This course covers a number of advanced topics in optimization. Students taking this course will learn to specify and implement optimization models that solve network problems (what is the shortest path through a network, what is the least cost way to route material through a network with multiple supply nodes and multiple demand nodes).
The aim of the course is to give you the skills to work with a variety of data types and data sources in R.
After taking this course, you will be able to install and run rjags, a program for Bayesian analysis within R. Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.
You will have a chance to try the first week of our course “Predictive Analytics 1 - Machine Learning
Tools.” You will be introduced to the basic concepts in predictive
analytics, also called predictive modeling, the most prevalent form of
data mining. The full course covers the two core paradigms that account for
most business applications of predictive modeling: classification and
prediction.
Try out our introductory statistics series for one week - you can learn stats!
This course covers the analysis of data gathered in surveys.
This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.
After taking this course, you will be equipped to introduce natural language processing (NLP) processes into your projects and software applications.
This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.
The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.
In this course, you'll learn everything you need to get you started using Python for data analysis
To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.
This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.
After taking this course, participants will be able to design appropriate conjoint and choice studies, using surveys, panels, and designed experiments. They will also be able to analyze and interpret the resulting data.
This course covers clinical trial designs including randomized controlled trials, ROC curves, CI and tests for relative risk and odds ratio, and an introduction to survival analysis.
To provide an easy introduction to statistical inference for a single variable.
Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.
In this online course you will learn you how to apply predictive modeling methods, and persuasion (uplift) models in particular. The focus will be on targeting voters in political campaigns.
Meta-Analysis refers to the statistical analyses that are used to synthesize summary data from a series of studies. This course covers some advanced issues in meta-analysis. Participants should have completed the basic course in meta-analysis or an equivalent.
This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.