Aim of Course:
To provide an easy introduction to statistical inference for a single variable.
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 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.
Aim of Course:
This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.
Aim of Course:
This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.
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