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Seeing Theory

Seeing Theory
Seeing Theory is a project designed and created by Daniel Kunin with support from Brown University's Royce Fellowship Program. The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations. Statistics is quickly becoming the most important and multi-disciplinary field of mathematics. According to the American Statistical Association, "statistician" is one of the top ten fastest-growing occupations and statistics is one of the fastest-growing bachelor degrees. Statistical literacy is essential to our data driven society. Yet, for all the increased importance and demand for statistical competence, the pedagogical approaches in statistics have barely changed.

http://students.brown.edu/seeing-theory/

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The World Factbook The Office of Public Affairs (OPA) is the single point of contact for all inquiries about the Central Intelligence Agency (CIA). We read every letter, fax, or e-mail we receive, and we will convey your comments to CIA officials outside OPA as appropriate. However, with limited staff and resources, we simply cannot respond to all who write to us. Contact Information Submit questions or comments online A Neural Network Playground Data Which dataset do you want to use? Features Which properties do you want to feed in? Click anywhere to edit.

The Mathematics of Machine Learning In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. The globe of economic complexity About close x The Globe of Economic Complexity The globe of economic complexity dynamically maps out the entire world production of goods to create an economic landscape of countries around the globe.

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Unlearning descriptive statistics If you've ever used an arithmetic mean, a Pearson correlation or a standard deviation to describe a dataset, I'm writing this for you. Better numbers exist to summarize location, association and spread: numbers that are easier to interpret and that don't act up with wonky data and outliers. Statistics professors tend to gloss over basic descriptive statistics because they want to spend as much time as possible on margins of error and t-tests and regression. Fair enough, but the result is that it's easier to find a machine learning expert than someone who can talk about numbers. Forget what you think you know about descriptives and let me give you a whirlwind tour of the real stuff. Mazes or Labyrinths ? Terracotta Angel, c.1896Watts Chapel, England Photo ©: Jeff Saward/Labyrinthos Please note, the contents

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