Using deliberate practice to study data science will set you apart from other data scientists
What differentiates data scientists who’ve made a career change and entered a new field in less than a year from the rest of us mere mortals?
No, they didn’t necessarily have a career in tech on which they could propel themselves into becoming a data scientist.
And no, they likely weren’t gifted in the art of programming, mathematics, and data visualization.
Instead, what sets apart those who get job offers from FAANG companies just months after beginning their journey into data science is that they are doing a daily habit differently than you.
All data scientists should be doing this daily habit, but only a few are doing it in such a way that they are progressing in their knowledge, skill, and careers far beyond those of their peers.
This habit is known as deliberate practice.
Luckily, deliberate practice is a habit that’s accessible to all and only requires you to be comfortable living and working outside of your comfort zone.
Deliberate practice is defined as “being effortful in nature, with the main goal of personal improvement of performance rather than enjoyment, and is often performed without immediate reward.”
Essentially, deliberate practice is practicing skills on the periphery of your comfort zone that act to improve your skills continuously.
For example, cooking is a difficult task to perform, but after spending many hours cooking different dishes, it becomes second nature and a task you can perform without much thought. However, after a certain point, your cooking skills don’t improve if you’re regularly making the same dishes over and over again. Your cooking skills will plateau, and may even regress because you’re not pushing yourself beyond your comfort zone. However, if you begin deliberately learning how to make different dishes from foreign cuisines, learning how to create full meals from only a few ingredients, or learning how to substitute ingredients, your skills will expand immediately.
Deliberate practice is designed so that your skills are always improving on an upward trajectory and are never plateauing or regressing. It forces you to revisit old knowledge, practice new skills, and apply different thought processes to new situations.
Deliberate practice is often a habit that develops unconsciously, such as when you’re attending university or self-studying with a short timeframe in which to complete your studies. For example, I was subconsciously using deliberate practice while I was completing my technical diploma in software engineering. In a two-year program, you end up feeling overwhelmed and pushed outside of your comfort zone most of the time, which meant that I had no choice but to keep studying more and more advanced topics despite not being completely comfortable with the ones I had previously learned. Over that two-year period, I managed to grow my skillset exponentially and master some of the more challenging concepts in software engineering by using deliberate practice.
Deliberate practice is key for data scientists who seek to remain impactful and relevant throughout their careers. Not only that but deliberate practice is designed to help data scientists remain on top of their game years after they’ve had any formal education.
Say, for example, that you’re feeling unwell and head to the hospital. While there, you get checked out by a senior doctor and a junior doctor. Who do you think would give you a more accurate diagnosis? Believe it or not, the junior doctor would likely give you a more accurate diagnosis because they would be fresh out of medical school, an experience where students are constantly in a state of deliberate practice with no sense of a comfort zone. It’s been found that senior doctors have “no better (and in some cases worse)” diagnostic abilities than junior doctors due to their lack of continued active training.
What the example about medical professionals tells us is that when you aren’t pushed outside of your comfort zone, your diagnosis skills (or analysis skills in the case of data scientists) will plateau or even diminish over time.
In the case of a data scientist, not keeping up on alternative mathematical modeling or analysis methods could mean that you’re omitting important skill sets from your toolbox that could otherwise be used to conduct data analyses.
In a situation comparable to the doctor example above, a younger data scientist fresh from their studies may be able to more accurately pull alternative conclusions from data than a more experienced counterpart who has been doing the same kind of analysis for the last five years with little variation. Essentially, data scientists who continue to expand and grow their skills will be more able to spot patterns, errors, inconsistencies, and alternative conclusions than a data scientist whose abilities have stagnated. Not only that, but data scientists who continue to master challenging skills will have no ceiling for their advancement or career opportunities down the line.
Therefore, deliberate practice, which forces you to progress your learning and skills outside of your comfort zone, is your answer to becoming a better data scientist who will retain their impact and relevance throughout their career.
Since daily data science practice is a necessity for up-and-coming and current data scientists alike, deliberate practice is the easy answer to improving your skills on a daily basis, as well as mastering the most challenging concepts in data science.
Deliberate practice involves five steps that can be used to grow your data science skills and knowledge.
The five steps of deliberate practice:
- Identify an area of weakness to strengthen: This could be anything from programming to mathematics to data visualization and more. Identifying an area of weakness is a humbling activity but one that is necessary to begin the deliberate practice process — deliberate practice is best used on a weakness that is found outside of your comfort zone that you would like to master. Not only does choosing a weakness that fulfills these requirements set you up for steps 2 and 3 of the process, but it also ensures that you will have the motivation to see the process through. For this example, let’s say that you want to master more advanced topics in programming.
- Split up that weakness into specific areas for performance improvement: Deliberate practice requires well-defined targets, which means that you need to get specific about what weakness you’re trying to improve. As mentioned above for this example, you’re looking to master more advanced topics in programming. This is a broad weakness to try to improve. However, if you narrow it down, you could try to improve your programming style, your development of production-ready code, or improving your writing of object-oriented code. Each of these fall within the “programming” weakness, but are more specific areas on which to focus your deliberate practice.
- Set challenging goals that extend your abilities: The goal of deliberate practice is to always be uncomfortable. At no point during the process should your goals seem easy to attain. This means that the goals you set should require you to learn skills or knowledge that is just beyond your current ability level. To continue with our example, this could involve you mastering the fundamentals of object-oriented programming in two weeks. This would require you to cover the four principles of object-oriented programming (encapsulation, abstraction, inheritance, and polymorphism) across two weeks, followed by the creation of a small project at the end of the two-week term to determine whether you truly understand the concept.
- Seek honest feedback to determine whether your skills have improved: The success of deliberate practice rests on you being able to receive instantaneous feedback on your progress. Without instant feedback, you’ll have a hard time establishing which pattern produces the results you’re looking for and which pattern sends you in the wrong direction. The best learning experience I’ve ever had is when I’ve had the humility to try something out, ask for feedback, and then use the feedback to improve my work. Feedback is essential to help you establish patterns concerning what works and what sends you in the wrong direction. This is also a great time to ask a colleague, manager, or boss if they can provide additional suggestions that will help you master the concept you’ve chosen.
- Repeat: Advancing your data science skills and knowledge through deliberate practice is a process that must be continuously repeated to ensure significant improvements. Without repeating this cycle, a compounding effect won’t be achieved, which will result in your gains remaining marginal.
- Force yourself to complete some of your daily tasks using the skill you’re trying to improve through deliberate practice. For example, if you’re updating analysis code, try implementing object-oriented programming principles to clean up your work and make your program run more efficiently.
- Build a practice plan that has you conducting deliberate practice during a designated time of the day — not only does this help you establish a routine but it also helps save time that would otherwise be wasted while trying to figure out what learning resources or plan you’re going to use. For example, break deep learning down into five sections of knowledge that then get spread out across five weeks. Set a time for yourself every day to pick away at your section for the week to help develop a daily study habit.
- Tell someone (i.e., a boss, manager, or coworker) that you plan on showing them a skill that you’re currently learning — this provides the motivation necessary to put in the hard work required to deliberately learn something outside of your comfort zone. For example, tell your boss that you’ve figured out a way to automate part of your analysis work and tell them how it can be used to optimize other workflows.
- Don’t go it alone. Instead, try deliberate learning with a friend or colleague and set time aside each day to work on the skill together. For example, try pair-programming an AI program that relates to your analysis work.
- Learning skills outside of data science is beneficial to your career. Try deliberately practicing a new instrument, language, sport, or skill. Not only does this give your brain a much-deserved break in the data science department, but it also exercises your brain in other ways which may end up being useful for your data science work.