Types of Data Scientists, Part 2

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Last week I looked at the Harris et al. report Analyzing the Analyzers, which describes four types of data scientists: Data Businessperson, Data Creative, Data Developer and Data Researcher. This week I want to conclude this two-part mini-series by describing where I see myself fitting in within these categories.

I am most interested in continue to develop the necessary skills to become a data researcher. I’m most interested in becoming a data researcher because the focus is on having a breadth of knowledge about programming, operations research/math, business, machine learning, programming, business and statistics. I have taken one course to date that included an introduction to machine learning, one course that focused on data visualization, and am developing my domain expertise on how to develop data products that create business value. I think some of these roles might be combined in the future especially in small companies that would allow me to put on multiple hats at some point in the data mining and/or product development process. At this point in my formal data science education, I am least interested in becoming a data developer because I lack the passion about programming on a daily basis.

In my undergraduate engineering education and in my professional experience, I have thrived at research. Even as a young child, I was passionate about asking a question and finding the information to answer that question. In my graduate program, I am working toward developing the skills Harris describes for the data researcher: Science (experimental design, technical writing), spatial statistics (GIS), structured data (SQL, JSON, XML), big data (Hadoop), machine learning, unstructured data (noSQL, R for data manipulation), visualization and product development (design, project management).

My personality type of ENFJ makes me naturally curious about how information can transform organizations and processes. My personality type also means that I would best enjoy a forward-thinking and people-centered work environment with a clear humanitarian mission and emphasis on using the data to create positive change. I easily get bored with routine tasks and like the variety and challenge of data science as a constantly evolving field. I think I will enjoy this field because like Overton in his book Going Pro in Data Science, I think that ‘the future of data science …means putting a hypothesis out in a public forum, writing openly with collaborators from other companies and holding open peer reviews (48).

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