Overview

This course focuses on building practical marketing analytics skills using Python and statistical methods that are essential in today’s data-driven business environment. It emphasizes turning complex datasets into meaningful insights that support strategic marketing decisions. Through hands-on examples, you’ll learn how to analyze marketing data, apply appropriate models, and interpret results to improve campaign performance and customer understanding. The course helps you move from raw data to actionable outcomes with confidence. What sets this course apart is its balance between theory and real-world application. It combines statistical reasoning with practical Python workflows to solve common marketing analytics problems. This course is ideal for marketing-focused data analysts and data scientists with prior experience in Python, basic statistics, and data analysis who want to deepen their analytical impact.

Syllabus

What Is Marketing Analytics?

In this section, we cover marketing analytics fundamentals, including descriptive and diagnostic analytics, and their role in decision-making.

Extracting and Exploring Data with Singer and pandas

In this section, we explore ETL processes using Singer and pandas for data extraction and exploratory data analysis. Key concepts include descriptive statistics, data issues, and practical data cleaning techniques.

Design Principles and Presenting Results with Streamlit

In this section, we explore Streamlit dashboard design, focusing on effective metrics, dimensions, and layout principles for clear data presentation and user-centered visualization.

Econometrics and Causal Inference with Statsmodels and PyMC

In this section, we explore linear and logistic regression models to analyze causal relationships and interpret coefficients for data-driven decision-making in marketing analytics.

Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast

In this section, we explore forecasting techniques like Prophet and ARIMA for marketing KPIs, focusing on model selection, performance evaluation, and practical applications in time series analysis.

Anomaly Detection with StatsForecast and PyMC

In this section, we explore anomaly detection using STL decomposition, S-H-ESD, and PyMC for Bayesian change point detection, emphasizing practical applications and technical accuracy.

Customer Insights Segmentation and RFM

In this section, we explore customer segmentation and RFM analysis to identify high-value customers and optimize marketing strategies using Python for data-driven decision-making.

Customer Lifetime Value with PyMC Marketing

In this section, we explore CLV fundamentals, challenges in its formula, and implement the BTYD model with PyMC Marketing to predict customer value and purchase frequency accurately.

Customer Survey Analysis

In this section, we explore customer survey design, reliability, validity, sampling methods, and NPS limitations to improve data accuracy and customer insights.

Conjoint Analysis with pandas and Statsmodels

In this section, we explain conjoint analysis and how to use it to understand customer preferences and decision-making.

Multi-Touch Digital Attribution

In this section, we explore heuristic and algorithmic attribution models to evaluate marketing touchpoints and optimize spend. Key concepts include Shapley values, marginal contributions, and Python implementation for conversion path analysis.

Media Mix Modeling with PyMC Marketing

In this section, we explore media mix modeling (MMM) to assess marketing effectiveness using Python. Key concepts include data collection, adstock effects, and synthetic data applications for limited data scenarios.

Running Experiments with PyMC

In this section, we explore designing and evaluating experiments using A/A testing, p-values, and statistical power to ensure reliable results in marketing and data analysis.