How to Become a Data Scientist? Here is a Proven-Framework

If you have made the decision to explore your options recently, chances are you have run into several questions. How to go about the learning process, where to start, how much to learn, what to learn, and most importantly, whether you can convert your effort into a successful career at the end of it all.

Tell me if this is you:

Don’t worry. You’re not alone. In 3 years of transitioning over 2500 careers to Data Science, we have seen learners experience all this, and more. The top question is this: What is the fastest way to learn Data Science?

STEP 1: Understand what Data Scientists REALLY do?

You would be hard-pressed to find a concrete definition of a Data Scientist anywhere. And really, definitions are not important. It is more critical to understand the role a Data Scientist plays within a company.

In any company, there are problems they want to solve: getting more customers, evaluating a new market, deciding on a big capital investment, reducing time-to-market, and so on. Companies want to use data to make an informed decision and light upon a solution to these problems. That is where you, as a Data Scientist, come in.

The form these steps take within an individual company will differ, but the essence remains the same. The bottom line is: you will be using Data Science to transform data into usable insights for your company.

STEP 2: Understand what employers want

Not surprisingly, employers want problem-solvers in their organizations. During an interview, an employer will look for certain traits that embody a good employee and a great Data Scientist. In a nutshell, these traits are:

The reason learners are unable to get jobs immediately after finishing their degrees is that the current education system doesn’t set up a learner for career success. Why is that?

STEP 3: Get the RIGHT recipe for success in Data Science

You’ve done your research; you’ve read articles, watched videos, attended conferences, spoken to Data Scientists. Each of these sources have their thought process. What you will find here is wisdom culled from all these sources, distilled into an actionable plan, and proven to work over time.

You need STRUCTURED learning: Online education is the best way to connect learners to knowledge, but it still requires the structure of a course. Self-paced doesn’t work and has proven to have consistent dropoff rates. You need a monthly plan to stay on track with your learning objectives, and see the outcomes at the end of the course.

You need a LEARNING PATH: There is so much content available, making a decision on what to do can be stressful and counterproductive: Python or R; which database to learn; is SQL important, etc. Curriculum designers will make learning decisions for you based on what the industry needs, and leave you free to learn without distractions.

You need industry MENTORS: Touchpoints are critical in the learning process. With mentors, you are guided throughout that process, with feedback and support when you need it. Mentors are also more effective than professors because they bring in experience from their every day work into the classroom.

You need RELEVANT content: The emphasis of a good Data Science course is on practising concepts in practical scenarios in order to prepare for application in the real world. Consider who has developed the course: academicians have expertise in learning delivery, and industry partners ensure that Data Science content is constantly reviewed and updated, ensuring that it is still relevant to and therefore always relevant.

You need an INTEGRATED learning environment: Distributed learning materials dilute focus, so there needs to be one environment where the whole learning process takes place: access lessons, practice applying concepts, get mentor guidance, and interact with peers for the synergy of group learning.

You need to PRACTICE: The leap from concepts to application should be undertaken at the learning stage. It is vital to have projects and applications throughout the learning curve. Start with mini projects, and steadily build up to real-life simulated projects on real datasets. Typical courses will have one big project at the end, which doesn’t help you build conceptual clarity nor a portfolio as you go along.

STEP 4: Prepare for career success

Right, you’ve finished your course. Where do you go from here? The easy answer is that you get a job. The path to that is not as easy as it sounds.

There is a huge demand for Data Scientists. You already know that, otherwise you wouldn’t be here. But there are lots of people doing Data Science, and yet the gap between demand and supply still exists. Once again, you need to understand how the industry looks for its talent, so you can be prepared.

Get your profiles ready: You need to present your skills in a format that recruiters can see. This comes in 2 parts:

Professional profiles: Learn which ones to set up, how to set them up, optimise them to communicate your abilities, and hit all the right notes.
Build a portfolio: A portfolio shows a recruiter your application abilities. Projects that you do in the course are an excellent starting point, and you should develop the habit of picking up more projects of your own.

Attend mock interviews: Experience is the best teacher, and going through the process with people who usually recruit, will give you insight on how you present yourself. You will find pointers on how to polish up speaking skills, and strike the right note between being knowledgeable and brash. You will get feedback on the points that recruiters look for in candidates, and will eventually learn to anticipate questions and be proactive.

Access to industry partners: A good course will have a solid placement process because industry partners have helped build the course, and therefore trust the process, in which they have been actively involved, to produce the talent they require.

Work on soft skills: Often ignored and severely underrated, soft skills are vital for success, not just to get a job, but to achieve success in your career and workplace over the long term.

Look beyond the course

Attending the right course will set you up for success, it should also expose you to the kinds of activities and habits that you must cultivate in order to stay successful.

Seize networking opportunities with people in the industry: Attend talks, participate in hackathons, and follow thought leaders to stay abreast of innovations in the field. This is an excellent way to keep your knowledge current.

Constantly upskill: Data Science is constantly evolving, and you should too. The skills you learn in any program are the foundation on which you have to build.

November 26, 2020

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