Author Spotlight
The Key to Success in Industry? Learn to Communicate Results Effectively
For Vicky Yu, a handful of key skills made the transition from finance to programming to data analytics smoother.
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share Vicky Yu’s conversation with Ben Huberman.
Vicky has worked as a data engineer, data scientist, and most recently a data analyst. She started writing to discuss her experiences and provide career advice she found lacking during her data journey.
What was your journey into data analytics like?
I went into the data science field by chance. I was a data engineer in a consulting company. One of my projects involved working with the data science team in an e-commerce company to develop an analytics database to become the primary source of training data. The team appreciated my work on the project and when a data scientist opening came up, they offered me the position, even though I had no prior data science experience.
After a few years working as a data scientist I felt I wasn’t learning anything new and decided to try a different data role and ended up getting a position as a data analyst.
How does the data analyst role fit within the typical division between data scientists and data engineers?
In companies that have a mature analytics infrastructure, there are clear responsibilities for data analysts, data scientists, and data engineers. The data engineer brings in the data needed for analysis and modeling. Data analysts use the data to surface trends and insights, while data scientists build models to segment users and make predictions about the future.
Companies starting to build out their analytics infrastructure may view a data scientist as a hybrid role. They may hire a data scientist when they primarily need a data analyst to find insights and build dashboards, or when they actually need a data engineer to build ETL (extract, transform, load) pipelines first before building models. A data analyst working in this environment may act as a data analyst/data scientist to generate insights, evaluate A/B tests, build dashboards, and develop models as needed.
What were the main challenges in navigating this landscape, where role definitions can be so fluid?
I started my corporate career as a financial analyst but I accepted a programming job without any academic training or prior experience. My new employer offered me the job with the confidence that I would eventually learn the skills needed. I struggled in the beginning because learning programming is completely different than finance. My mindset was to view a failed attempt as learning an approach that didn’t work. If I wasn’t able to solve a problem after a few times, I revised my approach and tried again. Over time, I learned how to debug my programming errors faster because I figured out all the ways that didn’t work.
The pivot from finance to programming helped me learn early on how to adapt and acquire new skills needed for completely different roles. My switch from data engineer to data scientist and then to data analyst was not without its challenges, but it was made easier because of what I learned early on in my career.
What should early-career professionals in data-related fields do to set themselves on the path towards success?
Learn early in your career how to communicate data results effectively. Stakeholders can’t appreciate your contributions if you don’t explain them in a way they can understand. My tried-and-true formula is to present the problem, show the data results, offer a solution, and show potential benefits if applicable.
What kinds of data-analysis projects do you find yourself gravitating towards these days?
I like to work on data-analysis projects I don’t have a clear idea how to solve. For example, I was asked to calculate lifetime value for a new user, taking into account ad revenue and subscription revenue if the user converted to a paying member. This had never been done before and I had no idea how to approach this problem. The project challenged me to come up with new ways to calculate lifetime value and helped me develop new methodologies I’ve been able to apply to other projects.
Alongside your data analytics work, you’re also a prolific author. What inspired you to start writing publicly?
I wouldn’t have been able to learn as much as I did about data science and have my questions answered without others writing about what they experienced and how they solved their problems. Writing is my way of giving back to the community that I’ve benefited from in my data journey. I also wanted to provide advice for others to help them avoid mistakes I made early on.
Many of your posts focus on the gap between data science as it’s taught in courses and universities, and how it actually plays out in real-life, industry contexts. Why is it important for you to talk about these topics?
Academia teaches students how to do something, but doesn’t necessarily show them how to apply those teachings in real-life situations. For example, a student learns the principles of A/B testing and how to measure results. In real life, the data analyst or data scientist needs to make sure the experiment is set up correctly and validate the data before measuring results. Bad data isn’t expected in school—it’s an example of a gap I feel is important to point out for real-life applications.
Looking into the future, what kinds of change do you hope to see within the broader data science community?
I hope to see a community of mentors helping junior data scientists and analysts grow in their careers. There are many resources to help people pass technical interviews, and some boot camps offer mentors as part of the program. But there are few resources to help data scientists after they get their first job. Managers aren’t necessarily the best mentors for junior data scientists, and it would be great for them to have a community to turn to for career advice.
Curious to learn more about Vicky’s work and projects? Follow her on Medium and LinkedIn. Here are some of our favorites from Vicky’s deep archive of posts on Towards Data Science, where she shares insights on topics like data storytelling and career advice, among others.
- Data analytics careers: for a taste of Vicky’s writing on the data analyst role, start with the basics: “How Do I Become a Data Analyst?” answers some of the most common questions you might have. “My Experience Changing Careers to Become a Data Analyst” covers the topic of transitioning to this new career path, while “What’s Next If You Can’t Get a Data Science Job?” suggests that data-analytics positions might be a good fit for some data scientists even if the job title would have you believe otherwise. For a taste of the hiring process, check out recent posts where Vicky goes “Behind the Scenes of a Data Analyst Recruiting Process” and explains “How to Become an Amazing Data Analytics Hire.”
- Data storytelling: translating data into actionable insights is, for Vicky, the key to success in any data-focused role—and she’s written extensively on how to approach this sometimes-tricky challenge. For a broad overview, there’s no better place to start than “How to Tell a Story With Data.” When you’re ready to share your findings, you’ll want to read Vicky’s advice on creating and delivering successful data presentations. And for even more specialized, hands-on advice, read her posts about translating machine-learning results into business impact and presenting them for non-technical stakeholders.
Stay tuned for our next featured author, coming soon. If you have suggestions for people you’d like to see in this space, drop us a note in the comments!
