People talk a lot these days about what it takes to be a good data scientist and how if you do their boot camp or join their course you will acquire that and make yourself stand out from the data scientist pool. Some of these people may be on to something but they generally focus a lot of specific skills and general abilities. That’s fine if you have the time to study what they are saying and find for yourself what you need. However, if you just want a single idea that is in the root of all the stuff they talk about, that’s something few can share with you, because they probably don’t know.
There are data scientists know, however, what it takes to be a good data scientist and many of them have already embodied this in their careers. Yet, they are so busy applying this that they don’t go out of their way to let you know, unless of course they are into education, in which case they will probably mention it in their books or videos.
One feature that I’ve found it succinctly summarizes what it takes to be a good data scientist, regardless of your domain or your specialization, is persistent engagement in the craft. Let’s break this down a bit, since it’s a fairly complex feature (a meta-feature if you will). This comprises of two things working in tandem: persistence and engagement. The first has to do with a sense of rhythm and commitment. All decent data scientists are very focused on what they are doing, even if they are involved in other things (e.g. 90-95% of my work is around data science, though I’m also involved in Cyber Security and to a smaller extent, in Neuroscience). Also, we tend to practice data science in one way or another very regularly. In other words, it is part of our daily routine. That’s all manifestations of consistency.
As for engagement, that is more of an inner state, an aspect of the mindset of a good data scientist. It involves being fascinated by the craft, even if it may seem that it doesn’t have any secrets from you any more. The thing is that there are always new things to learn, especially over time as it evolves and new methods and techniques come about. Engagement is akin to what is known in Zen as the “beginner’s mind” which is a certain approach to things as if they are completely new to you. Coupled with the experience and expertise that a good data scientist has, this approach allows him to go more in depth regarding the field and find new ways to bring about value through data science. It also involves coming up with new models, new processes for data engineering, and in some cases, new data products.
Consistent engagement in data science doesn’t require particular talent or experience, however. Everyone can (and ought to) embrace it. So, instead of trying to memorize the inner workings of some obscure model, just because someone else says so, try cultivating this trait first. Afterwards, everything else will appear easier and more interesting, just like new know-how appears intriguing and within reach, to a novice that has a genuine thirst for learning. After all, there are many ways to achieve mastery of the craft, but they all go through consistent engagement.
If you are looking into a way to hide those ultra-secret blog articles before they hit the web, or those intimate poems of yours, then you may be interested in this Cyber Security methodology called Steganography.
This video I made that was recently published on Safari, takes you through the basics of Steganography and provides you with enough know-how to appreciate it, as well as with some tools you can use to hide your important documents from the unsuspecting eavesdroppers out there. Check it out when you have the chance!
For those of you celebrating Thanksgiving, I just wanted to wish you all a happy Thanksgiving weekend! There are lots of things to be thankful for and studies have shown that gratitude is linked to happiness. So, even if it doesn't always seem like it, this is a holiday for happiness (not just getting some good deals on Black Friday!). What are you thankful for in your life (apart from being into the fields of data science and A.I. of course)?
Sometime in October, one of the Foxy Data Science readers contacted me with a question/suggestion about this topic. As I hadn’t really thought about it much, I decided to look into it and write a blog post about it. I’m not an expert in AEI, but I believe I know enough about A.I. in general and about the business world to venture an insightful view on the matter. At the very least, it can trigger some interesting contemplation in you.
Artificial Emotional Intelligence is a kind of A.I. that emulates the EQ aspects of our mental process. In other words, it is machines that know (to some fairly limited extent) how to exhibit qualities that fall on the intersect between intelligence and emotional maturity, aka EQ. By the way, I do not believe that EQ is more important than IQ, nor that it is any less important. Both are equally useful and neither can be a substitute for SQ (moral intelligence), which is a truly superior kind of intelligence. This, however, could be the topic of another blog post…
Considering the possibility of computers and machines in general, emulating empathy and other traits that are under the EQ umbrella seems a bit futuristic. However, there are already A.I. systems that do just that. Not only that, some of them are quite successful, particularly in psychology roles, even more so than their human counterparts (link to some interesting research by USC).
Could this be the end of EQ-based professions? Probably not, though these people may start considering offering something more than just listening and nodding, if they are to stand out from their AEI competition. Naturally, psychology is so much more than helping someone vent about their issues and showing them that there are more constructive ways to dealing with their problems, something that AEIs may be able to do equally well. That’s why this whole AEI business may be an incentive for these professionals to expand their profession and turn their sessions into something more, something AEIs may not be able to mimic (for the time being). Art therapists, for example, seem to do just that, combining the benefits of conventional psychology with that of an art form (usually music, painting, or dance).
AEIs may be nothing more than a novelty now, but it very poignantly points to the possibility of new forms of A.I. that the original pioneers of the field may not have thought of. Movies like “Her” may be science fiction but for how long? These are interesting things to think about, since A.I. just like natural intelligence, can take many forms, not just the ones that we are more inclined to investigate so far. Surely Deep Learning may still be the most relevant A.I. for data science, but it doesn’t hurt to consider other ways that a machine can benefit the world through A.I. After all, there is much more to life than predicting a hand-written digit with high accuracy. Maybe in the years to come there will be AIs that can look at your handwriting and not only understand it, but also figure out if you are going through a difficult time in your life and require solace and comfort. We definitely live in interesting times!
Education in Data Science
“I have never let my schooling interfere with my education.” (quote believed to be originally by Mark Twain)
People talk about education a lot these days, particularly in a data science setting. However, we need to discern between actual education and training. Both are essential, but it is the former that holds the most value. The latter is easier and oftentimes faster, but it may not be a good investment of your time if it is not accompanied by the former.
Education is all about mindset development and the ability to feel inspired from knowledge, thereby developing a healthy yearning for it. It is what happens when you teach a child how to play a game, or do a specific task. Although it’s more of a state of mind than anything else, education also has a formal aspect to it which is related to courses, seminars, workshops and talks, geared towards enhancing one’s understanding and comprehension of the topic at hand.
Training on the other hand is more geared towards techniques, methods, and the technical details of the topic taught. This is useful, of course, since every data scientist needs to know all these things. That’s why there are so many data science books and videos out there! However, knowing how to build an SVM or a neural network doesn’t make someone a competent data scientist. In fact, in some cases it doesn’t make him even an employable one.
Perhaps there is a reason why most companies require X years of experience in their recruits. Some things in data science you can only learn through time, by practicing them and by developing an intuition for the data and how it is processed. Although the idea that a data scientist has to have X years of experience to be worthy is something that remains debatable (why X and not Y?), this trend shows that hiring managers can spot a difference between someone who knows data science from a book (or videos) and someone who knows the craft because she has worked the data and has developed a bunch of models, through lots of trials and the inevitable mistakes that ensue.
Education is therefore something that can be attained through experience, not just reading and watching data science material on the Safari platform. The latter can be a great start, but you still need to get your hands dirty and also think about the whole thing, instead of just following recipes, from a data science cookbook. It’s important to know techniques, no doubt, but unless you have developed an understanding that allows you to go beyond these techniques and explore alternative features and alternative models, you may never grow beyond the advanced beginner stage.
Even someone who has spend most of his life in data science can still learn about this field, as it's a) very diverse and wide-spread, and b) always evolving. Personally, I still find that I’m learning new things as I delve deeper into the field and as I converse with other data scientists and A.I. professionals, of all levels. This too can be a form of education, not any less valuable than the education of creating a new data analytics method, or a new data product. The moment someone starts looking down on education and thinks that he knows “enough” is the moment he begins becoming obsolete.
We often tend to forget that at the end of the day, data science is a business process and that data is a business resource. Whether this business is a for-profit or a non-profit is irrelevant. The essence of the whole thing is that data science is not a typical scientific field. In fact, some would argue that it’s not a “real science” at all since it is so attached to the business world. Although these people would probably view this as a defect of the craft, I tend to look at it from a very positive aspect. After all, what constitutes a real science is often a matter of debate.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.