For those of you who missed out on our article on how to become a data scientist in these series, you can read it here.
Let us now go back to our question.
What should you do to become a data scientist? If you do not remember your choice, wait a minute. Scroll back and recollect what you said that you liked.
I am an “I love coding and mathematics” kind of person. In fact, I am sort of at my best when I am alone and in front of my terminal.
You should train yourself to be a data scientist who can stretchalgorithms. You stretch an algorithm in two ways
If it is comfortable working on some amount of data at a certain speed, you learn the software programming and engineering required to run the algorithm 100 times more data with 10 times more speed. In other words, you learn to scale an algorithm.
You learn to make its underlying mathematics better so that it works even better than it used to. Essentially, you invent newer mathematical formulae. So, you learn research methods in AI required to test existing models and build better ones.
Once trained on these two aspects, you will be recruited by AI product companies and R&D labs of AI and make a lot of money!!!
I am an “I love building things: I take apart things and put them together” kind of person.
You should train yourself in two disciplines at an advanced level: A core branch of science or engineering and data science. That way, you should be able to use data science techniques in addition to core engineering, chemistry, or health care principles to solve complex problems.
You need not invent new algorithms. You use existing algorithms on your data to build powerful models. Yes, the focus here is on building models. So, you learn to collect the data carefully, build models using your core science and data science, combine both to build even better models. You learn to test and tweak.
Once trained in building models using data science and core science, you join those core science firms (manufacturing, pharma, finance, energy, petrochemicals, etc.) in responsible positions. As you can solve problems using basic science and data science, you grow much faster than the others.
I am “I love thinking about problems logically and communicate” kind of person.
You need to train yourself to visualize models effectively in addition to mastering basic business processes and communications. The focus here is definitely not algorithms. It is also a lot less of model development.
Instead, you train on defining problems that are not solved effectively in your firm and learn to build quick and dirty solutions using data science. Then you visualize and communicate the pros and cons of implementing these solutions.
Once trained in the business and visualization aspects of data science, you will become a favorite candidate for consulting firms or any position that involves working with clients closely in helping them make better decisions.
So, hopefully, you see that not every data scientist needs to learn mathematics, programming, modeling, visualizations, and…
Truly understand what kind of career excites you. Then you use this article to figure out exactly what aspect of data science you should master to reach those goals.
We will be glad to talk to you to help you reach the goal. We have special programs designed for coding geeks, engineers & science graduates, and managers. Contact us to learn more