Project Management in Data Science
Tom is Director Data & Analytics at Interone in Hamburg, Germany.
Request to reuse this
Add to my favorites
Topics: Business Intelligence, Estimating, Requirements Management, Strategy
In 2012, Thomas H. Davenport and D.J. Patil published the article “Data Scientist: The Sexiest Job of the 21st Century” in the Harvard Business Review. Since then, almost no presentation about the benefits of data was delivered without the use of this article’s title as a quote. And with more and more data being collected and computers becoming cheaper and faster every day, obviously a new league of data professionals is required.
In the best of all worlds, these data scientists know how to process huge amounts of data (also called “Big Data”)—sometimes in real-time systems, no matter how complex the data is—and make sense of it. They are fluent in several computer programming languages, experts in data mining and machine learning, PhD statisticians and senior enough to present the meaning of data to C-levels.
In reality, there are probably very few data scientists that fulfill all these requirements. Most often, data scientists are strong in some of these fields, but not all of them. What is more, data science is just a new term for a combination of long-existing fields.
However, the most important skill that is not mentioned in the data science world is project management. In fact, many authors in the data science field suggest project management techniques—probably without being aware of it, and most often without …
Please log in or sign up below to read the rest of the article.
