Marketing Data Science, Forecasting, and Decision Models

1

Marketing data science: fundamentals and strategy

Introduction to data science applied to marketing: segmentation, prediction, and causality.

2

Python for marketing data science

Essential Python tools for marketing analytics: pandas, scikit-learn, statsmodels, Prophet.

Prerequisites: 1

3

Customer analytics and segmentation

Customer segmentation techniques: RFM, K-means, and behavioral clustering for marketing.

Prerequisites: 1

4

Cluster analysis: techniques and applications

Advanced clustering techniques: hierarchical, DBSCAN, and Gaussian Mixture Models for segmentation.

Prerequisites: 1

5

NPS critique and advanced satisfaction metrics

Critical analysis of the Net Promoter Score and alternative metrics to measure customer satisfaction.

Prerequisites: 1

6

PMF and product-market fit analytics

Measure product-market fit with quantitative methods: retention, NRR, and Sean Ellis test.

Prerequisites: 1

7

Modern Attribution Modeling

From last-click to incremental models: how to read attribution and the real contribution of channels without confusing correlation and causality.

Prerequisites: 1

8

Advanced unit economics for marketing

Model CAC, LTV, and payback period with segmentation and dynamic prediction.

Prerequisites: 1

9

Trigger analytics and marketing automation

Identify behavioral triggers to activate marketing campaigns in real time.

Prerequisites: 1

10

Text generation and NLP for marketing

Applying NLP and generative AI to marketing: copy generation, sentiment, and classification.

Prerequisites: 1

11

Embeddings and semantic representation

Using embeddings to represent customers, products, and content in vector spaces.

Prerequisites: 1

12

Analytical traps and biases in marketing

Common statistical errors in marketing analytics and frameworks to avoid them.

Prerequisites: 1

13

HADI cycles: practical application

The HADI framework (Hypothesis-Action-Data-Insights) for iterative marketing data science.

Prerequisites: 1

14

Incrementality testing and holdout

Measuring the incremental effect of marketing with holdout tests and control groups.

Prerequisites: 1

15

Predictive models for LTV and churn

Build predictive models for Customer Lifetime Value and churn probability in marketing.

Prerequisites: 1

16

Retention analytics and sustainable growth

How to read retention to understand product quality and growth sustainability, with cohorts, curves, and operational decisions.

Prerequisites: 1

17

Drift, model decay, and monitoring

Drift, model decay, and monitoring. Core lesson of the Marketing Data Science, Forecasting, and Decision Models module with real problem, conceptual model, rigorous formalization, applied case, 3-level lab, and final checkpoint.

Prerequisites: 1

18

Cheat Sheet — Marketing Data Science

Quick reference for data science techniques and patterns applied to marketing.

Prerequisites: 1

19

Case Study: End-to-End Marketing Data Science

Practical Project: Predict churn and build a data-driven retention strategy.

Prerequisites: 1