Data Science for Marketing Analytics with Python

Modern marketing relies on the ability to turn vast amounts of data into clear, actionable strategies for growth and retention. This course provides a foundational path for using Python to analyze customer behavior and optimize marketing outcomes through data-driven decision-making. You will learn how to transition from basic data exploration to building predictive models that solve real-world marketing challenges. By the end of this course, you will be able to identify high-value customer segments and predict future trends using industry-standard analytical techniques. What you'll learn: - Understand foundational data science terminology and modern Python environments for marketing. - Manipulate marketing datasets using pandas and modern dataframe libraries to clean and prepare raw information. - Perform customer segmentation using clustering techniques to identify distinct audience groups. - Build regression models to calculate and predict customer lifetime value with precision. - Apply classification algorithms to predict customer churn and anticipate product choices. - Practice creating structured marketing reports that translate complex data into business insights. - Explore how prompt engineering can assist in interpreting patterns within marketing datasets. The course begins with essential definitions and core concepts before moving into practical data manipulation and statistical modeling. You will progress through written explanations and code-based exercises that cover the entire analytics lifecycle, from data ingestion to predictive modeling. This course is designed for marketing professionals, business analysts, and beginners who want to build a technical foundation in data science. No prior programming or statistics experience is required to begin. Start applying data science to your marketing strategy today.