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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

Summary

    Congratulations, we have now learned how to use key features of the numpy library along with some practical real-world examples. We started by learning about arrays and why they are important by providing examples of how they have been rooted in computer science and programming languages for decades. We also learned about the foundation of structured data, which uses the concepts of arrays, by explaining the differences between single and multiple dimensional arrays and how we commonly identify them as tables with columns and rows.

    Once the history and theories were explained, we learned how to make a NumPy array and walked through some useful functions available. We ended this chapter with a practical real-world example by loading stock prices into an array to show how it can answer specific questions by using a few NumPy commands available for data analysis. Data literacy skills were re-enforced throughout this chapter by understanding why data types impact data...

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Practical Data Analysis Using Jupyter Notebook
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