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
The Institute for Statistics Education at Statistics.com
is the leading provider of online education in statistics.
We offer 100+ courses in introductory and advanced statistical subjects.
Students from around the world study with leading authorities via private discussion boards
We offer 100+ courses in introductory and advanced statistical subjects.
Students from around the world study with leading authorities via private discussion boards
on flexible schedules.
Teaching assistants provide individual responses in practical exercises.
Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.
Teaching assistants provide individual responses in practical exercises.
Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.
Available courses
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:
This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.
- Teacher: Jen Golbeck
Aim of Course:
This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.
- Teacher: Karen Schmidt
Aim of Course:
This course covers the issues that need to be addressed in order for a study to produce statistically valid conclusions. The first session covers different study designs, with an emphasis on designs that can be used with observational data (clinical trials are reviewed briefly, but those seeking a more in depth treatment should take the Institute's sequence of courses in clinical trial design and analysis). Students will learn what types of bias and extraneous factors can endanger studies, and how to avoid or adjust for them.
- Teacher: Eli Rosenberg
Aim of Course:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Peter Bruce
- Teacher: Kuber Deokar
Aim of Course:
This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software.
- Teacher: Adil Yalcin
Aim of Course:
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.
- Teacher: Abhaya Indrayan
Aim of Course:
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.
- Teacher: Peter Bruce
Aim of Course:
This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text.
- Teacher: Anurag Bhardwaj
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:
To provide an easy introduction to statistical inference for a single variable.
- Teacher: Meena Badade
Aim of Course:
This course continues the study begun in the prior course "Structural Equation Modeling" and covers many popular advanced SEM models with practical exercises.
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:
Try out our introductory statistics series for one week - you can learn stats!
- Teacher: Anthony Babinec
- Teacher: Kuber Deokar
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.
- Teacher: Peter Gedeck
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.
- Teacher: Kuber Deokar
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.
- Teacher: Anthony Babinec
Aim of Course:
Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable.
- Teacher: Iain Pardoe
Aim of Course:
This course will introduce basic concepts in computer programming via R - it is for those who have had little or no experience in programming.
- Teacher: Paul Murrell