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!

Last week I’ve finished my part of the final corrections stage of the new technical book I’d been working on for the past few months. My co-author, Yunus, has done the same, so the book should be in the press later this month! Hopefully, you should be able to purchase it soon, either from the publisher’s site, or from some other vendor (e.g. Amazon). Just wanted to share that with you all. Once the book is out there, I’ll be sure to make an announcement about it here on this blog. Cheers!

A/B testing is a crucial methodology / application in the data science field. Although it mainly relies on Statistics, it has a remained quite relevant in this machine learning and AI oriented era of our field.  It's no coincidence that in Thinkful that's one of the first things data science students learn, once they get comfortable with descriptive Stats and basic data manipulation. So, I decided to do a video on this topic to help those interested in learning about it get a good perspective of it and understand better its relationship with Hypothesis Testing.  It is my hope that this video can be a good supplement to one's learning on the subject. Enjoy!

 I was never particularly fond of this unsupervised learning methodology that’s under the umbrella of machine learning. It’s not that I didn’t see value in it, but the methods that were available for it when I started delving into it were rudimentary at best and fairly crude. In fact, if I were to do a PhD now, I’d choose a clustering-related topic since there is so much room for improvement that even a simple idea for improving the most popular clustering methods out there is bound to improve them! 

However, the fact that data science researchers and machine learning engineers in particular haven’t spent much time looking into clustering doesn’t make clustering a bad methodology. In fact, I’d argue that it’s one of the most insightful ones and it plays an important role in many data science projects, particularly in the data exploration stage. 

The key issues with clustering are:
1. The whole set of distance metrics used
2. The fact that the vast majority of clustering methods yield a (slightly) different result every time they are run
3. The need of an external parameter (K) in most clustering methods used in practice, in order to define how many clusters there are
4. The fact that it’s very shallow in its results 

There may be more issues with clustering, but these are the most important ones I’ve found. So, if we were to rethink clustering and do it better, we’d need to address each one of these issues. Namely:
1. A new set of distance metrics would be needed, metrics that are not influenced by the dimensional “noise” so much, in the case of many dimensions in the dataset.
2. The option for a deterministic clustering method, one that would optimize the centroid seed before starting the whole clustering process
3. An optimization process would be in place so as to find the best number of clusters. This should include the possibility of a single cluster, in the case where there isn’t enough diversity in the dataset.
4. A multi-level clustering option needs to be available, much like hierarchical clustering but in reverse, i.e. start with the main clusters in the dataset and gradually dig deeper into levels of sub-clusters. 

Now, all this may sound simple but it’s not as easy to put into practice. Apart from an in-depth understanding of data science, a quite refined programming ability is needed too, so that the implementation of this clustering approach can be efficient and scalable. Perhaps all this is not even possible with the conventional data analytics framework, but there is not a single doubt in my mind that it is possible in general, and if a high-performance language is used (e.g. Julia), it is even practically feasible. 

Naturally, a clustering framework like this one would require a certain level of A.I. to be used. This doesn’t have to be an ANN though, since A.I. can take many forms, not just network-based ones. Whatever the case, conventional statistics-based methods may be largely inadequate, while the very basic machine learning methods for clustering may not be sufficient either. 

This illustrates something that many data science practitioners have forgotten: that data science methods evolve, just like other aspects of the craft. New tools may be intriguing, but equally intriguing are the conventional methodological tools, especially if we were to rethink them from a more advanced perspective. This can be beneficial in many ways, such as opening new avenues of data analytics and even synthesizing new data. This, however, is a story for another time...