In this part, we will go through the fundamentals of analytics, introducing marketing analytics as a discipline. We will be focusing on data extraction, ingestion, and exploratory data analysis, followed by techniques for effectively presenting results and building dashboards for non-technical audiences. The subsequent discussion shifts toward econometrics and causal inference, providing a foundational understanding of statistics and equipping you with the skills to construct, test, and evaluate statistical models, emphasizing their significance and application in marketing.
This part contains the following chapters:
- Chapter 1, What is Marketing Analytics?
- Chapter 2, Extracting and Exploring Data with Singer and pandas
- Chapter 3, Design Principles and Presenting Results with Streamlit
- Chapter 4, Econometrics and Causal Inference with Statsmodels and PyMC