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Thomas Nield - Essential Math for Data Science

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Thomas Nield Essential Math for Data Science
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Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way youll also gain practical insights into the state of data science and how to use those insights to maximize your career.Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

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Praise for Essential Math for Data Science

In the cacophony that is the current data science education landscape, this book stands out as a resource with many clear, practical examples of the fundamentals of what it takes to understand and build with data. By explaining the basics, this book allows the reader to navigate any data science work with a sturdy mental framework of its building blocks.

Vicki Boykis, Senior Machine Learning Engineer at Tumblr

Data science is built on linear algebra, probability theory, and calculus. Thomas Nield expertly guides us through all of those topicsand moreto build a solid foundation for understanding the mathematics of data science.

Mike X Cohen, sincXpress

As data scientists, we use sophisticated models and algorithms daily. This book swiftly demystifies the math behind them, so they are easier to grasp and implement.

Siddharth Yadav, freelance data scientist

I wish I had access to this book earlier! Thomas Nield does such an amazing job breaking down complex math topics in a digestible and engaging way. A refreshing approach to both math and data scienceseamlessly explaining fundamental math concepts and their immediate applications in machine learning. This book is a must-read for all aspiring data scientists .

Tatiana Ediger, freelance data scientist and course developer and instructor

Essential Math for Data Science

by Thomas Nield

Copyright 2022 Thomas Nield. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

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  • Cover Designer: Karen Montgomery
  • Illustrator: Kate Dullea
  • June 2022: First Edition
Revision History for the First Edition
  • 2022-05-26: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781098102937 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Essential Math for Data Science, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-098-10293-7

[LSI]

Preface

In the past 10 years or so, there has been a growing interest in applying math and statistics to our everyday work and lives. Why is that? Does it have to do with the accelerated interest in data science, which Harvard Business Review called the Sexiest Job of the 21st Century? Or is it the promise of machine learning and artificial intelligence changing our lives? Is it because news headlines are inundated with studies, polls, and research findings but unsure how to scrutinize such claims? Or is it the promise of self-driving cars and robots automating jobs in the near future?

I will make the argument that the disciplines of math and statistics have captured mainstream interest because of the growing availability of data, and we need math, statistics, and machine learning to make sense of it. Yes, we do have scientific tools, machine learning, and other automations that call to us like sirens. We blindly trust these black boxes, devices, and softwares; we do not understand them but we use them anyway.

While it is easy to believe computers are smarter than we are (and this idea is frequently marketed), the reality cannot be more the opposite. This disconnect can be precarious on so many levels. Do you really want an algorithm or AI performing criminal sentencing or driving a vehicle, but nobody including the developer can explain why it came to a specific decision? Explainability is the next frontier of statistical computing and AI. This can begin only when we open up the black box and uncover the math.

You may also ask how can a developer not know how their own algorithm works? We will talk about that in the second half of the book when we discuss machine learning techniques and emphasize why we need to understand the math behind the black boxes we build.

To another point, the reason data is being collected on a massive scale is largely due to connected devices and their presence in our everyday lives. We no longer solely use the internet on a desktop or laptop computer. We now take it with us in our smartphones, cars, and household devices. This has subtly enabled a transition over the past two decades. Data has now evolved from an operational tool to something that is collected and analyzed for less-defined objectives. A smartwatch is constantly collecting data on our heart rate, breathing, walking distance, and other markers. Then it uploads that data to a cloud to be analyzed alongside other users. Our driving habits are being collected by computerized cars and being used by manufacturers to collect data and enable self-driving vehicles. Even smart toothbrushes are finding their way into drugstores, which track brushing habits and store that data in a cloud. Whether smart toothbrush data is useful and essential is another discussion!

All of this data collection is permeating every corner of our lives. It can be overwhelming, and a whole book can be written on privacy concerns and ethics. But this availability of data also creates opportunities to leverage math and statistics in new ways and create more exposure outside academic environments. We can learn more about the human experience, improve product design and application, and optimize commercial strategies. If you understand the ideas presented in this book, you will be able to unlock the value held in our data-hoarding infrastructure. This does not imply that data and statistical tools are a silver bullet to solve all the worlds problems, but they have given us new tools that we can use. Sometimes it is just as valuable to recognize certain data projects as rabbit holes and realize efforts are better spent elsewhere.

This growing availability of data has made way for data science and machine learning to become in-demand professions. We define essential math as an exposure to probability, linear algebra, statistics, and machine learning. If you are seeking a career in data science, machine learning, or engineering, these topics are necessary. I will throw in just enough college math, calculus, and statistics necessary to better understand what goes in the black box libraries you will encounter.

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