Preface
Welcome, and thank you for taking the time to read this book. Throughout this book, I will take you on a journey through the evolution of data analysis in a very simple and easy-to-understand manner. The book will introduce you to modern tools, such as Jupyter Notebook and various Python libraries, to teach you how to work with data. In the process, you will learn about the many different types of data and how to clean, blend, visualize, and analyze data to gain useful insights.
Data literacy is the ability to read, work with, analyze, and argue with data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines both concepts by sharing proven techniques and hands-on examples, so you can learn how to communicate effectively with data.
Complete with hands-on tutorials and real-world examples, this easy-to-follow guide will teach you concepts of data analysis using SQL, Python, and Jupyter Notebook.
Who this book is for
This book is for anyone who is looking to develop their skills to become data-driven, personally and professionally. No prior knowledge of data analysis or programming is required to get started with this book. Anyone looking for a new career working with data will enjoy reading the book.
What this book covers
Chapter 1, Fundamentals of Data Analysis, is a straightforward introduction to what data analysis is and how a blend of traditional and modern techniques can be used for data analysis.
Chapter 2,Overview of Python and Installing Jupyter Notebook, provides an introduction to the Python programming language using an open source data analysis tool called Jupyter Notebook.
Chapter 3,Getting Started with NumPy, is where you will learn about the key functions used for analysis with a powerful Python library named NumPy; You will also explore arrays and matrix data structures.
Chapter 4,Creating Your First pandas DataFrame, teaches you what a pandas DataFrame is and how to create them from different file type sources, such as CSV, JSON, and XML.
Chapter 5, Gathering and Loading Data in Python, shows you how to run SQL SELECT queries from Jupyter Notebook and how to load them into DataFrames.
Chapter 6,Visualizing and Working with Time Series Data, explores the process of making your first data visualization by breaking down the anatomy of a chart. Basic statistics, data lineage, and metadata (data about data) will be explained.
Chapter 7,Exploring, Cleaning, Refining, and Blending Datasets, focuses on essential concepts and numerical skills required to wrangle, sort, and explore a dataset.
Chapter 8,Understanding Joins, Relationships, and Aggregates, delves into constructing high-quality datasets for further analysis. The concepts of joining and summarizing data will be introduced.
Chapter 9,Plotting, Visualization, and Storytelling, continues to teach you how to visualize data by exploring additional chart options, such as histograms and scatterplots, to advance your data literacy and analysis skills.
Chapter 10,Exploring Text Data and Unstructured Data, introduces Natural Language Processing (NLP), which has become a must-have skill in data analysis. This chapter looks at the concepts you'll need to know in order to analyze narrative free text that can provide insights from unstructured data.
Chapter 11,Practical Sentiment Analysis, covers the basics of supervised machine learning. After that, there's a walk-through of sentiment analysis.
Chapter 12,Bringing It All Together, brings together many of the concepts covered in the book using real-world examples to demonstrate the skills needed to read, work with, analyze, and argue with data.
To get the most out of this book
This book is for anyone who is absolutely new to the field of data analysis. No prior knowledge or experience of working with data or programming is required. The book is a step-by-step guide that walks you through installations and exercises.
Only basic technical acumen is required. The ability to download files, access websites, and install applications on your computer is all that is needed in this regard.
If you are using the digital version of this book, we advise you to type the code yourself or access the code via the GitHub repository (link available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.
Download the example code files
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The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Practical-Data-Analysis-using-Jupyter-Notebook. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available athttps://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781838826031_ColorImages.pdf.
Conventions used
There are a number of text conventions used throughout this book.
CodeInText:Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles.Here is an example:"So, purchase_data.iloc[0] or purchase_data.ix[0] will both return the same results."
A block of code is set as follows:
product_data = {
'product a': [13, 20, 0, 10],
'project b': [10, 30, 17, 20],
'project c': [6, 9, 10, 0]
}
Any command-line input or output is written as follows:
>cd \
>cd projects
>jupyter notebook
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Depending on the OS, such as Linux, a CSV would only include a line feed (LF) and not a carriage return (CR) for each row."
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