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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

Interpreting and Evaluating Machine Learning Models

The promise and potential of machine learning systems to create systems that can make decisions without the need for hardcoded rules or heuristics is huge. However, this promise is often far from straightforward to fulfil, and in developing machine learning models or leading teams who develop machine learning models, great care needs to be taken to ensure their accuracy and reliability.

In this chapter, we will explore how to interpret and evaluate different machine learning models.

This is one of, if not the most important skill you can have in your toolkit as a decision-maker working on data science projects.

While it can be convenient to allow data scientists to evaluate their own models and “mark their own homework,” this is a risky decision to make and will, invariably, eventually lead to problems.

This chapter covers the following topics:

  • How do I know whether this model will be accurate?
  • ...
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Data Science for Decision Makers
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