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.

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

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.

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.

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.

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.

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.

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

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

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.

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.

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.