Moving From Academia To Industry: 5 Challenges That Lie Ahead In Data Science
Data Science has gained massive popularity over the last five years. Given the surge of interest, it is no surprise that academicians and many teaching assistants or research assistants are looking to explore data science job roles in the industry. But in doing so there may be many apprehensions that might come into the mind — whether companies accept people with academic experience, what kind of experience or expertise is expected from them, or how should academics prepare for roles in the industry. While some believe that those who work in academia can as easily blend in industry roles, others face may face challenges fitting into the role. This article talks about various challenges that they might face and how to overcome them effectively. Making that first step in the industry: This could be the most crucial step to get started in an industry role in data science. While you may have made many acquaintances during your research stint, if there are no industry connections in your personal network, it might be a challenge to get the job. Many data science professionals who have transitioned from academia to industry believe that they wouldn’t have gotten the job if not for a strong personal connection in the industry. While it acts as a reference point, having an industry connect exposes one to industry practices and trends that you might need to teach yourself to quickly adapt to the changing environment. A shift in the work culture: There are many cultural differences when it comes to working in academia and industry. In academic settings, typically several months or years are spent working on the same problem and optimising it, whereas industry is focused on developing a quick solution that can be deployed by others. The basic focus of the work is different as the industry works at a much faster pace than academia. In industry, if analysis or experiment isn’t producing results quickly, you might need to drop it and start working on other solutions that produce results. Industry works on deliverables while academia on the freedom to experiment. There is also a shift in the culture in a way that academia is about working alone, whereas industry requires to work in a team and collaborate with people on projects. Decision-making tasks and interacting with the end user: Data Science job roles in the industry are not just working with the tools but a lot of back and forth with the end users. It requires data scientists to be able to interact with the end users after analyses and algorithms are complete and convince decision-makers to use the insight to drive decisions. In academia, your audience at most times are fellow research assistants who can understand the data science jargons but in industry, the presentations have to be tailored in a way that layman can understand it all. Industry data science roles may require you to work with the product team, marketing team, sales team and other non-technical colleagues. The need to pick up new skills: Talking about data scientists in industry, they are expected to have a wide range of skill sets ranging from running analyses in Excel to implementing neural networks. You may have to focus on a lot of complicated machine learning algorithms, data analysis and more, unlike academia where you might not have to know more beyond the statistical packages and programming languages that you already know. You may have to know commercial quality coding, write reusable code, keep the projects clean and readable, and more. The bottom line is that you may have to explore the skills and tools beyond the ones that you already know. Adapting to the competitiveness of the industry: The basic focus of academicians is on ideas rather than products, in fact, they like to get into the crux of the problem rather than the usual practice by the industry that focuses on developing solutions. Academicians might not come out to be competitive in seeing their work getting through completion and implementation, which might come as a challenge if you are looking to go into industry role. Companies like to see a portfolio of completed projects rather than half-finished ideas. The industry role requires you to be more competitive and not have a laid-back attitude.
Data Science has gained massive popularity over the last five years. Given the surge of interest, it is no surprise that academicians and many teaching assistants or research assistants are looking to explore data science job roles in the industry. But in doing so there may be many apprehensions that might come into the mind […]
Data Science has gained massive popularity over the last five years. Given the surge of interest, it is no surprise that academicians and many teaching
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["data science", "Go", "machine learning", "programming_languages:R", "AI", "neural network", "programming_languages:Go", "R"]
["AI", "machine learning", "neural network", "data science", "R", "Go", "programming_languages:R", "programming_languages:Go"]
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.