March 2019: Two years after the completion of this post I wrote a follow-up. Read it here.
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 email@example.com). 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” http://www.dataists.com/. 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.
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 https://hilarymason.com/about/