Data science, machine learning, and analytics without coding
Eric Hulbert
Build a KNIME Random Forest model for a sales-and-marketing client, prepare data and interpret predictions within a broader no-code data-science course
Marketing focus
Prepare campaign data, model Google Ads outcomes using Random Forest and Gradient Boosting, compare channels and benchmarks, and apply cohort/RFM analysis to retention and ROI
Spaark Hub, Gaurav Shandilya · Udemy
Prepare campaign data, model Google Ads outcomes using Random Forest and Gradient Boosting, compare channels and benchmarks, and apply cohort/RFM analysis to retention and ROI
Practice: A three-lecture campaign-analytics exercise section is advertised. The 24 minutes refer to that section, not the entire course
Recorded Udemy course advertised with a public course preview. Exact lesson count may be unknown. Purchase or organization-subscription terms and current availability require checking
Related tasks, tools and formats. Check the differences before choosing.
Eric Hulbert
Build a KNIME Random Forest model for a sales-and-marketing client, prepare data and interpret predictions within a broader no-code data-science course
DataCamp
Four chapters (17 videos, 55 exercises) use online-retailer transactions for cohort acquisition/retention metrics, RFM value scoring and custom segments, then prepare and scale RFM features, fit k-means and interpret the resulting…
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