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Data Science for Decision Makers

You're reading from   Data Science for Decision Makers Enhance your leadership skills with data science and AI expertise

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781837637294
Length 270 pages
Edition 1st Edition
Languages
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Author (1):
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Jon Howells Jon Howells
Author Profile Icon Jon Howells
Jon Howells
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Understanding Data Science and Its Foundations
2. Chapter 1: Introducing Data Science FREE CHAPTER 3. Chapter 2: Characterizing and Collecting Data 4. Chapter 3: Exploratory Data Analysis 5. Chapter 4: The Significance of Significance 6. Chapter 5: Understanding Regression 7. Part 2: Machine Learning – Concepts, Applications, and Pitfalls
8. Chapter 6: Introducing Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Interpreting and Evaluating Machine Learning Models 12. Chapter 10: Common Pitfalls in Machine Learning 13. Part 3: Leading Successful Data Science Projects and Teams
14. Chapter 11: The Structure of a Data Science Project 15. Chapter 12: The Data Science Team 16. Chapter 13: Managing the Data Science Team 17. Chapter 14: Continuing Your Journey as a Data Science Leader 18. Index 19. Other Books You May Enjoy

Assembling your data science team – key roles and considerations

Building a successful data science team requires a carefully curated mix of roles and skills. There are many roles that have evolved since companies started to introduce data science, machine learning, and artificial intelligence into their business processes and products.

There are now many different, sometimes overlapping roles, not all of which are essential to every team.

Here’s a breakdown of the roles to consider when assembling your team, along with insights from leading tech companies.

Data scientists

Data scientists are at the core of many data science teams, focusing on framing business problems as machine learning tasks, developing predictive models, and extracting valuable insights from data.

Responsibilities

  • Frame business problems as machine learning tasks
  • Develop predictive models
  • Extract valuable insights from data

Skills and knowledge

  • Strong...
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