On MOOCs

When Massive Online Open Courses (a.k.a MOOCs) emerged some X years ago, I was ecstatic. I was sure that MOOCs were the Big Boom of higher education. Unfortunately, the MOOC impact turned out to be very modest. This modest impact, combined with the high production cost was one of the reasons I quit making my online course after producing two or three lectures. Nevertheless, I don’t think MOOCs are dead yet. Following are some links I recently read that provide interesting insights to MOOC production and consumption.

  • A systematic study of academic engagement in MOOCs that is scheduled for publication in the November issue of Erudit.org. This 20+ page-long survey summarizes everything we know about MOOCs today (I have to admit, I only skimmed through this paper, I didn’t read all of it)
  • A Science Magazine article from January, 2019. The article, “The MOOC pivot,” sheds light to the very low retention numbers in MOOCs.
  • On MOOCs and video lectures. Prof. Loren Barbara from George Washington University explains why her MOOCs are not built for video. If you consider creating an online class, you should read this.
  • The economic consequences of MOOCs. A concise summary of a 2018 study that suggest that MOOC’s economic impact is high despite the high churn rates.
  • Thinkful.com, an online platform that provides personalized training to aspiring data professionals, got in the news three weeks ago after being purchased for $80 million. Thinkful isn’t a MOOC per-se but I have a special relationship with it: a couple of years ago I was accepted as a mentor at Thinkful but couldn’t find time to actually mentor anyone.

The bottom line

We still don’t know how this future will look like and how MOOCs will interplay with the legacy education system but I’m sure the MOOCs are the future

Book review: The Formula by A. L Barabasi

The bottom line: read it but use your best judgement 4/5

I recently completed reading “The Formula. The Universal Laws of Success” by Albert-László Barabási. Barabási is a network science professor who co-authored the “preferential attachment” paper (a.k.a. the Barabási-Albert model). People who follow him closely are ether vivid fabs or haters accusing him of nonsense science.

For several years, A-L Barabási is talking and writing about the “science of success” (yeah, I can hear some of my colleagues laughing right now). Recently, he summarized the research in this area in an easy-to-read book with the promising title “The Formula. The Universal Laws of Success.” The main takeaways that I took from this book are:

  • Success is about us, not about you. In other words, it doesn’t matter how hard you work and how good your work is, if “we” (i.e., the public) don’t know about it, or don’t see it, or attribute it to someone else.
  • Be known for your expertise. Talk passionately about your job. The people who talk about an idea will get the credit for it. Consider the following example from the book. Let’s say, prof. Barabasi and the Pope write a joint scientific paper. If the article is about network science, it will be perceived as if the Pope helped Barabasi with writing an essay. If, on the other hand, if it is a theosophical book, we will immediately assume that the Pope was the leading force behind it.
  • It doesn’t matter how old you are; the success can come to you at any age. It is a well-known fact that most successful people broke into success at a young age. What Barabási claims is that the reason for that is not a form of ageism but the fact that the older people try less. According to this claim, as long as you are creative and work hard, your most significant success is ahead of you.
  • Persistence pays. This is another claim that Barabasi makes in his book. It is related to the previous one but is based on a different set of observations (did you know that Harry Potter was rejected twelve times before it was published?). I must say that I’m very skeptical about this one. Right now, I don’t have the time to explain my reasons, and I promise to write a dedicated post.

Keep in mind that the author uses academic success (the Nobel prize, citation index, etc.) as the metric for most of his conclusions. This limitation doesn’t bother him, after all, Barabási is a full-time University professor, but most of us should add another grain of salt to the conclusions. 

Overall, if you find yourself thinking about your professional future, or if you are looking for a good career advice, I recommend reading this book. 

Curated list of established remote/distributed/virtual tech companies

Illustration: a young woman works on a computer and drinks from a cup

Someone asked me about distributed companies or companies that offer remote positions. Of course, my first response was Automattic but that person didn’t think that Automattic was a good fit for them. So I googled and was surprised to discover that my colleague, Yanir Seroussi, maintains a list of companies that offer remote jobs.

I work at Automattic, one of the biggest distributed-only companies in the world (if not the biggest one). Recently, Automattic founder and CEO, Matt Mullenweg started a new podcast called (surprise) Distributed.

The third wave data scientist – a useful point of view

diagram that shows "business mindset" in the middle, surrounded by three segments: "soft skills" "statistics toolbox" and "software engineering craftsmanship"

In 2019, it’s hard to find a data-related blogger who doesn’t write about the essence and the future of data science as a profession. Most of these posts (like this one for example) are mostly useless both for existing data scientists who think about their professional plans and for people who consider data science as their career.

Today I saw yet another post which I find very useful. In this post, Dominik Haitz identifies a “third wave data scientist.” In Dominik’s opinion, a successful data scientist has to combine four features: (1) Business mindset (2) Software engineering craftsmanship (3) Statistics and algorithmic toolbox, and (4) Soft skills. In Dominik’s classification, the business mindset is not “another skill” but the central pillar.

The professional challenges that I have been facing during the past eighteen months or so, made me realize the importance of points 1, 2, and 3 from Dominik’s list (number 4 was already very important on my personal list). However, it took reading his post to put the puzzle parts in place.

Dominik’s additional contribution to the discussion is ditching the famous data science Venn Diagram in favor of another, “business-oriented” visual which I used as the “featured image” to this post.

Painting: sailors in a wavy sea
A fragment from an 1850 painting by the Russian Armenian marine painter Ivan Aivazovsky named “The Ninth Wave.” I wonder what the “ninth wave data scientist” will be.

To specialize, or not to specialize, that is the data scientists’ question

In my last post on data science career, I heavily promoted the idea that a data scientist needs to find his or her specialization. I back my opinion with my experience and by citing other people opinions. However, keep in mind that I am not a career advisor, I never surveyed the job market, and I might not know what I’m talking about. Moreover, despite the fact that I advocate for specialization, I think that I am more of a generalist.

Since I published the last post, I was pointed to some other posts and articles that either support or contradict my point of view. The most interesting ones are: “Why you shouldn’t be a data science generalist” and “Why Data Science Teams Need Generalists, Not Specialists“, both are very recent and very articulated but promote different points of view. Go figure

The featured image is based on a photo by Tom Parsons on Unsplash

The data science umbrella or should you study data science as a career move (the 2019 edition)?

Illustration: photo of people under umbrellas

TL/DR: Studying data science is OK as long as you know that it’s only a starting point.

Almost two years ago, I wrote a post titled “Don’t study data science as a career move.” Even today, this post is the most visited post on my blog. I was reminded about this post a couple of days ago during a team meeting in which we discussed what does a “data scientist” mean today. I re-read my original post, and I think that I was generally right, but there is a but…

The term “data science” was born as an umbrella term that meant to describe people who know programming, statistics, and business logic. We all saw those numerous Venn diagrams that tried to describe the perfect data scientist. Since its beginning, the field of “data science” has finally matured. There are more and more people that question the mere definition of data science.

Here’s what an entrepreneur Chuck Russel has to say:

Now don’t get me wrong — some of these folks are legit Data Scientists but the majority is not. I guess I’m a purist –calling yourself a scientist indicates that you practice science following a scientific method. You create hypotheses, test the hypothesis with experimental results and after proving or disproving the conjecture move on or iterate.

Screenshot of a Google image search showing many Venn diagrams
There can’t be enough Venn diagrams

Now, “create and test hypotheses” is a very vague requirement. After all, any A/B test is a process of “creating and testing hypotheses” using data. Is anyone who performs A/B tests a data scientist? I think not.
Moreover, a couple of years ago, if you wanted to run an A/B test, perform a regression analysis, build a classifier, you would have to write numerous lines of code, debug and tune it. This tedious and intriguing process certainly felt very “sciency,” and if it worked, you would have been very proud of our job. Today, on the other hand, we are lucky to have general-purpose tools that require less and less coding. I don’t remember the last time I had to implement an analysis or an algorithm from the first principles. With the vast amount of verified tools and libraries, writing an algorithm from scratch feels like a huge waste of time.
On the other hand, I spend more and more time trying to understand the “business logic” that I try to improve: why has this test fail? Who will use this algorithm and what will make them like the results? Does effort justify the potential improvement?

I (a data scientist) have all this extra time to think of a business logic thanks to the huge arsenal of generalized tools to choose from. These tools were created mostly by those data scientists whose primary job is to implement, verify, and tune algorithms. My job and the job of these data scientists is different and requires different sets of skills.

There is another ever-growing group of professionals who work hard to make sure someone can apply all those algorithms to any amount of data they feel suitable. These people know that any model is at most as good as the data it is based on. Therefore, they build systems that deliver the right information on time, distribute the data among computation nodes, and make sure no crazy “scientist” sends a production server to a non-responsive state due to a bad choice of parameters. We already have a term for professionals whose job is to build fail-proof systems. We call them engineers, or “data engineers” in this case.

The bottom line

Up till now, I mentioned three major activities that used to be covered by the data science umbrella: building new algorithms, applying algorithms to business logic, and engineering reliable data systems. I’m sure there are other areas under that umbrella that I forgot. In 2019, we reached the point where one has to decide what field of data science does one want to practice. If you consider stying data science think of it as studying medicine. The vast majority of physicians don’t end up general practitioners but rather invest at least five more years of their lives professionalize. Treat your data science studies as an entry ticket into the life-long learning process, and you’ll be OK. Otherwise, (I’m citing myself here): You might end up a mediocre Python or R programmer who can fiddle with the parameters of various machine learning libraries, one of the many. Sometimes it’s good enough. Frequently, it’s not.

PS. Here’s a one-week-old article on Forbes.com with very similar theses: link.

Five misconceptions about data science

One item on my todo list is to write a post about “three common misconceptions about data science. Today, I found this interesting post that lists misconceptions much better than I would have been able to do. Plus, they list five of them. That 67% more than I intended to do 😉

I especially liked the section called “What is a Data Scientist” that presents six Venn diagrams of a dream data scientist.

The analogy between the data scientist and a purple unicorn is still apt – finding an individual that satisfies any one of the top four diagrams above is rare.

 

Enjoy reading  Five Misconceptions About Data Science – Knowing What You Don’t Know — Track 2 Analytics

Once again on becoming a data scientist

My stand on learning data science is known: I think that learning “data science” as a career move is a mistake. You may read this long rant of mine to learn why I think so. This doesn’t mean that I think that studying data science, in general, is a waste of time.

Let me explain this confusion. Take this blogger for example https://thegirlyscientist.com/. As of this writing, “thegirlyscientst” has only two posts: “Is my finance degree useless?” and “How in the world do I learn data science?“. This person (whom I don’t know) seems to be a perfect example of someone may learn data science tools to solve problems in their professional domain. This is exactly how my professional career evolved, and I consider myself very lucky about that. I’m a strong believer that successful data scientists outside the academia should evolve either from domain knowledge to data skills or from statistical/CS knowledge to domain-specific skills. Learning “data science” as a collection of short courses, without deep knowledge in some domain, is in my opinion, a waste of time. I’m constantly doubting myself with this respect but I haven’t seen enough evidence to change my mind. If you think I miss some point, please correct me.

 

 

Don’t take career advises from people who mistreat graphs this badly

Recently, I stumbled upon a report called “Understanding Today’s Chief Data Scientist” published by an HR company called Heidrick & Struggles. This document tries to draw a profile of the modern chief data scientist in today’s Big Data Era. This document contains the ugliest pieces of data visualization I have seen in my life. I can’t think of a more insulting graphical treatment of data. Publishing graph like these ones in a document that tries to discuss careers in data science is like writing a profile of a Pope candidate while accompanying it with pornographic pictures.

Before explaining my harsh attitude, let’s first ask an important question.

What is the purpose of graphs in a report?

There are only two valid reasons to include graphs in a report. The first reason is to provide a meaningful glimpse into the document. Before a person decided whether he or she wants to read a long document, they want to know what is it about, what were the methods used, and what the results are. The best way to engage the potential reader to provide them with a set of relevant graphs (a good abstract or introduction paragraph help too). The second reason to include graphs in a document is to provide details that cannot be effectively communicating by text-only means.

That’s it! Only two reasons. Sometimes, we might add an illustration or two, to decorate a long piece of text. Adding illustrations might be a valid decision provided that they do not compete with the data and it is obvious to any reader that an illustration is an illustration.

Let the horror begin!

The first graph in the H&S report stroke me with its absurdness.

Example of a bad chart. I have no idea what it means

At first glance, it looks like an overly-artistic doughnut chart. Then, you want to understand what you are looking at. “OK”, you say to yourself, “there were 100 employees who belonged to five categories. But what are those categories? Can someone tell me? Please? Maybe the report references this figure with more explanations? Nope.  Nothing. This is just a doughnut chart without a caption or a title. Without a meaning.

I continued reading.

Two more bad charts. The graphs are meaningless!

OK, so the H&S geniuses decided to hide the origin or their bar charts. Had they been students in a dataviz course I teach, I would have given them a zero. Ooookeeyy, it’s not a college assignment, as long as we can reconstruct the meaning from the numbers and the labels, we are good, right? I tried to do just that and failed. I tried to use the numbers in the text to help me filling the missing information and failed. All in all, these two graphs are a meaningless graphical junk, exactly like the first one.

The fourth graph gave me some hope.

Not an ideal pie chart but at least we can understand it

Sure, this graph will not get the “best dataviz” award, but at least I understand what I’m looking at. My hope was too early. The next graph was as nonsense as the first three ones.

Screenshot with an example of another nonsense graph

Finally, the report authors decided that it wasn’t enough to draw smartly looking color segments enclosed in a circle. They decided to add some cool looking lines. The authors remained faithful to their decision to not let any meaning into their graphical aidsScreenshot with an example of a nonsense chart.

Can’t we treat these graphs as illustrations?

Before co-founding the life-changing StackOverflow, Joel Spolsky was, among other things, an avid blogger. His blog, JoelOnSoftware, was the first blog I started following. Joel writes mostly about the programming business and. In order not to intimidate the readers with endless text blocks, Joel tends to break the text with illustrations. In many posts, Joel uses pictures of a cute Husky as an illustration. Since JoelOnSoftware isn’t a cynology blog, nobody gets confused by the sudden appearance of a Husky. Which is exactly what an illustration is – a graphical relief that doesn’t disturb. But what would happen if Joel decided to include a meaningless class diagram? Sure a class diagram may impress the readers. The readers will also want to understand it and its connection to the text. Once they fail, they will feel angry, and rightfully so

Two screenshots of Joel's blog. One with a Husky, another one with a meaningless diagram

The bottom line

The bottom line is that people have to respect the rules of the domain they are writing about. If they don’t, their opinion cannot be trusted. That is why you should not take any pieces of advice related to data (or science) from H&S. Don’t get me wrong. It’s OK not to know the “grammar” of all the possible business domains. I, for example, know nothing about photography or dancing; my English is far from being perfect. That is why, I don’t write about photography, dancing or creative writing. I write about data science and visualization. It doesn’t mean I know everything about these fields. However, I did study a lot before I decided I could write something without ridiculing myself. So should everyone.

 

The Keys to Effective Data Science Projects — Operationalize

Recently, I’ve stumbled upon an interesting series of posts about effective management of data science projects.  One of the posts in the series says:

 “Operationalization” – a term only a marketer could love. It really just means “people using your solution”.

The main claim of that post is that, at some point, bringing actual users to your data science project may be more important than improving the model. This is exactly what I meant in my “when good enough is good enough” post (also available on YouTube)