Becoming a Data Head
How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
Alex J. Gutman, Jordan Goldmeier
- English
- ePUB (adapté aux mobiles)
- Disponible sur iOS et Android
Becoming a Data Head
How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
Alex J. Gutman, Jordan Goldmeier
Ă propos de ce livre
"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful."
Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage
You've heard the hype around dataânow 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, andeminently readable book. Anyone can become a Data Headâan 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.
Foire aux questions
Informations
PART I
Thinking Like a Data Head
- Chapter 1: What Is the Problem?
- Chapter 2: What Is Data?
- Chapter 3: Prepare to Think Statistically
CHAPTER 1
What Is the Problem?
âA problem well stated is a problem half solved.ââCharles Kettering, inventor & engineer
QUESTIONS A DATA HEAD SHOULD ASK
- Why is this problem important?
- Who does this problem affect?
- What if we don't have the right data?
- When is the project over?
- What if we don't like the results?
Why Is This Problem Important?
- What keeps you (us) up at night?
- Why does this matter?
- Is this a new problem, or has it been solved already?
- What is the size of the prize? (What's the return on investment?)
- Methodology focus: In this trope, companies simply think trying some new analysis method or technology will set them apart. You've heard this marketing fluff before: âIf you're not using Artificial Intelligence (AI), you're already behind ⊠.â Or, companies find some other buzzword they would like to incorporate (e.g., âsentiment analysisâ).
- Deliverable focus: Some projects go off track because companies focus too much on what the deliverable will be. They say the project needs to have an interactive dashboard, for example. You start the project, but the outcome becomes about the installation of the new dashboard or business intelligence system. Project teams need to take a step back and trace how what they want to build brings value to the organization.