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Practical Data Analysis Using Jupyter Notebook

Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen
3.9 (9)
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Practical Data Analysis Using Jupyter Notebook

Practical Data Analysis Using Jupyter Notebook

3.9 (9)
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)
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1
Section 1: Data Analysis Essentials
7
Section 2: Solutions for Data Discovery
12
Section 3: Working with Unstructured Big Data
16
Works Cited
Practical Sentiment Analysis

This is going to be a fun chapter. In this chapter, we will explore and demonstrate some practical examples of using Natural Language Processing (NLP) concepts to understand how unstructured text can be turned into insights. In Chapter 10, Exploring Text Data and Unstructured Data, we explored the Natural Language Toolkit (NLTK) library and some fundamental features of working with identifying words, phrases, and sentences. In that process of tokenizing, we learned how to work with data and classify text, but did not go beyond that. In this chapter, we will learn about sentiment analysis, which predicts the underlying tone of text that's input into an algorithm. We will break down the elements that make up an NLP model and the packages used for sentiment analysis before walking through an example together.

In this chapter, we will cover the following topics:

  • Why sentiment...

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