In the previous article, I talked about the dichotomy of Student – Mentee in a data science context. However, it’s really the dichotomy of teacher – Mentor that is at the root of all this, while the data science field itself has a role to play too, something many learners of the craft have forgotten. In this article, we’ll explore just that, in an attempt to gain a better perspective of how true learning works and what it takes to connect to the essence of this fascinating field that’s being tainted by the ones who see it as merely a career-boosting opportunity.
Although there is nothing wrong with the role of a professor in any field, especially the fields of science, it’s important to highlight a distinct difference between a professor and a mentor. The former is usually geared towards giving a set of lectures, in order to fulfill the requirements of his/her professional position, something that may or may not be adequate for conveying the essence of the field, especially for a field as complex as data science. It’s not that the professor doesn’t care for all this, but the nature of this profession makes it incredibly difficult, if not impossible, to do this justice. After all, most of these professional educators have other priorities, such as their research.
Mentors, on the other hand, help others learn about data science, not because it’s our job, but because we care about the field, while we have other sources of income to cover our daily expenses. Of course, we may still have monetary benefits linked to mentoring, but it’s generally not the key motivation of all this. Also, we share knowledge about the field based on our own experience, rather than some curriculum which may not always align with the field. Finally, the connection with the people we help (mentees) is more direct and tailored to their needs, rather than generic and impersonal.
It’s important to note that these two roles although different, may still have an overlap. There are professors who can act as mentors, though they usually do this outside the classroom, as in the case of their supervisory role for a PhD student. Also, someone can be a mentor and yet also work part-time in a university. So, it’s good to maintain a flexible view of this whole matter.
Anyway, if you are willing to learn data science in depth, it’s definitely better to do so through a mentor, particularly one with a diversity of experiences in the field. But what about the mentor himself? Where does he learn about all this? In many cases, a mentor may have another mentor to learn from, though it is also possible that the data science field itself is that person’s mentor. After all, data science is a living field, dynamic and ever-changing, with plenty of things to teach to those who are willing to learn from it. Many of its secrets have been discovered but there is still a lot that it’s uncharted territory. That’s something data science can teach anyone who is willing to learn from it. All it takes is a solid understanding of the fundamentals, a strong sense of discipline, and the open-mindedness to abandon what you know for what you can know if you maintain a beginner’s mind...
I've talked about mentoring in the past and what a good mentee looks like. Here I'd like to highlight the differences between a student and a mentee since it's easy to confuse the two.
First of all, a student is someone who is generally more passive than a mentee. The latter takes initiative and feels responsible for her progress in the field of study. The former often outsources this to the instructor(s) and focuses more on passing exams rather than actually learning.
In addition, a mentee has a closer connection with the person helping him learn, namely the mentor. The student's relationship with his instructors is more impersonal, mainly because the latter have lots of students to deal with and can't usually focus on every single one unless it's for their dissertation project or something. The mentor, however, is more dedicated to getting to know the mentee better and coach him accordingly.
Moreover, the mentee-mentor relationship is beyond academia, even though it can exist in a university too (e.g. in the case of a PhD program). More often than not, mentees and mentors are working professionals, while the former tends to already have a degree.
Furthermore, mentees tend to have a more focused approach to learning, usually related to a specific field, much like an apprentice. The student, on the other hand, may study lots of different fields, as part of her curriculum at the college or university.
Interestingly, although there are several university courses on Data Science these days, most people who learn the craft, tend to do so either independently or through the help of a mentor. Perhaps there is something to this, other than a coincidence. That's not to say that university courses are bad, but oftentimes learning data science as a mentee tends to be more cost-effective and efficient, time-wise.
What are your thoughts and experiences on the matter?
Short answer: Nope! Longer answer: clustering can be a simple deterministic problem, given that you figure out the optimal centroids to start with. But isn’t the latter the solution of a stochastic process though? Again, nope. You can meander around the feature space like a gambler, hoping to find some points that can yield a good solution, or you can tackle the whole problem scientifically. To do that, however, you have to forget everything you know about clustering and even basic statistics, since the latter are inherently limited and frankly, somewhat irrelevant to proper clustering.
Finding the optimal clusters is a two-fold problem: 1. you need to figure out which solutions make sense for the data (i.e. a good value for K), and 2. figure out these solutions in a methodical and robust manner. The former has been resolved as a problem and it’s fairly trivial. Vincent Granville talked about it in his blog, many years ago and since he is better at explaining things than I am, I’m not going to bother with that part at all. My solution to it is a bit different but it’s still heuristics-based. The 2nd part of the problem is also the more challenging one since it’s been something many people have been pursuing a while now, without much success (unless you count the super slow method of DBSCAN, with more parameters than letters in its name, as a viable solution).
To find the optimal centroids, you need to take into account two things, the density of each centroid and the distances of each centroid to the other ones. Then you need to combine the two in a single metric, with you need to maximize. Each one of these problems seems fairly trivial, but something that many people don’t realize is that in practice, it’s very very hard, especially if you have multi-dimensional data (where conventional distance metrics fail) and lots of it (making the density calculations a major pain). Fortunately, I found a solution to both of these problems using 1. a new kind of distance metric, that yields a higher U value (this is the heuristic used to evaluate distance metrics in higher dimensional space), though with an inevitable compromise, and 2. a far more efficient way of calculating densities. The aforementioned compromise is that this metric cannot guarantee that the triangular inequality holds, but then again, this is not something you need for clustering anyway. As long as the clustering algo converges, you are fine.
Preliminary results of this new clustering method show that it’s fairly quick (even though it searches through various values of K to find the optimum one) and computationally light. What’s more, it is designed to be fairly scalable, something that I’ll be experimenting with in the weeks to come. The reason for the scalability is that it doesn’t calculate the density of each data point, but of certain regions of the dataset only. Finding these regions is the hardest part, but you only need to do that once, before you start playing around with K values.
Anyway, I’d love to go into detail about the method but the math I use is different to anything you’ve seen and beyond what is considered canon. Then again, some problems need new math to be solved and perhaps clustering is one of them. Whatever the case, this is just one of the numerous applications of this new framework of data analysis, which I call AAF (alternative analytics framework), a project I’ve been working on for more than 10 years now. More on that in the coming months.
Recently I came across this interesting platform for sharing curated content, called Wakelet. It's also a British startup from Manchester, by the way, one that appears quite promising, given that they find a way to monetize their project.
Anyway, the platform is a bit like Pinterest but with more features and an offline presence too. These are the most important features, in my view:
* Very intuitive and fast to learn
* Can work with a variety of content types: videos, images, formatted text, PDFs, and website links
* Every list can be exported to a PDF
* Free to use
* No account is required to view the lists
* Lots of free images to use as thumbnails and backgrounds
* QR code is generated for each list you wish to share
* Private lists are also an option
* Plenty of tutorials online that explain the various features and use-cases
You can check out a wake (that's how these curated lists are called) that I've made in the space of a few minutes, here. In the future I'll probably be using it more, particularly on this blog. Whatever the case, do let me know what you think of this platform and of my wake. Cheers!
This famous Buddhist quote is one of my personal favorites and one that Bruce Lee also used in one of his movies. Although it may seem more relevant to some Eastern philosophy or martial arts, it actually has a lot of relevance in data science too.
Through this blog, my books, and my videos, I’ve put forward some ideas and hopefully some useful knowledge for anyone interested in data science and A.I. However, it’s easy to mistake conviction with cult-like hegemony, something I’ve observed in social media a lot. Whenever someone competent enough to have a good professional role and some prestige comes about, many people choose to become his or her followers, treating that person as a guru of sorts. This, in my view, is one of the most toxic things someone can do and it’s best to avoid at all costs. That’s not to say that all those people who have followers are bad, far from it! However, the act of blindly following someone just because of their status and/or their conviction is dangerous. You may get lots of information this way, but you will lose the most important thing in your quest: initiative.
Of course, some of these people are happy to have a following and couldn’t care less about your loss of initiative. After all, they often measure their value in terms of how many followers they have, how many downloads their free book has, and how many likes they receive. This in and of itself should raise some serious red flags because no matter how much data science or A.I. know-how these individuals have, the path they are on doesn’t go anywhere good.
I’m a firm believer in free will and I value it more than anything else, especially in the domain of science. As data science (and A.I.) are part of this domain, it’s imperative to show respect to this quality, even at the expense of a large following. That’s why whenever I share something with you, be it some data science methodology, some A.I. system, some heuristic, or some ideas about our field, I expect you to experiment with it and draw your own conclusions. Don’t take my word for it, because even though I make an effort to verify everything I write about, some inaccuracies are inevitable. After all, data science and A.I. are not an exact science!
Naturally, it takes more than experimentation to learn data science and A.I., but with some guidance, some contemplation, some skepticism, and some experimentation, it is quite doable to learn and eventually master this craft. That has been my experience both for my own journey in data science and A.I., as well as in the journeys of my mentees. Hopefully, your experience will be equally rewarding and educational...
With so many ways to get a book out there, even in a fairly challenging subject such as data science, you may wonder what this process entails and what is the best way to go about it. After all, these days it’s easier than ever to reach an audience online and promote your work, all while branding yourself as a professional in the field.
Writing a book in data science is first and foremost an education initiative, targeting a particular audience. Usually, this is data science learners though it may be other professionals involved in data science, such as managers, developers, etc. A data science book generally tries to explain what data science can do, what its various methodologies are, and how all of that can be useful for solving particular problems (emphasis on the last part!). If you see a book that focuses a lot of the methods, particularly those of a particular methodology, it may be too specialized to be of most audiences, unless you are targeting that particular niche that requires this specific know-how.
A key thing to note when exploring the option of writing a book is a publisher. Even if you prefer to self-publish, your book must be able to compete with other books in this area and a publisher is usually the best way to figure that out. If a publisher is interested in your book, then it’s likely to be somewhat successful. Also, if you are new to book authoring, you may want to start with a publisher since there are a lot of things you’d never learn without one. Also, a book published through a publisher is bound to have more credibility and a larger life-span.
Understandably, you may have explored the various deals publishers make with their authors and figured out that you’ll never make a lot of money by publishing books. Fair enough; you’ll probably never make a living by selling your words (although it is possible still). However, if your book is good, you’ll probably make enough money to justify the time you’ve put into this project. Also, remember that most publishing deals provide you with a passive income, even if the publisher wants you to promote your book to some extent. So, even though you won’t make a lot of cash, you’ll have a revenue stream for the duration of your book’s lifetime.
With all the data science material available on the web these days, acquiring all the relevant information and compiling it into a book is a fairly straight-forward task. However, just because it is fairly feasible, it doesn’t mean that it’s what the readers need. Without someone to guide you through the whole process and give you honest feedback (that’s also useful feedback), it’s really hard to figure out what is necessary to put in the book, what should be included in an appendix, and what should be mentioned in a link. Your readers may or may not be able to provide you with this information, while if your main means of interacting with them is how many of them download your book or visit your website, you are just satisfying your ego!
A publisher's honest feedback often hurts but that’s what gradually turns you into a real author, namely one who has some authority in his/her written works. Otherwise, you’ll be yet another writer, which is fine if you just want to talk about writing a book or how you have written a book that you have on Amazon, things that are bound to be forgotten quicker than you may think…
As the field of Data Science matures and everything in it is categorized and turned into a teaching module, compartmentalization may seem easier and more efficient as a learning strategy. After all, there is a bunch of books on specialized topics of the craft. That’s all great and for some people, it may even work satisfactorily, but that’s where the risk lies and it’s a pretty big risk too!
Learning about something specialized in data science, particularly without a good sense of context or its limitations, can be catastrophic. The old saying “for someone who only knows how to use a hammer everything starts looking like a nail” is applicable here too. Learning about a specialized aspect of data science can often make you think that this is the best approach to solving data science related problems. After all, the author seems to know what he’s talking about and some employers value this skill. However, if this know-how is out of context, it is bound to be ineffective at best and problematic at worst. Data science is an interdisciplinary field with lots of different tools in it, from various areas. Anyone who tries to dissect it and focus mainly on one of them is doing a disservice to the field and if you as a data science learner pay attention to this person, you are bound to warp your knowledge of the craft and delay your mastery of it.
Also, this overspecialization in know-how may make you think that you are better than the other data science practitioners who have not developed that niche skill yet. This will limit your ability to learn and perhaps even cooperate with these people, significantly. After all, you are an expert in this, so why bother with less fancy know-how at all? Well, sometimes even the more humble aspects of the field, such as feature engineering, can turn to be more effective at solving a problem well, than some fancy model, so it’s good to remember that.
That’s why I’ve always promoted the idea of the right mindset in data science, something that no matter how the field evolves, it is bound to remain stable in the years to come and help you adapt to whatever know-how becomes the norm. Also, no matter how important the algorithms are, it’s even more important knowing how to create your own algorithms and change existing ones, optimizing them for the problem at hand. That’s something that no data science book teaches adequately, as the emphasis is covering material related to certain buzzwords, sometimes without the supervision of an editor. The latter can help immensely in making the contents of a book more comprehensible and relevant to data science in general, providing you with a sense of perspective.
So, be careful with what you let enter your data science curriculum as you learn about the craft. Some books may be a waste of time while others, especially those not published through a publisher, may even hinder your development as a data scientist.
Lately I worked on a new series of videos, this time on Optimization. This A.I. methodology is a very popular one these days, one that adds a lot of value to both data science and other processes where resources are handled. Specifically, I talk about:
* Optimization in general (including its key applications)
* Particle Swarm Optimization
* Genetic Algorithms
* Simulated Annealing
* Optimization ensembles
* some auxiliary material that supplements these topics
You can find this video series on Safari, along with my other A.I. videos. Cheers!
So, about 18 months ago I created a video on Safari about how A.I. could benefit Data Science (DS and AI). Even though at that time I was still figuring things out regarding how educational videos work, the vid was immensely popular and even today still attracts lots of views. Considering that all of my recent videos are (much) better than that one, at least technically, this is quite intriguing.
Anyway, fast forward to September last year. As I was walking in the streets of suburban Seattle, thinking about what to do next (my Data Science Mindset, Methodologies, and Misconceptions book had just been released), I decided to write another book, one about A.I. since this topic continued to fascinate me, while it was becoming a popular topic among various data scientists. So, I pitched the idea of a new book to Steve Hoberman and after sorting out the details, we got a contract going. However, due to various reasons we decided to start the book in January.
The whole project was quite a turbulent one, with my co-author dropping out around March, leaving me in a very difficult situation. Yet, I decided that the book was worth completing. Fortunately, another data scientist / A.I. expert decided to join me in this endeavor, Yunus E. Bulut, who I got acquainted with through Thinkful. Long story short, after a few discussions about the project he had a contract of his own as a co-author.
Three months later, the first draft was complete. Of course the book went through a lot of revisions since then, partly because the technology was changing and partly because there were a lot of topics in this book, which was difficult to coordinate and merge into a coherent whole. Also, at one point Julia reached adulthood as a programming language (v. 1.0) so we had to update the code for the chapters that had programs in Julia.
So, after a feverish summer, plagued by heat waves and other obstacles, we finished the edits (at least the most important ones, since a book is never really finished!) and the book went to the press. Now, it is finally available for you to buy at whatever vendor you prefer. Check out the publisher's site for more details. Cheers!
With all the plethora of material out there for data science education, it is easy to get overwhelmed and even confused about what to study and how much time, money, and effort to put into it. Enter evaluation of data science material, a concise strategy for tackling this issue. In this 24 minute video, I talk about the various aspects of data science material, criteria for evaluating it, the matter of resources required to delve into this material, and some useful things to have in mind in your data science education efforts. Whether you are a newcomer to the field or a more seasoned data scientist, you have something to learn about data science (I know I do!) and this video can hopefully aid you in that. You can find it on Safari.
Note that in order to be able to view this video in its entirety, you'll need a subscription for the Safari platform. Also, it's important to remember that this video can offer you a framework for evaluating the data science material; you'll still need to find that material though and put the effort to study it, in order to make the most of it. The video can only help you organize your efforts more efficiently. Enjoy!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.