Data Scientist. It’s currently one of the most attractive jobs in business. In fact, Forbes named it the best job in the United States in 2019. With an immense buzz around the Data Science domain, it’s no surprise that so many are interested in getting their hands dirty for themselves.
But where should you start? Data Science is a very broad, incredibly diverse field with people coming into it from all different backgrounds and professions. This diversity can leave the path to becoming a Data Scientist unclear and makes it difficult to find a starting point that’s appropriate to your unique background.
As someone who has spent most of their professional career in some-type of Operational or Project Management role, I also struggled with finding an appropriate starting point. However, through a considerable amount of trial and error, I’ve found success in several principles that can push you along to becoming a rock-star Data Scientist, whether you’re a few years “fresh” out of college like myself or simply looking for a career change.
Recognize Three Abilities of a Stand-Out Data Scientist
With the field becoming increasingly popular, a greater competition is presented amongst Data Scientists. To rise to the top, there are three distinct areas of expertise you should develop to ensure you’re providing the most value:
First is developing a deep understanding the fundamental concepts and practices of the domain. Having this foundation allows you to evaluate the business problem at-hand and develop an analytics roadmap leading to an optimized solution.
Second is being able to tactically complete the required work, or more simply, serving as a do-er. These two roles complement each other by presenting an opportunity to deeply understand and master Data Science concepts through practice.
Third, and most important, is acting as a trusted advisor to clients. Of course, understanding and completing the work is critical for success, but effectively communicating results (or going from deep in the weeds to high-level) sets apart average from stand-out Data Scientists.
Identify Your Professional Strengths and Weaknesses
Maybe you struggle with the methodologies of Data Science but have experience in writing Python. Maybe the opposite: Concepts and communicating results comes naturally, but you currently lack the hard skills required to independently execute the work. Regardless of the situation, identifying your professional strengths and weaknesses will uncover those gaps.
Self-assessments serve as a suitable starting point in the skill-identification process. However, seeking feedback (and sometimes, constructive criticism) from a respected colleague or peer is truly invaluable and can be pivotal towards your growth.
From doing the above, you receive a greater awareness of both the hard and soft skills you possess from an internal and external perspective. You also gain an understanding of the capabilities in which need improvement, and consequently, can better direct your focus as it pertains to upcoming learning efforts.
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Find the Right Mentor
Finding your perfect “match” can be difficult, but a good mentor is invaluable in every stage of professional development. They are passionate about helping others and knowledgeable in the domain. They were also once in your shoes and can provide insight that only life experience can bring.
Because your journeys are closely related, you’ll gain a wealth of information solely through their personal experiences – approaches that helped them achieve their end goal, challenges that were faced and lessons learned, etc. Advantageous to you, this information can then be applied to your own practices as your mentor continues to support as your career develops.
But while experience and previous encounters are helpful to hear, there are also many other useful reasons to seek a mentor. To highlight a couple more: They sincerely want to see you succeed and will ensure you do so through providing motivation and assistance as you navigate through your practice. Additionally, they can introduce you to other educated colleagues within the field and ultimately help build your network.
And, by chance, if you’re still not convinced that a mentor is necessary, here are some statistics around “Mentoring for Career Development” provided by Guider AI:
- 87% of mentors and mentees feel empowered by their mentoring relationships and have developed greater confidence
- 89% of those with mentors believe their colleagues value their work, compared with 75% who do not have mentor
- Mentees are promoted 5 times more often than those without mentors
Become Involved in Data Science Groups
Everyone’s schedule, situation and preference differ, but it’s vitally important to develop your network. There are two different options for taking part in Data Science groups – virtually and physically – and depending on your preference, you may even decide to participate in a combination of both.
From the virtual perspective, LinkedIn offers countless groups to choose from based on where your interest lies within the Data Science domain – some cover Data Science broadly, while others may address topics within a specific area, such as Natural Language Processing or Neural Networks. Other social platforms including Reddit also extend an open dialogue to converse with others and ask questions.
If you’re instead looking to pursue physical gatherings, a simple Google search (e.g. “Data Science Groups Atlanta”) should do the trick. As an added tip, once you’ve found a group, you’re interested in joining, research whether a Meetup account has been created for it. Meetup.com and its mobile application is a simple way to remain up to date on the group’s meetings and upcoming events in your local area.
Learning Never Stops
University programs often adapt their curriculum based on industry trends and needs from top businesses. Because of this, obtaining a formal degree is certainly a viable way to learn relevant skills the industry is seeking. However, there are also numerous other resources that can be leveraged for continuing education.
Websites like Coursera and Udemy offer both free and low-cost courses on many Data Science topics ranging anywhere from Data Mining to Regression and everything in-between. These courses usually include practice assignments and quizzes to help reinforce the concepts explained within the readings and videos. Upon completion, some courses award a certificate which creates additional credibility towards your Data Science skillset.
Dive into real-world problems as regularly as possible. Often, there’s no better way to learn than through being thrown into the fire. Data Science groups are constantly organizing events (e.g. Hack-a-thons) and real-world consulting-like volunteer initiatives which allow all interested individuals, regardless of experience or professional background, to participate. You become exposed to new technologies and face new challenges which might require the use of unfamiliar solutions.
Finally, don’t ignore the data problems and opportunities to learn that might be right in front of you. Your current company may be open to you doing exploratory work and helping you on your journey. If you’re as fortunate as I, working for a company that strongly encourages and supports individual career development, like Aspirent, you may have the opportunity in front of you already.
Stepping into a new role can be overwhelming and putting on the shoes of a Data Scientist is no different. However, having the right mindset and understanding the resources available to you can make all the difference. Dedicate the time and put forth the effort — remember why you started your journey — and you, too, will find yourself landing that role you were dreaming about.
