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 ūüėČ

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 ‚Äď finding an individual that satisfies any one of the top four diagrams above is rare.


Enjoy reading¬†¬†Five Misconceptions About Data Science ‚Äď Knowing What You Don‚Äôt Know ‚ÄĒ 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 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.


What is the best thing that can happen to your career?

Diagram depicting a Japanese concept of Ikigai, meaning "a reason for being"

Today, I’ve read a tweet by Sinan Aral (@sinanaral) from the MIT:


I’ve just realized that Ikigai is what happened to my career as a data scientist. There was no point in my professional life where I felt boredom or¬†lack of motivation. Some people think that I’m good at what I’m doing. If they are right (which I hope they are), It is due to my love for what I have been doing since 2001. I am so thankful for being able to do things that I love, I care about, and am good at. Not only that, I’m being paid for that! The chart shared by Sinan Aral in his tweet should be guiding anyone in their career choices.


Featured image is taken from this article. Original image credit: Toronto Star Graphic 

Advice for aspiring data scientists and other FAQs ‚ÄĒ Yanir Seroussi

It seems that career in data science is the hottest topic many data scientists are asked about. To help an aspiring data scientist, I’m reposting here a FAQ by my teammate Yanir Seroussi

Aspiring data scientists and other visitors to this site often repeat the same questions. This post is the definitive collection of my answers to such questions (which may evolve over time). How do I become a data scientist? It depends on your situation. Before we get into it, have you thought about why you want […]

via Advice for aspiring data scientists and other FAQs ‚ÄĒ Yanir Seroussi

How to be a better teacher?

If you know me in person or follow my blog, you know that I have a keen interest in teaching. Indeed, besides being a full-time data scientist at Automattic, I teach data visualization anywhere I can. Since I started teaching, I became much better in communication, which is one of the required skills of a good data scientist.
In my constant strive for improving what I do, I joined the Data Carpentry instructor training. Recently, I got my certification as a data carpentry instructor.

Certificate of achievement. Data Carpentry instructor

Software Carpentry (and it’s sibling project Data Carpentry) aims to teach researchers the computing skills they need to get more done in less time and with less pain. “Carpentry” instructors are volunteers who receive a pretty extensive training and who are committed to evidence-based teaching techniques. The instructor training had a powerful impact on how I approach teaching. If teaching is something that you do or plan to do, invest three hours of your life watching this video in which Greg Wilson, “Carpentries” founder, talks about evidence-based teaching and his “Carpentries” project.

I also recommend reading these papers, which provide a brief overview of some evidence-based results in teaching:

What you need to know to start a career as a data scientist

Illustration: wall graffiti

It’s hard to overestimate how I adore StackOverflow. One of the recent blog posts on is “What you need to know to start a career as a data scientist” by Julia Silge. Here are my reservations about that post:

1. It’s not that simple (part 1)

You might have seen my post “Don‚Äôt study data science as a career move; you‚Äôll waste your time!“. Becoming a good data scientist is much more than making a decision and “studying it”.

2. Universal truths mean nothing

The first section in the original post is called “You’ll learn new things”. This is a universal truth. If you don’t “learn new things” every day, your professional career is stalling. Taken from the word of classification models, telling a universal truth has a very high sensitivity but very low specificity. In other words, it’s a useless waste of ink.

3. Not for developers only

The first section starts as follows: “When transitioning from a role as a developer to a position focused on data, …”. Most of the data scientists I know were never developers. I, for example, started as a pharmacist, computational chemist, and bioinformatician. I know several physicists, a historian and a math teacher who are now successful data scientists.

4. SQL skills are overrated

Another quote from the post: “Strong SQL skills are table stakes for data scientists and data engineers”. The thing is that in many cases, we use SQL mostly to retrieve data. Most of the “data scienc-y” work requires analytical tools and the flexibility that are not available in most of the SQL environments. Good familiarity with industry-standard tools and libraries are more important than knowing SQL. Statistics is¬†way more important than knowing SQL. Julia Silge did indeed mention the tools (numpy/R) but didn’t emphasize them enough.

5. Communication importance is hard to overestimate

Again, quoting the post:

The ability to communicate effectively with people from diverse backgrounds is important.

Yes, Yes, and one thousand times Yes. Effective communication is a non-trivial task that is often overlooked by many professionals. Some people are born natural communicators. Some, like me, are not. If there’s one book that you can afford buying to improve your communication skills, I recommend buying “Trees, maps and theorems” by Jean-luc¬†Doumont. This is a small, very expensive book that changed the way I communicate in my professional life.

6. It’s not that simple (part 2)

After giving some very general tips, Julia proceeds to suggest her readers checking out the data science jobs at StackOverflow Jobs site. The impression that’s made is that becoming a data scientist is a relatively simple task. It is not. At the bare minimum, I would mention several educational options that are designed for people trying to become data scientists. One such an option is Thinkful¬†(I’m a mentor at Thinkful). Udacity and Coursera both have data science programs too. The point is that to become a data scientist, you have to study¬†a lot. You might notice a potential contradiction between point 1 above and this paragraph. A short explanation is that becoming a data scientist takes a lot of time and effort. The post “Teach Yourself Programming in Ten Years” which was written in 2001 about programming is¬†relevant in 2017 about data science.

Featured image is based on a photo by Jase Ess on Unsplash

Don’t study data science as a career move; you’ll waste your time!

No, this account wasn’t hacked. I really think that studying data science to advance your career is wasting your time. Briefly, my thesis is as follows:

  • Data science is a term coined to bridge between problems and experts.
  • The current shortage of data scientists will go away, as more and more general purpose tools are developed.
  • When this happens, you’d better be an expert in the underlying domain, or in the research methods. The many programs that exist today are too shallow to provide any of these.

To explain myself, let me start from a Quora answer that I wrote a year ago. The original question was:

I am a pharmacist. I am interested in becoming a data scientist. My > interests are pharmacoeconomics and other areas of health economics. What do I need to study to become a data scientist?

To answer this question, I described how I gradually transformed from a pharmacist to a data scientists by continuous adaptation to the new challenges of my professional career. In the end, I invited anyone to ask personal questions via e-mail (it’s Two days ago, I received a follow-up question:

I would like to know how to learn data science. Would you suggest a master’s degree in analytics? Or is there another way to add “data scientist” label on my resume?

Here’s my answer that will explain why, in my opinion, studying data science won’t give you job security.

Data scientists are real. Data science isn’t.

I think that while “data scientists” are real, “data science” isn’t. We, the data scientists, analyze data using the scientific methods we know and using the tools we mastered. The term “data scientist” was coined about five years ago for the job market. It was meant to help to bring the expertise and the positions together. How else would you explain a person who knows scientific analysis, machine learning, writes computer code and isn’t too an abstract thinker to understand the business need of a company? Before “data scientist,” there was a less catchy “dataist” However, “data scientist” sounded better. It is only after the “data scientist” became a reality, people started searching for “data science.” In the future, data science may become a scientific field, similar to statistics. Currently, though, it is not mature enough. Right now, data science is an attempt to merge different disciplines to answer practical questions. Sometimes, this attempt is successful, which makes my life and the lives of many my colleagues so exciting.

Hilary Mason, from whom I learned the term dataist
Hilary Mason, from whom I learned the term “dataist”

One standard feature of most if not all, the data science tasks is the requirement to understand the underlying domain. A data scientist in a cyber security team needs to have an understanding of data security, a bioinformatician needs to understand the biological processes, and a data scientist in a financial institution needs to know how money works.

That is why, career-wise, I think that the best strategy is to study an applied field that requires data-intense solutions. By doing so, you will learn how to use the various data analysis techniques. More importantly, you will also learn how to conduct a complicated research, and how the analysis and the underlying domain interact. Then, one of the two alternatives will happen. You will either specialize in your domain and will become an expert; or, you will switch between several domains and will learn to build bridges between the domains and the tools. Both paths are valuable. I took the second path, and it looks like most of the today’s data scientists took that route too. However, sometimes, I am jealous with the specialization I could have gained had I not left computational chemistry about ten years ago.

Who can use the “data scientist” title?

Who can use the “data scientist” title? I started presenting myself as a “data scientist and algorithm developer” not because I passed some licensing exams, or had a diploma. I did so because I was developing algorithms to answer data-intense questions. Saying “I’m a data scientist” is like saying “I’m an expert,” or “I’m an analyst,” or “I’m a manager.” If you feel comfortable enough calling yourself so, and if you can defend this title before your peers, do so. Out of the six data scientists in my current team, we have a pharmacist (me), a physicist, an electrical engineer, a CS major, and two mathematicians. We all have advanced degrees (M.A. or Ph.D.), but none of us had any formal “data science” training. I think that the many existing data science courses and programs are only good for people with deep domain knowledge who need to learn the data tools. Managers can benefit from these courses too. However, by taking such a program alone, you will lack the experience in scientific methodology, which is central to any data research project. Such a program will not provide you the computer science knowledge and expertise to make you a good data engineer. 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.

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.

Lessons from the past

When I started my Ph.D. (in 2001), bioinformatics was HUGE. Many companies had bioinformatics departments that consisted of dozens, sometimes, hundreds of people. Every university in Israel (where I live), had a bioinformatics program. I knew at least five bioinformatics startups in my geographic area. Where is it now? What do these bioinformaticians do? I don’t know any bioinformatician who kept their job description. Most of those who I know transformed into data science, some became managers. Others work as governmental clerks.

The same might happen to data science. Two years ago, Barb Darrow from the Fortune magazine wrote quoting industry experts:

Existing tools like Tableau have already sweated much of the complexity out of the once-very-hard task of data visualization, said Raghuram. And there are more higher-level tools on the way … that will improve workflow and automate how data interpretations are presented. “That’s the sort of automation that eliminates the need for data scientists to a large degree,” …¬†And as the technology solves more of these problems, there will also be a lot more human job candidates from the 100 graduate programs worldwide dedicated to churning out data scientists
Supply, meet demand. And bye-bye perks.

My point is, you have to be versatile and expert. The best way to become one isn’t to take a crash course but to solve hard problems, preferably, under supervision. Usually, you do so by obtaining an advanced degree. By completing an advanced degree, you learn, you learn to learn, and you prove to yourself and your potential employees that you’re capable of bridging the knowledge gaps that will always be there. That is why is why I advocate obtaining a degree in an existing field, keeping the data science as a tool, not a goal.

I might be wrong.

Giving advice is easy. Living the life is not. The path I’m advocating for worked for me. I might be completely wrong here.

I may be completely wrong about data science not being a mature scientific field. For example, deep learning may be the defining concept of data science as a scientific field on its own.

Credits: The crowd image is by Flicker user Amy West. Hilary Mason's photo is from her site