Aim of Course:
This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.
This course covers modeling technique making decisions in the presence of risk or uncertainty. Specific topics include risk analysis using Monte Carlo simulation for risk simulation, queuing theory for problems involving waiting lines, and decision trees for analyzing problems with multiple discrete decision alternatives.
This course will offer an introduction to sample size and power analysis and will show how to use it simply and effectively to plan the appropriate sample size for a study.
The purpose of this online course is to teach you how to extract data from a relational database using SQL, so that you can perform statistical operations. The focus is on structuring queries to extract structured data (not on building databases or methods of handling big data).
In this online course, you will learn about the rapidly evolving field of Deep Learning. At the end of this course you will understand the basic concepts underlying the representations and methods in deep learning and see some applications where deep learning is most effective.
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.
This course covers the fundamental concepts and theory of Structural Equation Modeling: model specification, model identification, model estimation, model testing, and model modification.
This course will introduce basic concepts in computer programming via R - it is for those who have had little or no experience in programming.
To provide an easy introduction to statistical inference for a single variable.
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.
This course will teach users how to implement spatial statistical analysis procedures using R software. Topics covered include point pattern analysis, identifying clusters, measures of spatial association, geographically weighted regression and surface processing.
This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software.
In this online course, you will learn how to examine data with the goal of detecting anomalies or abnormal instances. This task is critical in a wide range of applications ranging from fraud detection to surveillance.
Meta analysis, the ‘analysis of analyses’, is the term used to describe
the quantitative synthesis of scientific evidence. The aim of this
course is to introduce students to the fundamentals of meta-analysis and
provide an in-depth review of tools for conducting meta-analyses in the
R language. The course will cover the fundamentals of the fixed and
random effects models for meta-analysis, the assessment of
heterogeneity, and evaluating bias.
This course teaches you how to estimate variances for complex surveys, and also how to model the results using linear and logistic regression, and other generalized linear models.