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Data Science for Decision Makers
Data Science for Decision Makers

Data Science for Decision Makers: Enhance your leadership skills with data science and AI expertise

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Data Science for Decision Makers

Introducing Data Science

Data science is not a new term; in fact, it was coined in the 1960s by Peter Naur, a Danish computer science pioneer who used the term data science to describe the process of working with data in various fields, including mathematics, statistics, and computer science.

Later, the modern use of data science began to take shape in the 1990s and early 2000s, and data scientist, as a profession, became more and more common across different industries.

With the exponential rise in artificial intelligence, one may think that data science is less relevant.

However, the scientific approach to understanding data, which defines data science, is the bedrock upon which successful machine learning and artificial intelligence-based solutions can be built.

Within this book, we will explore these different terms, provide a solid foundation in core statistical and machine learning theory, and concepts that can be applied to statistical, machine learning and artificial intelligence-based models alike, and walk through how to lead data science teams and projects to successful outcomes.

This first chapter introduces the reader to how statistics and data science are intertwined, and some fundamental concepts in statistics which can help you in working with data.

We will explore the differences between data science, artificial intelligence, and machine learning, explain the relationship between statistics and data science, explain the concepts of descriptive and inferential statistics, as well as probability, and basic methods to understand the shape (distribution) of data.

While some readers may find this chapter covering basic, foundational knowledge, the aim is to provide all readers, especially those from less technical backgrounds, with a solid understanding of these concepts before diving deeper into the world of data science. For more experienced readers, this chapter serves as a quick refresher and helps establish a common language that will be used throughout the book.

In this next section, let's look at these terms of data science, artificial intelligence, and machine learning, how they are related, and how they differ.

This chapter covers the following topics:

  • Data science, AI, and ML – what’s the difference?
  • Statistics and data science
  • Descriptive and inferential statistics
  • Probability
  • Describing our samples
  • Probability distributions
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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 26, 2024
Length: 270 pages
Edition : 1st
Language : English
ISBN-13 : 9781837638345
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Product Details

Publication date : Jul 26, 2024
Length: 270 pages
Edition : 1st
Language : English
ISBN-13 : 9781837638345
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Concepts :

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Table of Contents

19 Chapters
Part 1: Understanding Data Science and Its Foundations Chevron down icon Chevron up icon
Chapter 1: Introducing Data Science Chevron down icon Chevron up icon
Chapter 2: Characterizing and Collecting Data Chevron down icon Chevron up icon
Chapter 3: Exploratory Data Analysis Chevron down icon Chevron up icon
Chapter 4: The Significance of Significance Chevron down icon Chevron up icon
Chapter 5: Understanding Regression Chevron down icon Chevron up icon
Part 2: Machine Learning – Concepts, Applications, and Pitfalls Chevron down icon Chevron up icon
Chapter 6: Introducing Machine Learning Chevron down icon Chevron up icon
Chapter 7: Supervised Machine Learning Chevron down icon Chevron up icon
Chapter 8: Unsupervised Machine Learning Chevron down icon Chevron up icon
Chapter 9: Interpreting and Evaluating Machine Learning Models Chevron down icon Chevron up icon
Chapter 10: Common Pitfalls in Machine Learning Chevron down icon Chevron up icon
Part 3: Leading Successful Data Science Projects and Teams Chevron down icon Chevron up icon
Chapter 11: The Structure of a Data Science Project Chevron down icon Chevron up icon
Chapter 12: The Data Science Team Chevron down icon Chevron up icon
Chapter 13: Managing the Data Science Team Chevron down icon Chevron up icon
Chapter 14: Continuing Your Journey as a Data Science Leader Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(5 Ratings)
5 star 80%
4 star 20%
3 star 0%
2 star 0%
1 star 0%
Steven Fernandes Aug 06, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book provides a clear and practical overview of key concepts in data science and machine learning. The book begins with a foundational understanding of how to interpret common statistical measures and make informed decisions based on data. It then covers a range of machine learning methodologies, including supervised, unsupervised, and reinforcement learning. Readers will learn how to evaluate both statistical and machine learning models effectively and understand the full data science lifecycle, from development to deployment. The guide also offers insights into choosing between ML, statistical modeling, and traditional BI methods and includes valuable advice on managing data teams and projects. A great resource for aspiring data scientists and analytics managers.
Amazon Verified review Amazon
Sai Kumar Bysani Oct 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐦𝐲 𝐤𝐞𝐲 𝐭𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬:1. 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: The book introduces key data science concepts like exploratory data analysis (EDA) and feature engineering. It simplifies understanding of these critical steps in any machine learning pipeline.2. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: From regression to clustering, the book breaks down both supervised and unsupervised learning algorithms. It also covers important aspects like hyperparameter tuning and model optimization.3. 𝐀𝐯𝐨𝐢𝐝𝐢𝐧𝐠 𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬: The book highlights key pitfalls such as overfitting, bias, and issues related to data quality. By addressing challenges like model generalization and data consistency, it ensures that machine learning models are robust and reliable.4. 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬: The book provides detailed guidance on evaluating models using metrics like accuracy, precision, recall, and AUC-ROC curves. It also explains how to interpret these results to make informed decisions.5. 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬: It covers essential statistical concepts such as hypothesis testing, p-values, and confidence intervals. These are critical for making data-driven decisions and evaluating models.6. 𝐕𝐢𝐬𝐮𝐚𝐥𝐥𝐲 𝐄𝐧𝐠𝐚𝐠𝐢𝐧𝐠 𝐏𝐢𝐜𝐭𝐮𝐫𝐞𝐬: The book uses clear visuals like charts, graphs, and confusion matrices to break down complex topics. These images make it easier to understand the material and apply it practically.Additionally, the book includes practical case studies such as fraud detection, churn prediction, forecasting, and many more. These real-world examples help illustrate how data science techniques are applied to solve complex business problems. I had a great time reading this book!
Amazon Verified review Amazon
Om S Aug 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Stepping into a leadership role, "Data Science for Decision Makers" guided me through the complexities of data science and AI. It covers everything from collecting and analyzing data to understanding machine learning concepts. With practical examples, I learned to interpret models and make informed decisions. The book also provides tools for managing data science projects and teams, making it an invaluable resource for executives looking to leverage data for impactful results.
Amazon Verified review Amazon
Kog Aug 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This has something for everyone from Data Science world—whether you're an executive, a manager, a consultant, an AI expert, a Data scientist, a researcher or just someone curious about how data can shape business strategy and strategic decisions! 📚#DataScience #DecisionMaking #BusinessStrategy #Leadership #ContinuousLearning
Amazon Verified review Amazon
Amazon Customer Sep 10, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book excels in its mission to elevate leadership skills by demystifying the core concepts of data science and AI. It offers a clear and practical guide for understanding and utilizing data to drive business value. The author break down intricate topics into accessible explanations, making complex concepts like statistical quantities, machine learning techniques, and model evaluation comprehensible for those without a deep technical background.The book equips leaders with the tools to make informed decisions about when to deploy ML, statistical models, or traditional business intelligence methods.In summary, this book is an invaluable resource for executives and emerging leaders in data science.It provides a thorough grounding in essential data science concepts while offering actionable insights and practical strategies for applying this knowledge in a business context.
Amazon Verified review Amazon
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