Overcome Imposter Syndrome In Data Science
Have you ever felt like you’re not good enough to be a data scientist? Like you’re just faking it, and sooner or later, someone will figure out that you don’t know what you’re doing?
If so, you’re not alone. Many data scientists suffer from imposter syndrome. Imposter syndrome is the belief that you’re not as competent as others think you are. Self-doubt and a fear of being exposed as a fraud exemplify imposter syndrome. Despite being brilliant and successful, people with imposter syndrome often feel like they don’t measure up to their own or others’ expectations.
If imposter syndrome sounds relatable, don’t worry. You can overcome it.
In this blog post, we’ll explore imposter syndrome in the context of data science. I’ll share my data science journey and how I combat feeling like an imposter along the way.
My journey to data science
I was nearing graduation from the University of Georgia MBA program in 2010. Taking a study break, I popped into a coffee shop where I ran into a friend putting the finishing touches on his thesis. His analysis of fish migration patterns and visuals using Python amazed me. I hadn’t heard of data science or Python but knew I was looking at something special.
However, my course was clear. I was headed to Corporate America to take the world of finance by storm. I had a bachelor’s degree in finance. I was about to complete my master’s in finance and level one of the chartered financial analyst designation.
My glimpse into the world of data science was a distant memory as the years of my fledgling career came and went — 9 to be exact.
In 2019, I worked in corporate FP&A (financial planning and analysis) doing routine forecasting, budgeting, and reporting in Excel 2010. One fateful day, I received a meeting invite from my CFO (chief financial officer). She informed me that I was chosen as one of two analysts to embed with Ernst & Young (EY) on a data science consulting engagement.
A flood of emotion washed over me. I was flattered and excited. But the strongest feeling was dread. I was accustomed to being a big fish in a little pond. I envisioned these consultants swooping in to do mystical, unheard-of analyses that left me in the dust. A challenge never deters me, so I knew this moment would alter my career trajectory.
EY analyzed and predicted employee churn. I made every effort to learn as much as possible, but unfortunately, my time on the project was limited. More importantly, though, I was inspired.
Seizing the moment, I joined forces with my teammate to craft a business plan for a data science department. We spent months preparing and pitching a better future to anyone who would listen. Our goal was to create a centralized, advanced analytics team specializing in machine learning. We believed it to be cheaper and better to develop those skills in-house rather than paying for expensive external consultants. We would need the right tools, data, and subject matter experts to execute internal consulting engagements.
After six months of pitching the business case, we were granted a 1.5M budget to stand up the data science department. We were given three directives: modernize the analytic tech stack, up-skill all analysts, and perform internal consulting engagements of strategic importance. We delivered on all three for the next two and a half years.
Crucible by Fire
So there, I found myself with a huge budget, a huge spotlight, and a huge career gamble. Dread and anxiety set in as I realized that I didn’t actually know much about data science. I met with my teammate at a coffee shop to plan for the future we had pitched, determined to do everything “right.” I mean, how often are you given a blank slate?
The dread, anxiety, and blank slate vanished as COVID broke out in the US two weeks later. Everyone scrambled to adapt as the world shut down. The future was now, and everything we were planning was out the window. We had to solve massive data and analytic issues with tremendous urgency. We had no time to feel stressed or overwhelmed, much less plan. We just had to execute around the clock. After getting a few free trials for analytic tools, we were off to the races, day and night.
Embarrassment of Riches
After surviving the COVID crucible, I dove headfirst into the world of data science. Every day felt like I was trying to outswim a tidal wave. I could only think or talk about data science. I woke up early and went to bed late, dedicating every moment to learning this new craft. I had to learn fast enough to avoid complete public failure.
In the beginning, I felt like a kid in a toy store. Every day was a new, fantastic game-changer. This brave new world stood in stark contrast to my prior five years of Excel 2010. I enthusiastically dove into every tool, technique, model, and analytic approach I encountered (Thanks, Medium!).
This embarrassment of riches gave way to feeling overwhelmed. Before long, I felt adrift in an ocean of possibilities. I foolishly tried to swim in every direction to “catch up” to every data science author or YouTuber. I fervently desired to emulate these intelligent and accomplished data scientists. I pushed myself harder, figuring the finish line was just over the horizon.
Imposter Syndrom Sets In
The more I learned, I realized how much I didn’t know. Each day I longed for more. I wanted to find the most cutting-edge techniques. I wanted to develop the most efficient pipelines. I wanted to create the most intuitive user experience. Nothing was good enough for me.
The more I learned, the more I internally disparaged my prior work.
Before long, I feared that others would see through me and share in my discontentment. I assumed that they would be just as critical of me as I was. I feared that what I thought was great work was amateurish to accomplished data scientists and that I just didn’t know any better. I imagined being patronized like a 4-year-old sharing a “painting” that s/he thought was a masterpiece.
Data science is an expansive field that attracts people from many disciplines. For example, I entered the profession as a financial analyst. Others may enter as engineers, researchers, or computer scientists. This phenomenon creates a rich tapestry of skill-sets but exacerbates imposter syndrome, leading to feeling behind or lesser. So how do we overcome these feelings?
My Five Approaches to Overcoming Imposter Syndrome
Seemingly daily, a new Python package promises to perform better analytics more efficiently. Analytic tools like Alteryx, Tableau, or DataRobot roll out a new release with even more functionality each quarter. From my experience, data science requires 5, 10, or 15 tools, so keeping up with the latest and greatest is daunting. Before long, you realize that learning just one coding language isn’t enough. Inevitably, data scientists need to know at least SQL and Git in addition to Python or R. To my dismay, I learned that different data warehouses have different SQL syntax. Once you take the gateway drug of Jupyter Notebook, you find out that some packages only work in other IDEs (integrated development environments).
That variety is enough to make your head spin, leaving you exhausted and overwhelmed. The tips below help keep my head above water.
1. Data science is an ever-expanding universe.
Whether this fact is a blessing or a curse is entirely up to the individual. I felt both the joy of a kid in a toy store and the dread of being adrift at sea. The field of data science didn’t change — I did. I have the agency to decide whether I feel excited by the possibilities or overwhelmed. Ultimately, data science is for lifetime learners. That fact isn’t changing anytime soon, so I needed to change my outlook.
2. I mustn’t be alone.
As I reflected on my experience, I realized that other analysts must experience similar feelings. If the data science universe is expanding for me, it must be expanding for everyone else. No one can “know” data science because it’s too extensive and ever-growing. It’s impossible to know it all, so everyone else is learning just like I am. Others may know more about data science than I do, but I surely know more about other topics than they do. Each of us is an expert in our own experience. We all have something to contribute and must work together to succeed. We’re not imposters; we’re data scientists.
3. Develop a path with foreseeable goals
The data science journey doesn’t have a finish line or a checkered flag. How do we avoid feeling aimless, overwhelmed, or demotivated? My approach is to set waypoints on the journey. I crafted a one-year learning path with defined goals. First, I set a goal of achieving a few analytic tool certifications. Along with setting specific targets, the certifications also provided a learning structure. Next, I desired to learn Python. After trying a few approaches, I realized that Kaggle machine learning competitions worked best for me, so I set goals to complete my submissions by specific dates. Working towards these goals gave me structure and a defined target. I celebrated progress along with the accomplishment. Rather than feeling further behind as the universe expanded, I felt pride as I progressed toward these waypoints.
4. Don’t hide behind your ego.
Defeat imposter syndrome by being humble, transparent, and willing to grow. Learning a new skill, showcasing it to peers, and having your livelihood at stake can make you feel vulnerable. The ego can create a wall to hide behind in response to this vulnerability. Unfortunately, this barrier of ego only perpetuates imposter syndrome. The more you hide, the more you’re afraid of being discovered. The wall keeps you from working with and learning from others. Humbly accept that you don’t know everything and ask for help when needed. Always express a willingness to grow and develop. Take opportunities to share what you know with others. Indeed, you know things they don’t, just like they know things you don’t.
5. Be your best friend.
Data science is hard for beginners and experts alike. The journey never ends as an endless supply of challenges come your way. The journey is hard enough without beating yourself up along the way for not being better, smarter, faster, more talented, or more diligent. This detrimental mental state is ultimately counterproductive and risks your data science journey entirely. Instead, sustain yourself with kindness. Celebrate your wins. Have patience during trials and tribulations. Grant yourself grace for setbacks. Go easy on yourself, and you’ll come out on top.
Looking Ahead
I’m two and a half years into my data science journey, and I’m looking forward to the next 30. Self-doubt and fear crop up occasionally, but I won’t let them steal the joy of that kid in the toy store. Data science is a splendid profession and craft, so long as we let it be. It’s something to celebrate, not dread. I hope my experience offers some help on your journey. Please let me know what has worked for you. We’re all in this together and should learn from each other!
