The Formula One Grand Prix
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.
Down the rabbit hole 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:
Finding the best answers 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. The two parts of success
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 SQTL, our comprehensive library of training courses and videos is the ideal place to begin learning these critical skills for success as a data scientist.
608466The 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 Ideas and execution
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 SQTL, 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! Qualities of a Data Scientist
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:
Love of data: If you don’t enjoy working with numbers and data sets, data science may not be the right career path for you.
Curiosity: Curiosity may kill the cat, but it’s the single most defining trait you should cultivate as a data scientist. Being able to make logical deductions by asking the right questions and analysing the relevant data is an invaluable asset.
Skepticism: Like the hosts of ‘MythBusters’, it’s good to be a skeptic. This trait, as we have discovered, helps you test your assumptions, override your biases and consequently, become a better data scientist.
Numeracy: There is a curious phenomenon that we have noticed – some people are almost ‘proud’ to say that they cannot multiply large numbers or perform simple arithmetic in their head. But, to be a good data scientist, you need to be ‘numerically literate’. This doesn’t mean being a math genius or a stats geek. What it requires is a comfort with numbers, and an enthusiasm for data.
Vision: It’s quite easy to miss the forest for the trees. When dealing with numbers and datasets, you need to be able to pull back and see how your work fits in the whole system. You need to know how to ask the right questions and find the best answers.
Business acumen: Most data analysts will work either in academia or corporate environments. Business acumen, when applied to data science, adds a dimension of relevance and precision when communicating with ‘non-data scientists’.
Willingness to practice: Getting good at anything takes time. Malcolm Gladwell proposed that it may take 10,000 hours of practice to reach ‘expert status’. Timothy Ferriss and James Clear purport that you need ‘active practice’ i.e. all your practice time should be spent on practicing stuff that you’re bad at to grow. This requires an ironclad will and determination to practice.
Creativity & visualization: Getting insights from data is only one part of the solution. Presenting these insights in easily understandable ways adds a lot of merit and ‘punch’ to your tool box.
Communication skills: Paraphrasing Seth Godin, “Ideas are a virus. They spread when you share.” For you to share your insights and help them spread, you need to be able to communicate effectively in print, vocally and on stage.
Gaining the core skills to build the data science mind-set 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 SQTL, we can help you learn these skills. Click here to get started.