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.
