Finding outliers begins by looking at the distribution curve but requires additional techniques that we will walk through together. Additionally, don't underestimate the need for soft skills where you must reach out to others to better understand why an outlier exists in your data. An outlier is commonly known as one or more data values that are significantly different than the rest of the data. Spotting outliers in data is easy depending on the data visualization used, but in many cases, especially when data volumes are very large, they can be obscured when data is aggregated. If you recall from Chapter 7, Exploring Cleaning, Refining, and Blending Datasets, we worked with hits created by a user for a website. A good example of obscuring outliers is when those user hits are aggregated by date. If a specific user has 1,000 hits per day when the average is 2, it would be difficult to identify that outlier user after the data was aggregated...
Practical Data Analysis Using Jupyter Notebook
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Practical Data Analysis Using Jupyter Notebook
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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.
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Table of Contents (18 chapters)
Preface
Section 1: Data Analysis Essentials
Free Chapter
Fundamentals of Data Analysis
Overview of Python and Installing Jupyter Notebook
Getting Started with NumPy
Creating Your First pandas DataFrame
Gathering and Loading Data in Python
Section 2: Solutions for Data Discovery
Visualizing and Working with Time Series Data
Exploring, Cleaning, Refining, and Blending Datasets
Understanding Joins, Relationships, and Aggregates
Plotting, Visualization, and Storytelling
Section 3: Working with Unstructured Big Data
Exploring Text Data and Unstructured Data
Practical Sentiment Analysis
Bringing It All Together
Works Cited
Other Books You May Enjoy
Customer Reviews