Data Science for Decision Makers - by Jon Howells (Paperback)

Book Synopsis

Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results

Key Features:

- Master the concepts of statistics and ML to interpret models and guide decisions

- Identify valuable AI use cases and manage data science projects from start to finish

- Empower top data science teams to solve complex problems and build AI products

- Purchase of the print Kindle book includes a free PDF eBook

Book Description:

As data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI.

This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements.

By the end of this book, you'll be able to characterize the data within your organization and frame business problems as data science problems.

What You Will Learn:

- Discover how to interpret common statistical quantities and make data-driven decisions

- Explore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learning

- Find out how to evaluate statistical and machine learning models

- Understand the data science lifecycle, from development to monitoring of models in production

- Know when to use ML, statistical modeling, or traditional BI methods

- Manage data teams and data science projects effectively

Who this book is for:

This book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.

Table of Contents

- Introducing Data Science

- Characterizing and Collecting Data

- Exploratory Data Analysis

- The Significance of Significance

- Understanding Regression

- Introducing Machine Learning

- Supervised Machine Learning

- Unsupervised Machine Learning

- Interpreting and Evaluating Machine Learning Models

- Common Pitfalls in Machine Learning

- The Structure of a Data Science Project

- The Data Science Team

- Managing the Data Science Team

- Continuing Your Journey as a Data Science Leader