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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

Discovering real-world datasets

Throughout this book, I have emphasized that the power of analytics comes from blending data together from multiple sources. An individual data source alone rarely includes all the fields required to answer key questions. For example, if you have a timestamp field but not a geographic field about a user, you can't answer any questions about the data related to where an event took place.

As a good data analyst, always offer up creative solutions that have filled data gaps or offer a different perspective by including an external data source. Finding new data sources is much easier today than ever before. Let's go over a few examples.

Data.gov

Data.gov is managed by the United States General Services Administration, which offers hundreds of thousands of datasets regarding various topics at the State and Federal levels. Most are curated from specific agencies and posted for public use. They are open source with limited...

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