These are the only 3 things that you need to become a great Data Scientist
A reflexion on what I’ve learned during my career change into data science
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When I decided to change careers, leaving behind my years of managing large marketing communication campaigns, it didn’t take me much research until I stumbled across the hot new career of the moment: Data Science. Forbes was saying it was a hot career, Harvard Business Review had said it before that it was the sexiest job of the 21st century, and last year, in 2019, Data Scientist was named the best job in America, according to Glassdoor.
That of course got me curious, but I was soul-searching for something more than a hot job title or a(another) well-paid career. I wanted to feel that my work was making a positive contribution to society. Yet, as I started to learn more about the field, I found myself increasingly excited about the potential that Data Science holds for creating positive change in the world.
For example, I learned on this inspiring TED Talk about how a great organization, Crisis Text Line, uses data science to make their important work helping teenagers and young adults in crisis become faster and more accurate. (they also make their wealth of data on real-time crisis, free of any identifiable information, open so that more people can use it.)
I was seeing many more examples of how Data Science can help improve the world, from helping reduce air pollution in London, to important advances in the medical field like helping doctors choose the best cancer treatment for individual patients, to many, many more.
I felt deeply inspired and I was completely sold. However, could I really get into this field, coming from a Liberal Arts background with my Communications degree? Would I have what it takes? Spoiler: the answer is yes! After a year of learning Data Science at a fast pace, all the while becoming more and more passionate about the field, I share with you my must-haves for anyone who is also considering going into Data Science. These below are the three points that you need to check the box for in order to become a great data scientist:
1. A Self-starter Attitude
The life of a Data Scientist is constant learning. You may graduate from your bootcamp, college, masters degree, or even your PhD, but you will never, ever get to a point where you don’t need to keep learning new things. Not only the field is constantly changing, but it is also vast. Thanks to its collaborative nature and large number of open source libraries and software, there is always something that even the most advanced data scientist will want to learn.
And what does that mean? It means that you must have the skills needed to learn by yourself and from others, in an active way. You must be comfortable reading documentation, searching high and low for solutions for the issues you might be struggling with, finding resources for the type of problem that you have at hand, many times learning a new library or method or even a language in order to accomplish your goals. You must be willing to collaborate, to reach out to other people, to exchange knowledge.
You will many times have to be your own coach and motivator (and become best friends with Google), and also develop or hone a go-get-it-done attitude.
2. Some inclination to Math and Statistics
Can someone with a Language Arts background break into Data Science? Absolutely! However, if you feel like you want to run for the hills when you see a complex-looking mathematical equation, or if you really, really hate numbers and graphs, well then maybe this field is not for you. Even if you haven’t used math or statistics in your life before, you do need to be comfortable around these disciplines if you want to work with Data Science.
You don’t need to know the how to calculate the maximum likelihood estimation of a Bernoulli distribution out of the top of your head, but you do need to be able to understand what is the intuition behind a gradient descent. In your daily work as a data scientist, for most cases you don’t really have to DO any heavy math, because the code will do it for you, but it’s important to have a sense of what it is being done and why. In sum, if you can understand the overall concepts, you have most of what is necessary for the day-to-day work.
The same goes for statistics and probabilities. You will be using these disciplines A LOT, so if you don’t see yourself doing that on a regular basis and still being a happy person, well, bad news. But if a beautiful distribution plot that explains the massive, complex data that you have in a clear, simple way that anyone can understand fills you with joy, you’ve got what it takes.
Business and Communications Skills
This one might be the least obvious one, especially for people who are very new to learning about data science. Anyhow, you thought you would just master Python ,or R, or both, learn how to apply some really cutting-edge algorithms, work on some super challenging Kaggle competitions and thus become a great Data Science right?
Well, no. In order to do an excellent job as a Data Scientist, you will have to be able to understand your company’s business, what they are doing, what are their needs and the needs of their customers. That is how you will be able to have the best insights when looking at the data, and how you will be capable of asking the right questions while investigating your data science task or problem at hand.
As much as the field might look very technical from a standpoint, some business knowledge and also great communication skills are paramount to being a prominent Data Science. Once you find valuable insights from your data or great solutions to the problems that you have been working on, you will need to be able to communicate them clearly and objectively to a non-technical audience. That is almost impossible to avoid, moreover if you are working at a smaller company where you are their whole data science team, or if you aim to raise at the ranks at larger companies. You need to be able to confidently and straightforwardly explain your project to your stakeholders, technical and non-technical.
There are many lists out there on what are the main skills a data scientist needs or what makes a good data scientist but in my view, these are the three ones that make or break it. Do you have what it takes? Let me know if you agree with my take, or what you would add to my list in the comments!
