1st Story — Start with Why Data Science
I started learning data science about 3 years before I started learning data science. If this sounds confusing to you, imagine how muddled I was. Here is the story, I worked as a business consultant for organizational culture, this meant I helped companies navigate the ins and outs of how they can build the right policies, environments and development programs which promote the behavior that represents their desired culture. Since one of the basic and most important recommendations of this is constant development, staying up to date and learning new things was, and still is, a big part of my life.
So, several years ago, after reading more and more about data science and machine learning in the news, I decided to learn more about it. After some light research online and a few YouTube videos, I decided to enroll in an online course from John Hopkins University through Coursera: Data Science: Foundations using R. After all it covered all of what I thought I needed, it was going to teach me programming (I had not done any programming since I was about 10 years old, on a Texas Instrument computer with Basic language, saving, or more accurately, recording, on a magnetic tape cassette), also some deeper statistics (I had read these were very important for data science), it was self-paced, online and from a very recognizable institution.
Everything started wonderfully, I learned some basics about what data science is (and understood about half of it), and I started understanding some basic concepts of R programming. Nonetheless, the challenges piled on very quickly and, as at the time, my level of tolerance to my code not working was very slim (and as anyone who has learned to code will know, failure is a big part of learning and a bigger part of doing actual programming).
So, I moved on to other things, coming back when the mood struck and quitting again as my mood changed again. This was my love hate relationship with data science for 3 years.
My wife, who is also my business partner, and I, had been working on an idea for some time regarding ways to diagnose human development, for our consulting business. As we worked on initial development, one night we were watching a documentary called “The Great Hack”, about how companies like Cambridge Analytica have been using behavioral economics and machine learning to manipulate people. That day, something clicked, and we started talking more and more about how, if you can develop systems to bring out the worst of our nature, we could also create something that does the complete opposite, nudge people to help them become better.
For the next few months, I read and researched about this, and about machine learning models that were helping people. I found that most literature on the subject has to do with improving business perspectives and sales, which makes perfect sense, but this just motivated me even more, having the background on psychology, psychometry and behavioral economics, all that I needed now was to learn Data Science.
The big lesson here, and apologies to Simon Sinek for taking the words out of his book: Start with Why. If you have a clear purpose, learning becomes much more enjoyable, everything new that you discover, becomes a clear idea for a future project, every time you encounter a challenge, you figure out a way to overcome it, because the purpose of your journey is so important, climbing the biggest mountain is just a way to get to the other side.
So dear reader considering entering the incredible world of data science (and especially me from a few years ago), before you even start to plan what to learn, before having to face the ever lasting debate about Python or R, before discovering that you will need to learn linear algebra and calculus, and even before you ask the weird question of why is Bayes Naïve (apologies for the geek statistics joke), ask yourselves this: Why do I want to learn data science.
Do not try to come up with an excuse, but a reason, a purpose. It can be anything as long as it’s very important to you and you alone. Remember that this reason will be your guiding light, your source of almost infinite courage and motivation. It will carry you when you are struggling with *args and *kwargs, it will guide you through Random Forests and K-Neighbors, it will empower you through Eigenvectors and PCA and it will accompany you every long night, every broken model and every error in your code.
Your purpose will drive you every step of the way on your Road to Data Science.
Hope we cross paths through our Journeys…
Jack Raifer Baruch
Follow me on Twitter: @JackRaifer
Follow me on LinkedIN: jackraifer
Next Story: The Eternal Conflict of Python and R
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 thorough 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.
