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Data Science for Decision Makers

Data Science for Decision Makers

By : Howells
4.8 (5)
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Data Science for Decision Makers

Data Science for Decision Makers

4.8 (5)
By: Howells

Overview of this book

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.
Table of Contents (20 chapters)
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1
Part 1: Understanding Data Science and Its Foundations
7
Part 2: Machine Learning – Concepts, Applications, and Pitfalls
13
Part 3: Leading Successful Data Science Projects and Teams

Supervised Machine Learning

Within machine learning, supervised learning is one of the most used and most useful subfields. It is often the first area students learn within machine learning and what people think of when first hearing about machine learning, as it involves learning on annotated or labeled data, similar to how we learn from correct examples.

The applications of supervised machine learning are wide and varied. From the spam detection on your email inbox, through to recommendation systems used when recommending TV shows and movies on your favorite streaming service, through to the call you get from your bank when its systems believe they may have detected fraudulent transactions, these are all applications of supervised machine learning.

In this chapter, we will discuss in more detail the steps involved in training and deploying supervised machine learning models, some of the core supervised machine learning models, factors to consider when training and evaluating...

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Data Science for Decision Makers
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