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E.g., 2017-02-24
E.g., 2017-02-24
E.g., 2017-02-24
Feb 27th 2017

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization.

Average: 8 (5 votes)
Feb 20th 2017

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results.

Average: 6.5 (13 votes)
Feb 13th 2017

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

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Jan 16th 2017

A conceptual and interpretive public health approach to some of the most commonly used methods from basic statistics.

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Sep 12th 2016

Learn fundamentals of probabilistic analysis and inference. Build computer programs that reason with uncertainty and make predictions. Tackle machine learning problems, from recommending movies to spam filtering to robot navigation.

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Jun 2nd 2014

An introduction to statistical ideas and methods commonly used to make valid conclusions based on data from random samples.

Average: 5.5 (2 votes)