Careers in Data Science and Analytics: Five Tips to Strategically Work Your Way
“How did you become a data science and analytics professional?”
Early in 2020 (before the pandemic lockdown), I addressed this question to an audience of aspiring and experienced data science professionals at the first Women in Data Science (WiDS) Regional Conference in Puerto Rico.
Realizing that my journey of 10+ years in the field was not clearly defined from the beginning and it continues to evolve, I decided to share 5 useful tips for aspiring data science and analytics professionals — leveraging my experience as a data science practitioner and as manager of data science and analytics professionals.
If you’re curious about analytics and data science roles and career paths — please read on! If you’re not — do it anyway — Most of these tips are tailored specifically to the field but are career agnostic in principle. You may find these useful and extend to any area of expertise.
Tip #1 : Think of your career as a jungle gym
In Lean In, Facebook executive Sheryl Sandberg encourages readers to treat careers as jungle gyms, as opposed to the traditional ladder.
Focusing on my career as a jungle gym enabled me to craft my own career through:
- Application of analytics and data science tools in several areas including: marketing analytics, retail operations, image analytics, card portfolio analytics, and fraud analytics
- Exposure to perspectives and priorities across different business functions, and markets, while working with internal and external clients
- Building and expanding my professional network
Tip #2 : Make sure you know your strengths
In addition to great exposure to corporate software development, my first professional experience (a global investment bank in NYC) taught me that putting large amounts of effort and time would not suffice to succeed in my role as an applications developer.
This lesson has been reinforced over throughout the years as a technical contributor and manager of analytics and data science professionals… People are most successful in roles when their contributions are aligned with natural strengths and talents.
Some of the tools that proved useful in the past and I still use as of today to learn and focus on my strengths include:
- StrengthsFinder test and analysis of signature themes — I was lucky enough to find this jewel of a test early in my career and still use it as as a tool for professional growth.
- Journaling / keeping track of moments and activities that generate a sense of empowerment, flow, high productivity and / or connection to a greater, meaningful purpose
- Gathering candid and constructive feedback from professors, colleagues, managers, and mentors
Focusing on your strengths enables you to align career interests, roles and development areas to your natural talents, resulting in a more meaningful and satisfying journey, so it’s worth a try.
(While out of the scope for this post, it’s worth mentioning that the benefits of learning and identifying one’s strengths go far beyond building a great career in data science and analytics — it’s an exercise of self-discovery.)
Tip #3 : Integrate strengths, expertise and interests into your data science path
One of reasons I encourage people to pursue a career in analytics and data science field is the vast majority of industries, roles and career options available. Years ago, when I decided to pursue a role that leveraged data, science, math and programming to enable data driven business decision-making, there were no formal titles for analytics and data science positions. It took me quite some time and several hours of research to find roles that resonated with my interests.
Fortunately, times changed. There are several tools that can assist analytics and data science enthusiasts, including several Venn diagram of skills required to become a data science professional.
(After doing my research, I found myself adapting my own version below.)
It’s evident from the diagram above data science and analytics spans domain areas of technology, engineering, and design, math and science, as well as specific subject-matter expertise, and there are many options to work your way through the field as long as you have a strong foundation in one of the areas listed. Use the diagram above to align your background, interests, and strengths and preferences with potential analytics and data science roles. Is there a specific area of expertise you already have? Any areas you are interested in moving towards? The skills for a computer engineer interested in solving key business questions and delivering analytical products will not be the same as a Statistician with a PhD that wants to test top-notch algorithms on large datasets in real time.
Once you have a general idea of where you are and what you’re curious about, it’s time to get the search started. Use Google, LinkedIn, Indeed, Glassdoor, and other job search engines to find out more about these roles. Is there anything that connects to your interests? What background, skills and experiences are employers looking for?
Tip #4 : Set your action plan by identifying programs, coursework, or project topics to strengthen the skills for the chosen role
Once you narrowed down your quest for potential analytics and data science role(s), the next step is to research for programs / learning opportunities to help you attain it.
- Based on your prior experiences and interests and assessment on Tip #3, consider whether if pursuing a degree in Analytics and Data Science is necessary. Keep in mind, most professionals in the field work their way through one of the key domain areas; primarily through a degree in science, technology, engineering, or math.
- If you have a strong foundation in one of the 3 domain areas, consider an online Analytics or Data Science program, such as the ones offered by Coursera, DataCamp, and Edx
- Build your data science project portfolio through side projects. Find a problem you are passionate about at work, on in your personal life and identify ways to solve it using analytics and data science — there’s no shortage of topics on health / healthcare, social justice, finance, retail, and technology. Or consider enrolling in a Kaggle competition.
Tip #5 : The path to mastery in the analytics and data science field is one that requires practice and time. Treat your journey as a labor of love.
Analytics and data science roles are so popular and in high demand, and well paid by employers. Why? Because becoming a data scientist and excelling at it requires time, dedication, and practice, and being willing to be a student for the rest of your career (I will explain why in a future post).
Malcolm Gladwell covers in his book Outliers, it takes at least 10,000 hours to master a subject — and data science is not the exception. So while bootcamp programs can be great introductions, don’t fall for that promise to make you an expert in “no time”… You’ll be disappointed.
Fortunately, this won’t be a deal breaker because you already have your career research done and feel confident that this is something you want to pursue…
Some tips on building your way to mastery involve:
- Breaking down those goals into smaller, incremental steps with specific actions and time frames. If you’re not familiar with creating SMART goals yet or haven’t updated yours in some time, give it a try.
- Finding as much joy in the process as you do once you get to the end result
- Recognize that we all have great days and days where nothing clicks at all, but do it anyways — consistency is key to mastery
Was this useful? Share your questions and thoughts in the comments below.
Originally published at http://datajungle.net.