Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning
by Alex J. Gutman, Jordan Goldmeier
- Publisher
- Wiley
- Year
- 2021
- Pages
- 272
About this book
"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 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.
You'll be redirected to Amazon to complete your purchase.
You might also like
Fluent Python: Clear, Concise, and Effective Programming
Luciano Ramalho
Don't waste time bending Python to fit patterns you've learned in other languages. Python's simplicity lets you become productive quickly, but often this means you aren't using everything the language has to offer. With the updated edition of this hands-on guide, you'll learn how to write effective, modern Python 3 code by leveraging its best ideas.
AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with Gpus, Cuda, and Pytorch
Chris Fregly
Elevate your AI system performance capabilities with this definitive guide to maximizing efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering provides engineers, researchers, and developers with a hands-on set of actionable optimization strategies. Learn to co-optimize hardware, software, and algorithms to build resilient, scalable, and cost-effective AI systems that excel in both training and inference. Authored by Chris Fregly, a performance-focused engineering and product leader, this resource transforms complex AI systems into streamlined, high-impact AI solutions.
View details →
Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron
The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.