Data Scientists — Taking Control of Our Industry
Today, the Road to Data Science takes a different turn, instead of another story about my experiences, so you can have a smoother journey, I wanted to talk about where I believe data science, and mostly data scientists should be going.
First off, a bit of background. As some of you may know, I am psychologist, specializing in behavioral economics and psychometry, but what most of you probably do not know, is that I have spent a lot of time the past 10 years working closely with many entrepreneurs as an entrepreneurship and innovation professor, consultant, and mentor. In all those years, I have had the privilege and honor of working with hundreds of entrepreneurs and intrapreneurs from all industries, and the pleasure of learning the ins and outs of building something from problem (because you should start with a problem and not with an idea), to a product sold in the market.
From that experience I have been noticing a worrying trend in data science: most people are talking about doing things to improve their resume to get a job (or a better job), and very, very few are talking about using their skills to create a business, or even a product. Just a quick note, there is one huge exception to this: Data Science education. In the last few years there have been tons of startups popping up to teach data science, as well as many amazing solopreneurs creating content and building their Data Science consulting or mentoring businesses.
Having made that exception clear, let us focus on ourselves, Data Scientists, taking control of the industry. Since businesses have been businesses, they have been mostly controlled by two types of people: money holders and inventors. Money holders are people who simply have money to spare and are always investing in all kinds of new businesses, weather they understand them or not. Their objective, to make more money. The second, inventors, people who create valuable things for others and leverage that to create a new business. A hundred years ago, most business was led by the first kind of person, but lately, it is the second. Today, 7 of the world’s 10 biggest companies were created, and are mainly ran, by inventors.
What about Data Science? Well, this one is complex. Most data science today is created by some of the companies above, and they are all tech companies. Alphabet, Amazon, Facebook, Apple, IBM, they are not just the leaders in tech, they are the leaders in Data Science as well. Let us get one thing straight, this is not unexpected, data science is fairly new, and the industry has mostly come from this tech companies, and they are also the ones who have collected the largest amount of data, this is not a critique, just an incredibly early wake up call.
Today, data science is HOT, everyone is talking about it and many companies feel the need to hire a data scientist, even if they have no idea why they need one or what to do with them. However, there are 10 times more data science degrees and certificates than data science startups, and that is a problem. This means we are educating a whole slew of people with skills in managing data and building models, at a much faster rate than jobs are growing. On the other side, although the amount of Data Science jobs is growing, they are mostly working in using their skills in improving whatever it is the company who hired them is already doing. Again, nothing wrong with this, but the downside is that we have a growing stock of Data Scientists looking for a job, who end up becoming the new wave of corporate reengineering, and very few working industry changing products. And even worse, the companies who are leading the changes, do not necessarily have a Data Science perspective.
The world could keep going the same way, and data science will still be at the core of more engagement, more sales, more growth, more, more, more.. of the same.
Let us imagine the future for a bit:
What would happen if Data Scientists started running the path of data science?
What would the world look like if companies led by data scientists would become more common?
What is the world we, as Data Scientists, want to build?
These may all seem complicated questions to answer, but I am sure most of us have an answer.
For me, I believe that if Data Scientists were leading business, we would have a more scientific, evidence-based society. We would have better products and services focused not just on the economic growth of the company, but with the purpose of improving every aspect of peoples lives, using data to nudge positive change instead of manipulating convenient behavior. We would be able to build a kind of world imagined by people like Carl Sagan and Gene Roddenberry, where evidence and not belief lead decisions and there is truly a view of an abundant, integrated, and equal society.
Maybe I am just dreaming. Maybe this is just my dream. But what else is life for than to simply chase after your dreams.
Now, entrepreneurship is not about talking, on the contrary, it is about doing, building, and creating. So, in the next few days, I will be launching the initially named Datapreneurs Initiative, to gather people interested in building startups with Data Science at the core, with a framework based on Evidence Based Entrepreneurship and Lean Startup, to go from problem, to sales. Where people can find teammates and mentors to help along the way.
Will it work? I have no idea. But there is one thing I am sure of. It will be based on the best framework data has created so far, and it will only get better. A better way to answer would be: “I do not know if it will work, but am highly confident it will”.
If you want to join the group, once it is up and running, drop me note on LinkedIn or Twitter (links below).
Hope we cross paths through our Journeys…
Jack Raifer Baruch
About the Road to Data Science Series
Today, I am working on the first steps of remarkably interesting projects for human development based on Data Science and Machine Learning.
But not that long ago (really, not long at all) I knew extraordinarily little about data science and much less what it all meant (and I am still learning more and more about it every day). In my quest for reinventing myself from Psychologist working in Behavioral Economics to Data Scientist I went through an incredibly interesting journey and learned a lot. This series is mostly a letter to my past self, to help anyone like me take this amazing road and, luckily, avoid some of the mistakes I made on the way due to lack of knowledge or perspective.
Hope you enjoy my ramblings as much as I found joy on my Road to Data Science.
Need Help on your Journey?
This can be a difficult path alone, so feel free to reach out to me through LinkedIN or Twitter. I started this series because of the #66DaysOfData initiative by Ken Jee, it is a great way to connect and get support, so just check out Ken on twitter @KenJee_DS and join the #66DaysOfData challenge.
Learning Resources I have Used:
A LOT of content, some free, most paid. Check out cupon sites where you can usually find free cupons for courses on python, R, data science, machine learning and much more.
Interesting place to learn, they have some free courses and then paid content. Very hands on coding exercises, few videos, mostly reading.
My favorite place to learn. Thousands of courses, a lot of content on programming, Data Science and Machine Learning. The University of Michigan has many courses here for python programming from the very basics to complex things. All courses are free to audit, you only pay if you want to earn a certificate.
The top free place to learn to code. Hundreds of hours of free videos on almost any language. They now also have certifications, also for free.
The place to learn anything. All of it is free, it might take a while to get to the content you want and enjoy.
Top site for data science, also run many competitions. They have many free courses, but the programming part is scarce, some basic ones and all focused on Data Science and Machine Learning.
Similar to Codecademy, with many paths and courses. Some free content, the rest is paid. Very focused on Data Science.
My favorite place to practice code, challenges for every level from beginners to advanced. This is a good place to challenge yourself and check your progress.