In this course you will work through a customer analytics project from
beginning to end, using R. You will start by gaining an understanding of the
problem and the context, and continue to clean, prepare and explore the
relevant data.
This course extends the Bayesian modeling framework to cover hierarchical models, and to add flexibility to standard Bayesian modeling problems.
In this course, you will learn how to make decisions in building a factor analysis model - including what model to use, the number of factors to retain, and the rotation method to use.
This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.
This course will introduce basic concepts in computer programming via R - it is for those who have had little or no experience in programming.
Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable.
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 provides an introduction to the Python programming language for the complete beginner. We will set up a Python programming environment from download to writing programs, taking small steps along the way. The programming environment will feature the Jupyter notebook interface.
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 will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories).
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.
To provide an easy introduction to statistical inference for a single variable.
The aim of the course is to give you the skills to work with a variety of data types and data sources in R.
This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models. It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide examples of data analysis. The course aims at providing a basic understanding and knowledge of the mixed effect models that will allow you to use them in practice.
This course covers the application of Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.
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!