watching them. Fortunately, you can easily pause the video so that the note-taking activity doesn’t distract you. If you are on the move while watching the video, you can always take a shorter note, perhaps of the time-stamp of the part of the video that you feel requires more thought. This way, you can go back to it when you’re at home and delve deeper into it. Also, an educational video may require some work from you too. Apart from assimilating its content, you may need to do some research as well, on the topics it covers. This may not make you an expert, but it will definitely help you retain the stuff you’ve learned.
 
Unlike conventional videos that are geared towards giving you an excuse to eat some popcorn or chips, educational videos provide you with a different kind of reward. This may take a while to settle, since assimilating new information, especially know-how, can be a time-consuming process. However, they definitely help you right here right now keep boredom at bay and make the most of the time that you’d otherwise dedicate to less productive tasks. That’s not to say that you need to watch educational videos whenever you are not engaged in some other productive activity, but you can definitely strike a balance between watching an educational video and playing your favorite game on your phone!
 
If you haven’t done so already, check out my own educational videos on SafariBooksOnline.If you can go beyond my peculiar accent, there is no doubt that your mind will have quite a bit to chew on for that day!

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.