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Available courses
Aim of Course:
This course covers the crafting of survey questions, the design of surveys, and different sampling procedures that are used in practice. Longstanding basic principles of survey design are covered, and the impact of the trend toward increased respondent resistance is discussed.
Aim of Course:
This course covers how to develop and implement data science projects in a responsible and ethical way. It includes a review of predictive modeling, but some prior familiarity is helpful.
- Teacher: Peter Bruce
- Teacher: Grant Fleming
Aim of Course:
This course covers key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis and classification.
- Teacher: Robert LaBudde
Aim of Course:
Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales and quality-of-life assessments. This course covers the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.
- Teacher: Everett Smith
Aim of Course:
To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.
- Teacher: Anuja Kulkarni
Aim of Course:
This course covers how to apply linear programming to complex systems to make better decisions – decisions that increase revenue, decrease costs, or improve efficiency of operations.
- Teacher: Cliff Ragsdale
Aim of Course:
This course continues the work of Predictive Analytics 1, and introduces
you to additional techniques in predictive analytics, also called
predictive modeling, the most prevalent form of data mining.
- Teacher: Kuber Deokar
Aim of Course:
In this online course, “Predictive Analytics 1 - Machine Learning Tools -
with R,” you will be introduced to the basic concepts in predictive
analytics, also called predictive modeling, the most prevalent form of
data mining. This course covers the two core paradigms that account for
most business applications of predictive modeling: classification and
prediction. In both cases, predictive modeling takes data where a
variable of interest (a target variable) is known and develops a model
that relates this variable to a series of predictor variables, also
called features. In classification, the target variable is categorical
("purchased something" vs. "has not purchased anything").
- Teacher: Inbal Yahav
Aim of Course:
This course continues the work of Predictive Analytics 1, and introduces
you to additional techniques in predictive analytics, also called
predictive modeling, the most prevalent form of data mining.
- Teacher: Peter Gedeck
Aim of Course:
In this online course, “Predictive Analytics 1 - Machine Learning Tools -
with Python,” you will be introduced to the basic concepts in
predictive analytics, also called predictive modeling, the most
prevalent form of data mining.
- Teacher: Peter Gedeck