Python for Data Science For Dummies (eBook)

John Paul Mueller, Luca Massaron (Autoren)

eBook Download: EPUB

2023 | 3. Auflage
464 Seiten
For Dummies (Verlag)
978-1-394-21309-2 (ISBN)

Infos

Let Python do the heavy lifting for you as you analyze large datasets

Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner's guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples.

• Get a firm background in the basics of Python coding for data analysis
• Learn about data science careers you can pursue with Python coding skills
• Integrate data analysis with multimedia and graphics
• Manage and organize data with cloud-based relational databases

Python careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

John Paul Mueller is a freelance author and technical editor who has written 124 books on topics ranging like networking, home security, database management, and heads-down programming. Luca Massaron is a data scientist specialized in solving real-world problems with AI, machine learning, and algorithms. He is also a Kaggle Grandmaster and a Google Developer Expert.

Inhalt

Introduction 1

Part 1: Getting Started with Data Science and Python 7

Chapter 1: Discovering the Match between Data Science and Python 9

Chapter 2: Introducing Python's Capabilities and Wonders 21

Chapter 3: Setting Up Python for Data Science 33

Chapter 4: Working with Google Colab 49

Part 2: Getting Your Hands Dirty with Data 71

Chapter 5: Working with Jupyter Notebook 73

Chapter 6: Working with Real Data 83

Chapter 7: Processing Your Data 105

Chapter 8: Reshaping Data 131

Chapter 9: Putting What You Know into Action 143

Part 3: Visualizing Information 157

Chapter 10: Getting a Crash Course in Matplotlib 159

Chapter 11: Visualizing the Data 177

Part 4: Wrangling Data 199

Chapter 12: Stretching Python's Capabilities 201

Chapter 13: Exploring Data Analysis 223

Chapter 14: Reducing Dimensionality 251

Chapter 15: Clustering 273

Chapter 16: Detecting Outliers in Data 291

Part 5: Learning from Data 305

Chapter 17: Exploring Four Simple and Effective Algorithms 307

Chapter 18: Performing Cross-Validation, Selection, and Optimization 327

Chapter 19: Increasing Complexity with Linear and Nonlinear Tricks 351

Chapter 20: Understanding the Power of the Many 391

Part 6: The Part of Tens 413

Chapter 21: Ten Essential Data Resources 415

Chapter 22: Ten Data Challenges You Should Take 421

Index 431

Textauszug

Introduction

The growth of the internet has been phenomenal. According to Internet World Stats (https://www.internetworldstats.com/emarketing.htm), 69 percent of the world is now connected in some way to the internet, including developing countries. North America has the highest penetration rate 93.4 percent, which means you now have access to nearly everyone just by knowing how to manipulate data. Data science turns this huge amount of data into capabilities that you use absolutely every day to perform an amazing array of tasks or to obtain services from someone else.

You’ve probably used data science in ways that you never expected. For example, when you used your favorite search engine this morning to look for something, it made suggestions on alternative search terms. Those terms are supplied by data science. When you went to the doctor last week and discovered that the lump you found wasn’t cancer, the doctor likely made the prognosis with the help of data science.

In fact, you may work with data science every day and not even know it. Even though many of the purposes of data science elude attention, you have probably become more aware of the data you generate, and with that awareness comes a desire for control over aspects of your life, such as when and where to shop, or whether to have someone perform the task for you. In addition to all its other uses, data science enables you to add that level of control that you, like many people, are looking for today.

Python for Data Science For Dummies, 3rd Edition not only gets you started using data science to perform a wealth of practical tasks but also helps you realize just how many places data science is used. By knowing how to answer data science problems and where to employ data science, you gain a significant advantage over everyone else, increasing your chances at promotion or that new job you really want.

About This Book

The main purpose of Python for Data Science For Dummies, 3rd Edition, is to take the scare factor out of data science by showing you that data science is not only really interesting but also quite doable using Python. You may assume that you need to be a computer science genius to perform the complex tasks normally associated with data science, but that’s far from the truth. Python comes with a host of useful libraries that do all the heavy lifting for you in the background. You don’t even realize how much is going on, and you don’t need to care. All you really need to know is that you want to perform specific tasks, and Python makes these tasks quite accessible.

Part of the emphasis of this book is on using the right tools. You start with either Jupyter Notebook (on desktop systems) or Google Colab (on the web) — two tools that take the sting out of working with Python. The code you place in Jupyter Notebook or Google Colab is presentation quality, and you can mix a number of presentation elements right there in your document. It’s not really like using a traditional development environment at all.

You also discover some interesting techniques in this book. For example, you can create plots of all your data science experiments using Matplotlib, and this book gives you all the details for doing that. This book also spends considerable time showing you available resources (such as packages) and how you can use Scikit-learn to perform some very interesting calculations. Many people would like to know how to perform handwriting recognition, and if you’re one of them, you can use this book to get a leg up on the process.

Of course, you may still be worried about the whole programming environment issue, and this book doesn’t leave you in the dark there, either. At the beginning, you find complete methods you need to get started with data science using Jupyter Notebook or Google Colab. The emphasis is on getting you up and running as quickly as possible, and to make examples straightforward and simple so that the code doesn’t become a stumbling block to learning.

This third edition of the book provides you with updated examples using Python 3.x so that you’re using the most modern version of Python while reading. In addition, you find a stronger emphasis on making examples simpler, but also making the environment more inclusive by adding material on deep learning. More important, this edition of the book contains updated datasets that better demonstrate how data science works today. This edition of the book also touches on modern concerns, such as removing personally identifiable information and enhancing data security. Consequently, you get a lot more out of this edition of the book as a result of the input provided by thousands of readers before you.

To make absorbing the concepts even easier, this book uses the following conventions:

• Text that you’re meant to type just as it appears in the book is in bold. The exception is when you’re working through a list of steps: Because each step is bold, the text to type is not bold.
• When you see words in italics as part of a typing sequence, you need to replace that value with something that works for you. For example, if you see “Type Your Name and press Enter,” you need to replace Your Name with your actual name.
• Web addresses and programming code appear in monofont. If you're reading a digital version of this book on a device connected to the internet, note that you can click the web address to visit that website, like this: http://www.dummies.com.
• When you need to type command sequences, you see them separated by a special arrow, like this: File  ⇒    New File. In this example, you go to the File menu first and then select the New File entry on that menu.

Foolish Assumptions

You may find it difficult to believe that we've assumed anything about you — after all, we haven’t even met you yet! Although most assumptions are indeed foolish, we made these assumptions to provide a starting point for the book.

You need to be familiar with the platform you want to use because the book doesn’t offer any guidance in this regard. (Chapter 3 does, however, provide Anaconda installation instructions, which supports Jupyter Notebook, and Chapter 4 gets you started with Google Colab.) To provide you with maximum information about Python concerning how it applies to data science, this book doesn’t discuss any platform-specific issues. You really do need to know how to install applications, use applications, and generally work with your chosen platform before you begin working with this book.

You must know how to work with Python. This edition of the book no longer contains a Python primer because you can find a wealth of tutorials online (see https://www.w3schools.com/python/ and https://www.tutorialspoint.com/python/ as examples).

This book isn’t a math primer. Yes, you do encounter some complex math, but the emphasis is on helping you use Python and data science to perform analysis tasks rather than teaching math theory. Chapters 1 and 2 give you a better understanding of precisely what you need to know to use this book successfully.

This book also assumes that you can access items on the internet. Sprinkled throughout are numerous references to online material that will enhance your learning experience. However, these added sources are useful only if you actually find and use them.

Icons Used in This Book

As you read this book, you come across icons in the margins, and here’s what those icons mean:

Tips are nice because they help you save time or perform some task without a lot of extra work. The tips in this book are time-saving techniques or pointers to resources that you should try in order to get the maximum benefit from Python or in performing data science–related tasks.

We don’t want to sound like angry parents or some kind of maniacs, but you should avoid doing anything that’s marked with a Warning icon. Otherwise, you may find that your application fails to work as expected, or you get incorrect answers from seemingly bulletproof equations, or (in the worst-case scenario) you lose data.

Whenever you see this icon, think advanced tip or technique. You may find that you don’t need these tidbits of useful information, or they could contain the solution you need to get a program running. Skip these bits of information whenever you like.

If you don’t get anything else out of a particular chapter or section, remember the material marked by this icon. This text usually contains an essential process or a morsel of information that you must know to work with Python or to perform data science–related tasks successfully.

Beyond the Book

This book isn’t the end of your Python or data science experience — it’s really just the beginning. We provide online content to make this book more flexible and better able to meet your needs. That way, as we receive email from you, we can address questions and tell you how updates to either Python or its associated add-ons affect book content. In fact, you gain access to all these cool additions:

• Cheat sheet: You remember using crib notes in school to make a better mark on a test, don’t you? You do?...

Produkt-Details

• Erscheint lt. Verlag: 3.10.2023
• Sprache: englisch
• Themenwelt: Informatik ► Programmiersprachen / -werkzeuge ► Python
• Schlagworte: Computer Science • Datenverarbeitung • Informatik • Programmierung • Programmierung u. Software-Entwicklung • Programming & Software Development • Python (Programmiersprache)
• ISBN-10: 1-394-21309-3 / 1394213093
• ISBN-13: 978-1-394-21309-2 / 9781394213092