Data quality is highly important for any data analysis and analytics. In many cases, you will not understand how good or bad the data quality is until you start working with it. I would define good-quality data as information that is well structured, defined, and consistent, where almost all of the values in each field are defined as expected. In my experience, data warehouses will have high-quality data because it has been reported on across the organization. In my experience, bad data quality occurs where a lack of transparency exists against the data source. Bad data quality examples are a lack of conformity and inconsistency in the expected data type or any consistent pattern of values in delimited datasets. To help to solve these data quality issues, you can begin to understand your data with the concepts and questions we covered in Chapter 1, Fundamentals of Data Analysis, with Know Your Data (KYD). Since the quality of...
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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.
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
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