Advanced Predictive Models for Complex Data obscures random/mixed-effects measures for multilevel data (clustered, repeated, and longitudinal) and Gaussian process models for dependent data. Emphasis is placed on both interpretations of inferences on model parameters and prediction.

Predictive analytics determine customer responses or purchases and promote cross-sell opportunities. Predictive models enable firms to entice, maintain and develop their most fortunate customers. They are improving operations, and many companies use predictive models to forecast inventory and manage resources.

A basic understanding of R is required for this course.

Developers aspiring to be a 'Data Scientist.' Analytics Managers 'R' experts who want to catch and explore Big Data Business Analysts interested in learning Machine Learning (ML) Techniques

Predictive models examine past interpretations to consider how potential customers display atypical behavior tomorrow. This class also contains models that desire light data patterns to reply to inquiries about customer arrangements, such as fake detection models.

Predictive analytics increases demand forecasting accuracy by simultaneously analyzing various factors, such as weather events or economic expansion. Data-driven decision-making and the capacity to determine potential market prospects are raised when companies use data analytics techniques.

Advanced Predictive Models for complicated Data covers random/mixed-effects models for multilevel data (clustered data, repeated measures, and longitudinal Data) and Gaussian process models for dependent data. Emphasis is placed on both interpretations of inferences on model parameters and prediction.