Analyzing and Visualizing Data with F#

✍ Scribed by Tomas Petricek

• Publisher: O'Reilly
• Year: 2015
• Tongue: English
• Leaves: 56
• Category: Library

✦ Table of Contents

Cover
Additional Resources
Copyright
Table of Contents
Acknowledgements
Chapter 1. Accessing Data with Type Providers
Data Science Workflow
Why Choose F# for Data Science?
Getting Data from the World Bank
Calling the Open Weather Map REST API
Plotting Temperatures Around the World
Conclusions
Chapter 2. Analyzing Data Using F# and Deedle
Downloading Data Using an XML Provider
Visualizing CO2 Emissions Change
Aligning and Summarizing Data with Frames
Summarizing Data Using the R Provider
Normalizing the World Data Set
Conclusions
Chapter 3. Implementing Machine Learning Algorithms
How k-Means Clustering Works
Clustering 2D Points
Initializing Centroids and Clusters
Updating Clusters Recursively
Writing a Reusable Clustering Function
Clustering Countries
Scaling to the Cloud with MBrace
Conclusions
Chapter 4. Conclusions and Next Steps
Adding F# to Your Project
Resources for Learning More