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alon's Projects

2014 icon 2014

Official content for the Fall 2014 Harvard CS109 Data Science course

2015 icon 2015

Public material for CS109

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Compiled and pretty version of labs

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A JavaScript / Python / PHP cryptocurrency trading library with support for more than 90 bitcoin/altcoin exchanges

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Statistics One is designed to be a comprehensive yet friendly introduction to fundamental concepts in statistics. Comprehensive means that this course provides a solid foundation for students planning to pursue more advanced courses in statistics. Friendly means exactly that. The course assumes very little background knowledge in statistics and introduces new concepts with several fun and easy to understand examples. This course is, quite literally, for everyone. If you think you can't learn statistics, this course is for you. If you had a statistics course before but feel like you need a refresher, this course is for you. Even if you are a relatively advanced researcher or analyst, this course provides a foundation and a context that helps to put one’s work into perspective. Statistics One also provides an introduction to the R programming language. All the examples and assignments will involve writing code in R and interpreting R output. R software is free! What this means is you can download R, take this course, and start programming in R after just a few lectures. That said, this course is not a comprehensive guide to R or to programming in general.

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Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

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The Open Source Data Science Masters

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This is a simple attractive circular countdown timer that counts down a number of seconds. The style is configurable and a callback is supported on completion.

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Python for .NET is a package that gives Python programmers nearly seamless integration with the .NET Common Language Runtime (CLR) and provides a powerful application scripting tool for .NET developers.

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Example R scripts and data for "Practical Data Science with R" by Nina Zumel and John Mount (Manning Publications)

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