Jupyter for Data Science

Data -- Review spread -- Finding the top rated firms -- Finding the most rated firms -- Finding all ratings for a top rated firm -- Determining the correlation between ratings and number of reviews -- Building a model of reviews -- Using Python to compare ratings -- Visualizing average ratings by cuisine -- Arbitrary search of ratings -- Determining relationships between number of ratings and ratings -- Summary -- Chapter 9: Machine Learning Using Jupyter -- Naive Bayes -- Naive Bayes using R -- Naive Bayes using Python -- Nearest neighbor estimator -- Nearest neighbor using R -- Nearest neighbor using Python -- Decision trees -- Decision trees in R -- Decision trees in Python -- Neural networks -- Neural networks in R -- Random forests -- Random forests in R -- Summary -- Chapter 10: Optimizing Jupyter Notebooks -- Deploying notebooks -- Deploying to JupyterHub -- Installing JupyterHub -- Accessing a JupyterHub Installation -- Jupyter hosting -- Optimizing your script -- Optimizing your Python scripts -- Determining how long a script takes -- Using Python regular expressions -- Using Python string handling -- Minimizing loop operations -- Profiling your script -- Optimizing your R scripts -- Using microbenchmark to profile R script -- Modifying provided functionality -- Optimizing name lookup -- Optimizing data frame value extraction -- Changing R Implementation -- Changing algorithms -- Monitoring Jupyter -- Caching your notebook -- Securing a notebook -- Managing notebook authorization -- Securing notebook content -- Scaling Jupyter Notebooks -- Sharing Jupyter Notebooks -- Sharing Jupyter Notebook on a notebook server -- Sharing encrypted Jupyter Notebook on a notebook server -- Sharing notebook on a web server -- Sharing notebook on Docker -- Converting a notebook -- Versioning a notebook -- Summary -- Index.