Cooper - Data science from scratch: the #1 data science guide for everything a data scientist needs to know: Python, linear algebra, statistics, coding, applications, neural networks, and decision trees

Data Science from Scratch

The #1 Data Science Guide for Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees

Steven Cooper

Copyright 2018 Steven Cooper

All rights reserved.

No part of this guide may be reproduced in any form without permission in writing from the publisher except in the case of review.

Legal & Disclaimer

The following document is reproduced below with the goal of providing information that is as accurate and reliable as possible.

This declaration is deemed fair and valid by both the American Bar Association and the Committee of Publishers Association and is legally binding throughout the United States.

Furthermore, the transmission, duplication or reproduction of any of the following work including specific information will be considered an illegal act irrespective of if it is done electronically or in print. This extends to creating a secondary or tertiary copy of the work or a recorded copy and is only allowed with an express written consent from the Publisher. All additional right reserved.

The information in the following pages is broadly considered to be a truthful and accurate account of facts, and as such any inattention, use or misuse of the information in question by the reader will render any resulting actions solely under their purview. There are no scenarios in which the publisher or the original author of this work can be in any fashion deemed liable for any hardship or damages that may befall them after undertaking information described herein.

Additionally, the information in the following pages is intended only for informational purposes and should thus be thought of as universal. As befitting its nature, it is presented without assurance regarding its prolonged validity or interim quality. Trademarks that are mentioned are done without written consent and can in no way be considered an endorsement from the trademark holder.

Preface

T he main goal of this book is to help people take the best actionable steps possible towards a career in data science. The need for data scientists is growing exponentially as the internet, and online services continue to expand.

Book Objectives

This book will help you:

Know more about the fundamental principles of data science and what you need to become a skilled data scientist.

Have an elementary grasp of data science concepts and tools that will make this work easier to do.

Have achieved a technical background in data science and appreciate its power.

Target Users

The book is designed for a variety of target audiences. The most suitable users would include:

• Newbies in computer science techniques

• Professionals in software applications development and social sciences

• Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way

• Students and academicians, especially those focusing on data science and software development

Is this book for me?

This book is for those who are interested in data science. There are a lot of skills that a data scientist needs, such as coding, intellectual mindset, eagerness to make new discoveries, and much more.

Its important that you are interested in this because you are obsessed with this kind of work. Your driving force should not be money. If it is, then this book is not for you.

Introduction

D ata is all around us, in everything that we do. Data science is the thing that makes human beings what they are today. Im not talking about the computer-driven data science that this book is going to introduce you to, but our brains ability to see different connections, learn from previous experiences and come to conclusions from facts. This is truer for humans than any other species that have lived on the planet. We humans depend on our brains to survive. Humans have used all of these features to earn out spot in nature. This strategy has worked for all of us for centuries, and I doubt we will be changing anything any time soon.

But the brain is only able to take us so far when we are faced with raw computing. The humans cant keep up with all of the data that we are able to capture. Therefore, we end up turning to machines to do some of the work: to notice the patterns, come up with connections, and to give the answers to many different questions.

Our constant quest for knowledge is ingrained in our genes. Using computers to do some of the work for us is not, but it is where we are destined to go.

Welcome to the amazing world of data science. While you were looking over the table of contents, you may have noticed the wide variety of topics that is going to be covered in this book. The goal for Data Science from Scratch is to give you enough information about every little section of data science to help you get started. Data science it a big field, so big that it would take thousands of pages to give you every bit of information that makes up data science.

In each chapter, we will cover a different aspect of data science that is interesting.

I sincerely hope that the information in this book will act as a doorway for you into the amazing world of data science.

Roadmap

Chapter one will give you a basic rundown of what data science is. It will go into the importance, the history, and the reasons data science matters so much.

Chapter two will go into everything that you need for data science. This will include the work ethics that are needed to make sure you are successful.

Chapter three will cover the advantages of data science. You will see the reason why so many people love data science.

Chapter four will cover how data science differs from big data, and how the two work together.

Chapter five will go into what a data scientist is and what they do. It will also cover the skills that a person needs to be a good data scientist. Its important for a data scientist to be inquisitive, ask questions, and make new discoveries.

Chapter six will go into the reasons why a data scientist should be familiar with hacking.

Chapter seven will cover the why data scientists need to know how to code. You will also learn about the most common programming languages that data scientists use.