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

Summary statistics and outliers

We touched on the necessity of fundamental statistics when working with data in Chapter 5, Gathering and Loading Data in Python.Let's walk through the differences between mean, median, and mode in statistics as it applies to data analysis. The mean or average is when you sum the values of numeric values in a series divided by the count of those same numbers. The mean or average is a measure in analytics and is typically used to gauge performance over a period of time and define a comparison for each period of time.

For example, you see average daily temperatures all the time in the news—how is that calculated? Depending on your geographic location, the weather will have the temperature recorded in specific increments, such as hours. The National Oceanic and Atmospheric Administration (NOAA), for example, uses stations and a scientific approach to calculate the minimum and maximum temperature values for each day and location...