In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
In this online course, "Programming 2:Python," you'll learn
everything you need to get you started using Python for data analysis. We'll review basic Python skills and data structures, move on to how
to load data from different sources, rearrange and aggregate it, and
finally how to analyze and visualize it to create high-quality products.
Continues the exploration of Rasch theory and its application in the Winsteps software, begun in "Practical Rasch Measurement-Core Topics." The course introduces exciting new topics and delves into earlier topics more deeply. Participants are encouraged to analyze their own datasets in parallel to the course datasets.
This course will show you how to use R to create statistical models and use them to analyze data.
Try out our introductory statistics series for one week - you can learn stats!
This course teaches participants how to model financial events that have uncertainties associated with them.
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.
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 teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. In marketing applications, clusters of customer records are called market segments (and the process is called market segmentation).
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.
To provide an easy introduction to statistical inference for a single variable.
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.
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.