Jupyter for Data Science

Your one-stop guide to building an efficient data science pipeline using Jupyter
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Jupyter for Data Science

Dan Toomey

2 customer reviews
Your one-stop guide to building an efficient data science pipeline using Jupyter

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Book Details

ISBN 139781785880070
Paperback242 pages

Book Description

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook.

If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks.

By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

Table of Contents

Chapter 1: Jupyter and Data Science
Jupyter concepts
A first look at the Jupyter user interface
Summary
Chapter 2: Working with Analytical Data on Jupyter
Data scraping with a Python notebook
Using heavy-duty data processing functions in Jupyter
Using SciPy in Jupyter
Expanding on panda data frames in Jupyter
Summary
Chapter 3: Data Visualization and Prediction
Make a prediction using scikit-learn
Make a prediction using R
Interactive visualization
Plotting using Plotly
Creating a human density map
Draw a histogram of social data
Plotting 3D data
Summary
Chapter 4: Data Mining and SQL Queries
Special note for Windows installation
Using Spark to analyze data
Another MapReduce example
Using SparkSession and SQL
Combining datasets
Loading JSON into Spark
Using Spark pivot
Summary
Chapter 5: R with Jupyter
How to set up R for Jupyter
R data analysis of the 2016 US election demographics
Analyzing 2016 voter registration and voting
Analyzing changes in college admissions
Predicting airplane arrival time
Summary
Chapter 6: Data Wrangling
Reading a CSV file
Reading another CSV file
Manipulating data with dplyr
Sampling a dataset
Tidying up data with tidyr
Summary
Chapter 7: Jupyter Dashboards
Visualizing glyph ready data
Publishing a notebook
Creating a Shiny dashboard
Building standalone dashboards
Summary
Chapter 8: Statistical Modeling
Converting JSON to CSV
Evaluating Yelp reviews
Using Python to compare ratings
Visualizing average ratings by cuisine
Arbitrary search of ratings
Determining relationships between number of ratings and ratings
Chapter 9: Machine Learning Using Jupyter
Naive Bayes
Nearest neighbor estimator
Decision trees
Neural networks
Random forests
Summary
Chapter 10: Optimizing Jupyter Notebooks
Deploying notebooks
Optimizing your script
Monitoring Jupyter
Caching your notebook
Securing a notebook
Scaling Jupyter Notebooks
Sharing Jupyter Notebooks
Converting a notebook
Versioning a notebook
Summary

What You Will Learn

  • Understand why Jupyter notebooks are a perfect fit for your data science tasks
  • Perform scientific computing and data analysis tasks with Jupyter
  • Interpret and explore different kinds of data visually with charts, histograms, and more
  • Extend SQL's capabilities with Jupyter notebooks
  • Combine the power of R and Python 3 with Jupyter to create dynamic notebooks
  • Create interactive dashboards and dynamic presentations
  • Master the best coding practices and deploy your Jupyter notebooks efficiently

Authors

Table of Contents

Chapter 1: Jupyter and Data Science
Jupyter concepts
A first look at the Jupyter user interface
Summary
Chapter 2: Working with Analytical Data on Jupyter
Data scraping with a Python notebook
Using heavy-duty data processing functions in Jupyter
Using SciPy in Jupyter
Expanding on panda data frames in Jupyter
Summary
Chapter 3: Data Visualization and Prediction
Make a prediction using scikit-learn
Make a prediction using R
Interactive visualization
Plotting using Plotly
Creating a human density map
Draw a histogram of social data
Plotting 3D data
Summary
Chapter 4: Data Mining and SQL Queries
Special note for Windows installation
Using Spark to analyze data
Another MapReduce example
Using SparkSession and SQL
Combining datasets
Loading JSON into Spark
Using Spark pivot
Summary
Chapter 5: R with Jupyter
How to set up R for Jupyter
R data analysis of the 2016 US election demographics
Analyzing 2016 voter registration and voting
Analyzing changes in college admissions
Predicting airplane arrival time
Summary
Chapter 6: Data Wrangling
Reading a CSV file
Reading another CSV file
Manipulating data with dplyr
Sampling a dataset
Tidying up data with tidyr
Summary
Chapter 7: Jupyter Dashboards
Visualizing glyph ready data
Publishing a notebook
Creating a Shiny dashboard
Building standalone dashboards
Summary
Chapter 8: Statistical Modeling
Converting JSON to CSV
Evaluating Yelp reviews
Using Python to compare ratings
Visualizing average ratings by cuisine
Arbitrary search of ratings
Determining relationships between number of ratings and ratings
Chapter 9: Machine Learning Using Jupyter
Naive Bayes
Nearest neighbor estimator
Decision trees
Neural networks
Random forests
Summary
Chapter 10: Optimizing Jupyter Notebooks
Deploying notebooks
Optimizing your script
Monitoring Jupyter
Caching your notebook
Securing a notebook
Scaling Jupyter Notebooks
Sharing Jupyter Notebooks
Converting a notebook
Versioning a notebook
Summary

Book Details

ISBN 139781785880070
Paperback242 pages
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From 2 reviews

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