courses for review by invited guests to our Learning Management System

Aim of Course:

This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

Aim of Course:

This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.

Aim of Course:

In this course, you'll learn everything you need to get you started using Python for data analysis

In this course, you'll learn everything you need to get you started using Python for data analysis. - See more at: http://www.statistics.com/course-catalog/python/#syllabus

Aim of Course:

Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.

Aim of Course:

Data sets often have missing values. Missing data is a problem, in particular, with multivariate modeling. If the analyst must discard an entire record because the value for one variable is missing, valuable information is lost. Better to find a way keep the record, adjust for the missing value(s), and let the analysis proceed. This course teaches the basics of handling missing data including evaluation of types and patterns of missing data, strategies for analysis of data sets with item missing data, and imputation of missing data with an emphasis on multiple imputation.

Aim of Course:

This course will show you how to use R to create statistical models and use them to analyze data.