Puzzles are the crux of data science.
So argue authors of “Radical Uncertainty” John Kay and Mervyn King, who categorize all modern problems as resolvable uncertainty or radical uncertainty.
According to Kay and King, resolvable uncertainties are the puzzles that drive the daily work of data scientists across every vertical. Armed with a natural affinity for problem-solving, data scientists are in constant pursuit of sharper skills and more agile processes. This is true for CNA Data Scientist Hieu Nguyen, who has embraced the joy in his daily work.
“It’s fun for me to learn the principles of solving a problem and designing a solution through coding,” Nguyen told Built In. “And when I’m enjoying something, the motivation to improve is intrinsically there.”
While there is fun in Nguyen’s approach to learning, there is pragmatism too.
“Improving my technical coding helps me complete my day-to-day tasks more efficiently,” he said. “Each new skill set I learn allows me to more effectively query and transform data, build data pipelines, create data visualization and so much more.”
With each new skill set comes improved predictive analytics and statistical use of the data, Nguyen said. Built In Chicago sat down with Nguyen to learn more about the tools and resources he capitalizes on at CNA to level up his skills.
CNA is a U.S. commercial property and casualty insurance company that provides a broad range of insurance products and services for businesses and professionals in the United States, Canada and Europe.
As a data scientist, what has been your main motivation for improving your technical coding skills?
I’ve come to realize that having technical coding skills makes me an asset to the teams I support. Given my expertise in the subject, I’m able to contribute to a wider range of projects. The skills are transferable and help me make a direct, positive impact on CNA.
I was also looking to learn more about modular programming, top-down design, package development in Python, and how to write readable and reusable codes. Modular programming and top-down design relate specifically to package development, which helps to improve and streamline the data science team’s responsibilities each day.
Steps in a data science project — data prep, transform features, select features, train model and deploy model — are often repetitive, so it’s helpful to modularize pieces of code wherever we can. By making and sharing reusable codes, we’re not only able to streamline our workflow and process, but we’re also making it easier to collaborate with other data scientists, peer review code and check each other’s work.
I’m able to contribute to a wider range of projects. The skills are transferable and help me make a direct, positive impact on CNA.”
What steps have you taken to learn or hone those skills? Tell us about any courses, certifications, projects, etc. that you found particularly beneficial.
I’m someone who loves learning. To hone my skills, I make an effort to take advantage of any learning opportunity available. I’ve learned so much from my manager, who is always willing to review my work and open to sharing his feedback.
Another opportunity I take advantage of is our data science team’s “Package Friday” sessions. Each week, we come together to code, practice writing Python packages, sharing best practices and so on. The sessions allow me to gain hands-on experience in coding, which is how I learn best.
I also practiced writing reusable code modules that we use often in our team’s work streams. Reusable code makes work for my team easier down the line and writing it helps me build muscle memory.
At CNA, we have access to Udemy for Business, which is a platform that hosts training for a variety of topics — everything from programming and coding to leadership coaching. The platform has been especially beneficial in terms of coding and filling my knowledge gaps around topics of interest. When I want to gain a basic understanding of systems or learn how to write a batch script, for example, Udemy for Business is one of the first places I go to search for relevant training.
