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

Preparing to work with unstructured data

Today, we are living in a digital age where data is entangled into our lives in ways not technically possible or even imaginable before. From social media to mobile to the Internet of Things (IoT), humanity is living in what is commonly known as the information age. This age is where an exponentially growing of data about you is available to you instantaneously anywhere in the world. What has made this possible has been a combination of people and technology, including contributions from the Evolution of Data Analysis, which was introduced in Chapter 1, Fundamentals of Data Analysis.

It is commonly predicted by multiple sources that 80 percent of all of the data created around the world will be unstructured over the next few years. If you recall from Chapter 1,Fundamentals of Data Analysis., unstructured data is commonly defined as information that does not offer uniformity and pre-defined organization. Examples of unstructured...