As a data scientist, there’s a lot you can learn from Formula One. We’re not talking just about the data that you can play around with. By going meta, you can learn good lessons that you can leverage to become a better data scientist.
At the end of every F1 season, there are two winners – the driver and the constructors. The constructors are the team that own the engine and the chassis. Both parties are critical components of the team and each plays an important role in the outcome of the race.
The driver is out there in crunch time – acting in real time and making decisions on the race track. He navigates and drives the car while handling the pressures of the race and the competition. If he hits the turn too fast or misses the line, millions of dollars and human lives can go up in a fiery explosion.
The constructors work their magic in laboratories and testing rigs. They invest in research and development to develop valuable intellectual property. They build the engine and the chassis the driver uses in the race. If the constructors build a chassis that cannot hold up to the rigors of a gruelling race, the team will lose.
Both the driver and the constructors operate in different arenas. But, they both contribute to the team’s success. The driver operates in real time, applying skills in the heat of the race. The constructor prepares for the race in advance and sets the driver up for victory.
In an ealier post, we delved into the concept of Data Science, Big Data and Data Analytics. It was a good introduction to three closely related topics that can be quite baffling. After writing that article, we realized that some of our readers had many questions. The questions from those in the early stages of their data science journey stood out, particularly this one:
How do I become a data scientist?
Have you ever wondered what it takes to become a data scientist? What is the exact sequence of steps that you need to follow?
We asked ourselves the same question, and decided that our graduates’ personal stories would make the best response. In the course of interviewing our students we made an interesting discovery: entering and succeeding in the world of data analytics has a lot to do with Formula One racing.
All the answers we received fell into one of two categories – mindset or skills. Most of the answers indicated that factors such as research, data processing, programming or statistics were critical to students’ success. We grouped these answers under a category called ‘Skills’.
According to our Formula One analogy, these skills constitute the driver’s contribution to the race. Knowledge of how a car handles, the best way to shift gears, when to overtake and when to draft behind another car are all critical skills that separate a good driver from the rest.
At Jigsaw Academy, our comprehensive library of training courses and videos is the ideal place to begin learning these critical skills for success as a data scientist.
The rest of the answers could be grouped under what we call ‘Mindset’. These are the softer, more nuanced skills that you gain through apprenticeship, emulation or experience. These are the skills that you would, after a while, term as the ingredients that affect your performance based on the preparation you do.
In our Formula One analogy, these would be akin to the constructors’ contributions. They represent an understanding of the key things that will impact the outcome of the race at a deeper level. An example would be the aerodynamics, stress testing, dyno and engine design.
Image Source: Freepik
Picking up skills in the ‘Mindset’ category would yield exponential returns on your investment. It doesn’t matter how good a driver you are – if you don’t have a good car, you can’t compete with drivers with better cars.
At Jigsaw Academy, we pride ourselves in equipping our graduates with the requisite ‘Skills’ and ‘Mindset’. Derek Sivers , a musician and entrepreneur said “Ideas are the multipliers of execution.” We train our students to have great ideas and execute them perfectly. We give them the perfect tool box to help them grow their careers in the frenzied but exciting world of data science.
Once you have your basics bolted down, you have several avenues to explore and build the different elements of your toolbox. You could join forums on LinkedIn and participate in discussions in popular data science communities like Data Science Central or Analytics Vidhya. There are several YouTube channels and MOOCs you could subscribe to, to enhance your knowledge. You could participate in competitions on sites like Kaggle or do data projects using the free datasets available online. You could even apprentice under someone or take up a job or an internship with any of the new analytics startups that have mushroomed all over the world!
We have compiled a list of skills that you should have in your toolbox as a data scientist. As with any tool box, you would use some skills more than others – but it is essential that you know how to use them all properly when required. Irrespective of the approach you choose, we, and our students, recommend that you cultivate the following skills:
Any execution would fall flat without the proper foundation. In the beginning, you have to learn the basics. There’s no escaping the magnitude of impact the fundamentals have on your career as a data scientist.
You need to learn the basics of mathematics, statistics, programming, tools and have a sound understanding of the industry (domain expertise) you wish to operate in.
At Jigsaw Academy, we can help you learn these skills. Click here to get started.