Practical Data Analysis Using Jupyter Notebook

Book description

Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook

Key Features

  • Find out how to use Python code to extract insights from data using real-world examples
  • Work with structured data and free text sources to answer questions and add value using data
  • Perform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing data

Book Description

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.

What you will learn

  • Understand the importance of data literacy and how to communicate effectively using data
  • Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis
  • Wrangle data and create DataFrames using pandas
  • Produce charts and data visualizations using time-series datasets
  • Discover relationships and how to join data together using SQL
  • Use NLP techniques to work with unstructured data to create sentiment analysis models
  • Discover patterns in real-world datasets that provide accurate insights

Who this book is for

This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.

Publisher resources

Download Example Code

Table of contents

  1. Title Page
  2. Copyright and Credits
    1. Practical Data Analysis using Jupyter Notebook
  3. About Packt
    1. Why subscribe?
  4. Foreword
  5. Contributors
    1. About the author
    2. About the reviewers
    3. Packt is searching for authors like you
  6. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
      1. Download the example code files
      2. Download the color images
      3. Conventions used
    4. Get in touch
      1. Reviews
  7. Section 1: Data Analysis Essentials
  8. Fundamentals of Data Analysis
    1. The evolution of data analysis and why it is important
    2. What makes a good data analyst?
      1. Know Your Data (KYD)
      2. Voice of the Customer (VOC)
      3. Always Be Agile (ABA)
    3. Understanding data types and their significance
      1. Unstructured data
      2. Semi-structured data
      3. Structured data
      4. Common data types
    4. Data classifications and data attributes explained
      1. Data attributes
    5. Understanding data literacy
      1. Reading data
      2. Working with data
      3. Analyzing data
      4. Arguing about the data
    6. Summary
    7. Further reading
  9. Overview of Python and Installing Jupyter Notebook
    1. Technical requirements
    2. Installing Python and using Jupyter Notebook
      1. Installing Anaconda
      2. Running Jupyter and installing Python packages for data analysis
    3. Storing and retrieving data files
    4. Hello World! – running your first Python code
      1. Creating a project folder hierarchy
      2. Uploading a file
    5. Exploring Python packages
      1. Checking for pandas
      2. Checking for NumPy
      3. Checking for sklearn
      4. Checking for Matplotlib
      5. Checking for SciPy
    6. Summary
    7. Future reading
  10. Getting Started with NumPy
    1. Technical requirements
    2. Understanding a Python NumPy array and its importance
      1. Differences between single and multiple dimensional arrays
    3. Making your first NumPy array
      1. Useful array functions
    4. Practical use cases of NumPy and arrays
      1. Assigning values to arrays manually
      2. Assigning values to arrays directly
      3. Assigning values to an array using a loop
    5. Summary
    6. Further reading
  11. Creating Your First pandas DataFrame
    1. Technical requirements
    2. Techniques for manipulating tabular data
    3. Understanding pandas and DataFrames
    4. Handling essential data formats
      1. CSV
      2. XML
        1. Data hierarchy
        2. Defined schema
      3. JSON
    5. Data dictionaries and data types
    6. Creating our first DataFrame
    7. Summary
    8. Further reading
  12. Gathering and Loading Data in Python
    1. Technical requirements
    2. Introduction to SQL and relational databases
    3. From SQL to pandas DataFrames
    4. Data about your data explained
      1. Fundamental statistics
      2. Metadata explained
    5. The importance of data lineage
      1. Data flow
        1. The input stage
        2. The data ingestion stage
        3. The data source stage
        4. The data target stage
      2. Business rules
    6. Summary
    7. Further reading
  13. Section 2: Solutions for Data Discovery
  14. Visualizing and Working with Time Series Data
    1. Technical requirements
    2. Data modeling for results
      1. Introducing dimensions and measures
    3. Anatomy of a chart and data viz best practices
      1. Analyzing your data
      2. Why pie charts have lost ground
      3. Art versus science
      4. What makes great data visualizations?
    4. Comparative analysis
      1. Date and time trends explained
    5. The shape of the curve
      1. Creating your first time series chart
    6. Summary
    7. Further reading
  15. Exploring, Cleaning, Refining, and Blending Datasets
    1. Technical requirements
    2. Retrieving, viewing, and storing tabular data
      1. Retrieving
      2. Viewing
      3. Storing
    3. Learning how to restrict, sort, and sift through data
      1. Restricting
      2. Sorting
      3. Sifting
    4. Cleaning, refining, and purifying data using Python
    5. Combining and binning data
      1. Binning
    6. Summary
    7. Further reading
  16. Understanding Joins, Relationships, and Aggregates
    1. Technical requirements
    2. Foundations of join relationships
      1. One-to-one relationships
      2. Many-to-one relationships
      3. Many-to-many relationship
      4. Left join
      5. Right join
      6. Inner join
      7. Outer join
    3. Join types in action
    4. Explaining data aggregation
      1. Understanding the granularity of data
      2. Data aggregation in action
    5. Summary statistics and outliers
    6. Summary
    7. Further reading<a href="">
  17. Plotting, Visualization, and Storytelling
    1. Technical requirements
    2. Explaining distribution analysis
      1. KYD
      2. Shape of the curve
    3. Understanding outliers and trends
    4. Geoanalytical techniques and tips
    5. Finding patterns in data
    6. Summary
    7. Further reading
  18. Section 3: Working with Unstructured Big Data
  19. Exploring Text Data and Unstructured Data
    1. Technical requirements
    2. Preparing to work with unstructured data
      1. Corpus in action
    3. Tokenization explained
      1. Tokenize in action
    4. Counting words and exploring results
      1. Counting words
    5. Normalizing text techniques
      1. Stemming and lemmatization in action
    6. Excluding words from analysis
    7. Summary
    8. Further reading
  20. Practical Sentiment Analysis
    1. Technical requirements
    2. Why sentiment analysis is important
    3. Elements of an NLP model
      1. Creating a prediction output
    4. Sentiment analysis packages
    5. Sentiment analysis in action
      1. Manual input
      2. Social media file input
    6. Summary
    7. Further reading
  21. Bringing It All Together
    1. Technical requirements
    2. Discovering real-world datasets
      2. The Humanitarian Data Exchange
      3. The World Bank
      4. Our World in Data
    3. Reporting results
      1. Storytelling
    4. The Capstone project
      1. KYD sources
      2. Exercise
    5. Summary
    6. Further reading
  22. Works Cited
  23. Other Books You May Enjoy
    1. Leave a review - let other readers know what you think

Product information

  • Title: Practical Data Analysis Using Jupyter Notebook
  • Author(s): Marc Wintjen
  • Release date: June 2020
  • Publisher(s): Packt Publishing
  • ISBN: 9781838826031