Jupyter for Data Science
Publisher: Packt Publishing
Published: 2017
Place of publication: Birmingham
Language: eng
Number of pages: 236
ISBN-13: 9781785883293
Subjects: Management information systems; COMPUTERS; Management Information Systems
Description: 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.
Notes: ""Analyzing 2016 voter registration and voting""
Table of contents: title: Cover; title: Copyright; title: Credits; title: About the Author; title: About the Reviewers; title: www.PacktPub.com; title: Customer Feedback; title: Table of Contents; title: Preface; title: Chapter 1: Jupyter and Data Science; title: Jupyter concepts; title: A first look at the Jupyter user interface; title: Detailing the Jupyter tabs; title: What actions can I perform with Jupyter?; title: What objects can Jupyter manipulate?; title: Viewing the Jupyter project display; title: File menu; title: Edit menu; title: View menu; title: Insert menu; title: Cell menu; title: Kernel menu; title: Help menu; title: Icon toolbar; title: How does it look when we execute scripts?Industry data science usage; title: Real life examples; title: Finance, Python; title: European call option valuation; title: Finance, Python; title: Monte Carlo pricing; title: Gambling, R; title: betting analysis; title: Insurance, R; title: non-life insurance pricing; title: Consumer products, R; title: marketing effectiveness; title: Using Docker with Jupyter; title: Using a public Docker service; title: Installing Docker on your machine; title: How to share notebooks with others; title: Can you email a notebook?; title: Sharing a notebook on Google Drive; title: Sharing on GitHub; title: Store as HTML on a web serverInstall Jupyter on a web server; title: How can you secure a notebook?; title: Access control; title: Malicious content; title: Summary; title: Chapter 2: Working with Analytical Data on Jupyter; title: Data scraping with a Python notebook; title: Using heavy-duty data processing functions in Jupyter; title: Using NumPy functions in Jupyter; title: Using pandas in Jupyter; title: Use pandas to read text files in Jupyter; title: Use pandas to read Excel files in Jupyter; title: Using pandas to work with data frames; title: Using the groupby function in a data frame; title: Manipulating columns in a data frameCalculating outliers in a data frame; title: Using SciPy in Jupyter; title: Using SciPy integration in Jupyter; title: Using SciPy optimization in Jupyter; title: Using SciPy interpolation in Jupyter; title: Using SciPy Fourier Transforms in Jupyter; title: Using SciPy linear algebra in Jupyter; title: Expanding on panda data frames in Jupyter; title: Sorting and filtering data frames in Jupyter/IPython; title: Filtering a data frame; title: Sorting a data frame; title: Summary; title: Chapter 3: Data Visualization and Prediction; title: Make a prediction using scikit-learn; title: Make a prediction using RInteractive visualization; title: Plotting using Plotly; title: Creating a human density map; title: Draw a histogram of social data; title: Plotting 3D data; title: Summary; title: Chapter 4: Data Mining and SQL Queries; title: Special note for Windows installation; title: Using Spark to analyze data; title: Another MapReduce example; title: Using SparkSession and SQL; title: Combining datasets; title: Loading JSON into Spark; title: Using Spark pivot; title: Summary; title: Chapter 5: R with Jupyter; title: How to set up R for Jupyter; title: R data analysis of the 2016 US election demographics