Marketing applications of R-based CART and random forests, cross-validation/model comparison, causal trees and forests for targeting, segmentation, principal-component analysis and recommender systems
University of Colorado System · Coursera
Listed level: Header labels the course intermediate. Descriptive prose calls it advanced. Preserve this level mismatch and its R-based marketing-model curriculum rather than label it beginner
Marketing applications of R-based CART and random forests, cross-validation/model comparison, causal trees and forests for targeting, segmentation, principal-component analysis and recommender systems
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An original example for comparing learning plans. Ask whether the course teaches this task, includes practice and offers feedback; the diagram does not describe a provider’s course.
Shared topic: Customer analytics and personalization
DataCamp
Builds click-through-rate prediction from advertising data: feature creation, classification/decision trees, cross-validation, regularization, random forests and grid-search tuning, evaluates predictions against ad-spend ROI
Intermediate. About four hours, 15 videos and 57 exercisesCheck current priceAsk about teaching language
13 modules and 15 assignments. Intermediate. Coursera estimates two weeks at ten hours per week. Some assignments are shortCheck current priceAsk about teaching language
Shared topic: Customer analytics and personalization
O.P. Jindal Global University
Python marketing workflows cover k-means, hierarchical and DBSCAN segmentation, PCA, t-SNE and autoencoders, anomaly detection, association mining, semi-supervised methods, and recommender systems
12 modules and 36 assignments. Coursera estimates two weeks at ten hours per weekCheck current priceAsk about teaching language