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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

Explaining data aggregation

Data aggregation is part of your daily life and you may or may not even realize it. When you pull up a review of a restaurant that uses one to five stars or if you purchase an item on Amazon because it has thousands of customer ratings, both examples are data aggregates. A data aggregate can be defined as a summary typically based on a significantly larger detail. In SQL, an aggregation is when a groupby command is applied against one or more tables, which includes a statistical calculation such as sum, average, min, or max against one or more fields.

Understanding the granularity of data

The aggregation of calculations would be known as the measure. When you are grouping by one or more fields to get their distinct values, they are classified as dimensions.

So, this should all sound very familiar because we introduced the concept of dimensions and measures in both Chapter 5, Gathering and Loading Data in Python...

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