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Becoming a Data Head

Becoming a Data Head

How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning


Course details

0∙0 Ratings0 reviews

Published
May 2021
Publisher
Wiley
Chapters
16

About the course

You've heard the hype around data—now get the facts.

In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it.

You'll learn how to:

  • Think statistically and understand the role variation plays in your life and decision making
  • Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace
  • Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence
  • Avoid common pitfalls when working with and interpreting data

Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

Authors

Alex J. Gutman

Alex J. Gutman

Alex J. Gutman, PhD, is a Data Scientist, Corporate Trainer, and Accredited Professional Statistician. His professional focus is on statistical and machine learning and he has extensive experience working as a Data Scientist for the Department of Defense and two Fortune 50 companies.
Jordan Goldmeier

Jordan Goldmeier

Jordan Goldmeier is a Data Scientist, author, speaker, and community leader. He is a seven-time recipient of the Microsoft Most Valuable Professional Award and he has taught analytics to members of the Pentagon and Fortune 500 companies.

Course Outline

Chapter 1: What Is the Problem?
Chapter 2: What Is Data?
Chapter 3: Prepare to Think Statistically
Chapter 4: Argue with the Data
Chapter 5: Explore the Data
Chapter 6: Examine the Probabilities
Chapter 7: Challenge the Statistics
Chapter 8: Search for Hidden Groups
Chapter 9: Understand the Regression Model
Chapter 10: Understand the Classification Model
Chapter 11: Understand Text Analytics
Chapter 12: Conceptualize Deep Learning
Chapter 13: Watch Out for Pitfalls
Chapter 14: Know the People and Personalities
Chapter 15: What's Next?
Chapter 16: Index

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Course details

0∙0 Ratings0 reviews

Published
May 2021
Publisher
Wiley
Chapters
16

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