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
Fundamentals of Data Analysis

Welcome and thank you for reading my book. I'm excited to share my passion for data and I hope to provide the resources and insights to fast-track your journey into data analysis. My goal is to educate, mentor, and coach you throughout this book on the techniques used to become a top-notch data analyst. During this process, you will get hands-on experience using the latest open source technologies available such as Jupyter Notebook and Python. We will stay within that technology ecosystem throughout this book to avoid confusion. However, you can be confident the concepts and skills learned are transferable across open source and vendor solutions with a focus on all things data.

In this chapter, we will cover the following:

  • The evolution of data analysis and why it is important
  • What makes a good data analyst?
  • Understanding data types and why they are important
  • Data classifications and data attributes explained
  • Understanding data literacy