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Data Scientists

Agile Data Science: What Does It Mean? How to Manage Data Science Projects

Agile Data Science: What Does It Mean? How to Manage Data Science Projects

Originally published here Does it even work? “What does agile data science mean?” you might be asking. In one word: agile! Agile is a methodology that has been embraced by many industries, including data science. It’s time to get agile with your data science projects and start increasing efficiency

Do You Need To Manage a Team of Data Scientists? — Understanding Control Theory

Do You Need To Manage a Team of Data Scientists? — Understanding Control Theory Might Change Everything

Originally published here How I would use it to manage junior, mid-level, and senior data scientist Data Scientist is a highly sought-after position in the world of business. There are many who want to enter this field, and there are also those who want to stay in it for as

The AMO theory: Solving the Black Box Problem for Data Scientists

The AMO theory: Solving the Black Box Problem for Data Scientists

Originally published here When you have to explain how and why people behave in a certain way In data science, we often want to test input or stimulus and see if that will have an impact on the outcome. But the black box problem sits in between to explain why

4 Reasons Why Every Data Scientist Should Study Organizational Psychology

4 Reasons Why Every Data Scientist Should Study Organizational Psychology

Originally published here Because we often model people’s behavior AND we still have to work with people Understanding the human elements and how to work with them are important aspects of data science. The field of organizational psychology has given us many insights into human behavior, motivations, fears, and

3 Critical Aspects of Effective Stakeholder Management For Data Science Projects

3 Critical Aspects of Effective Stakeholder Management For Data Science Projects

Originally published here Can’t forget that we still have to work with people What does it take to do data science successfully? Data, of course. But what else? Great tools and software packages can get you only so far in the quest for analytical insights. In addition to having