Crossing the Bridge: A Comparison of Data Science in Academia and Industry – Towards Data Science

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Nazlı Alagöz
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Towards Data Science
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As a current Ph.D. student who decided to leave academia for the industry, I’ve devoted substantial time to exploring how my academic experience could translate into an industry setting. Initially, the two domains seemed to stand at opposite ends of the spectrum. However, after extensive research and discussions with many who’ve traversed the path from academia to industry, I found more parallels than I anticipated. In this blog post, I’ll share my insights about the differences, and commonalities between academic and industrial data science.
I should note that lacking firsthand industry experience, my comparisons between industry and academia rely on insights from others who have traversed this path. Additionally, I am a Ph.D. candidate in quantitative marketing and thus more familiar with academic research in economics and business. So, when I make comparisons I compare industry practices to the academic research process in these areas.
For those unfamiliar with the process of academic research and the application of data science within this context, I define data science as the process of driving insights from data using scientific methods and algorithms. For example, I mainly use causal inference and machine learning methods to answer the research questions in my dissertation projects. I will next provide an overview of academic research in quantitative marketing.
In academia, we need to find relevant questions that warrant answers (e.g., how do paywalls affect revenue, how do certain campaigns affect sales). We then gather the data required to answer these questions (e.g., by collaborating with an online newspaper, web scraping, using APIs, or procuring data). Once we have the data, we can start the process of preparing the data for analysis and use the data to test our hypotheses. Once we have some initial results, we communicate these through presentations and draft papers to get feedback. We update the analyses, presentations, and papers to address the feedback received. This process (i.e., get feedback, address the feedback) repeats until we…
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Towards Data Science
PhD candidate in Quantitative Marketing. I write on causal inference, data science and work related topics. www.linkedin.com/in/nazli-m-alagoz
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