Book Image

Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen
Book Image

Practical Data Analysis Using Jupyter Notebook

By: Marc Wintjen

Overview of this book

Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
Table of Contents (18 chapters)
1
Section 1: Data Analysis Essentials
7
Section 2: Solutions for Data Discovery
12
Section 3: Working with Unstructured Big Data
16
Works Cited

Summary

Congratulations, you have now created your first DataFrame using the pandas library! We started the chapter by introducing you to the concepts of structured tabular data and the different techniques available to manipulate it by transposing and pivoting the data. More importantly, we discussed the importance of why data should be in tabular form. We then introduced the pandas library and defined a DataFrame, and demonstrated the many benefits of this powerful feature that are available for you during data analysis. In the handling of essential data formats, we went through the different data formats available by going through the details of the CSV, XML, and JSON file formats. Before we ended the chapter by creating our first DataFrame, we discussed the importance of data dictionaries and how different data types improve your data literacy, as well as why they are important before, during, and after the data analysis workflow has completed.

In the next chapter...