Data Science for Decision Makers
Packt Publishing Limited (Verlag)
978-1-83763-729-4 (ISBN)
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 DescriptionAs 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 forThis 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.
Jon Howells is a seasoned AI and Data Science professional with a decade of experience in the field. He runs an AI consultancy called Qualifai and has worked with various companies, including Unilever, Permira and Capgemini, developing and deploying data science services and solutions. He holds a Master's degree in Computational Statistics & Machine Learning from UCL. Jon is particularly interested in the application of Large Language Models (LLMs) in consumer-focused businesses, such as using LLMs for consumer research and feedback analysis, personalized content generation, and enhanced customer support, ultimately helping businesses better understand and engage with their customers.
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
| Erscheinungsdatum | 16.11.2023 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Software Entwicklung ► User Interfaces (HCI) | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-83763-729-6 / 1837637296 |
| ISBN-13 | 978-1-83763-729-4 / 9781837637294 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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