Exploratory Data Analysis

 

 


 

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E.g., 2016-12-07
E.g., 2016-12-07
E.g., 2016-12-07
Dec 12th 2016

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

Average: 7 (4 votes)
Dec 12th 2016

With marketers are poised to be the largest users of data within the organization, there is a need to make sense of the variety of consumer data that the organization collects. Surveys, transaction histories and billing records can all provide insight into consumers’ future behavior, provided that they are interpreted correctly.

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Dec 5th 2016

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: 7.7 (3 votes)
Dec 5th 2016

This course will expose you to the data analytics practices executed in the business. We will explore such key areas of data analytics as the analytical process, how data is created, stored, and accessed, and how the organization works with data and creates the environment in which analytics can flourish.

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Jul 11th 2016

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.

Average: 7.8 (4 votes)
Oct 15th 2015

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

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Self Paced

Investigate, Visualize, and Summarize Data Using R.

Average: 3.7 (9 votes)