Job Skills How can I transition from academia to become a data scientist in the industry?

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Transitioning from academia to a data science role requires building a practical portfolio and enhancing technical skills in R, Python, SQL, and big data concepts like Spark. Engaging in projects, contributing to open-source on GitHub, and participating in Kaggle competitions can demonstrate practical knowledge and problem-solving abilities. It's crucial to effectively communicate relevant academic experiences on a resume, emphasizing transferable skills and project outcomes. MOOCs can be beneficial for self-learning, but practical experience and a strong portfolio are more impactful for job applications. Overall, focus on showcasing your ability to work with data and articulate your process clearly to potential employers.
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atyy said:
At least for supervised learning, I think the only differences from traditional statistics are that the number of parameters is larger, and optimization is nonconvex.

From what I understand, there has been much fertile research within statistics on working in domains of large number of parameters, dimensionality reduction, and sparse signal detection (e.g. recent research on higher criticism -- see the following link: https://arxiv.org/pdf/1411.1437.pdf)

Researchers working in machine learning also work on many of these same problems, so increasingly there is a blurring of disciplines between the machine learning and statistics communities. As a matter of fact, it is not uncommon for researchers to be cross-listed between the statistics and computer science departments (where such departments exist separately). My alma mater, for example, have 3 faculty members cross-listed as such.
 

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