Predictive Analytics with Categorical Data: Advanced Regression Methods for Real-World Applications

Modern data sets are often full of categorical variables. To make informed choices—such as whether to invest in one company or another—you can use categorical regression analysis. In this comprehensive course, instructor Franz Buscha guides you through the intricacies of advanced regression analysis, helping you unlock the potential of categorical data to make data-driven decisions in your field. Along the way, Franz shares hands-on examples and data sets from real-world scenarios, ranging from job hiring practices to transportation preferences. By the end of this course, you'll be prepared to not only leverage statistical techniques, but also communicate your findings effectively, enhancing your role as a data storyteller. These advanced lessons are suited for individuals with a background in statistics or data analysis, particularly data scientists familiar with linear regression concepts. Tune in and let this course be your guide to the vast world of categorical data analysis.

Learning objectives
Identify and differentiate between binary, ordered and nominal data types and determine when to use them.
Implement logit/probit regression and ordered logistic regression models to binary and ordered dependent variables.
Implement advanced categorical regression models such as multinomial logit, stereotype logit models, and exploded logit models to evaluate complex data designs.
Interpret model coefficients using marginal effects calculation, and assess specialized goodness-of-fit statistics for a range of different categorical choice models.
Prepare and deliver results in reports or presentations of complex nonlinear models in such a way that nonexperts can understand the results and implications.