In this Specialization, you’ll learn how to use data, technology, and analytics to make stronger digital marketing decisions—from understanding modern marketing systems to running analyses that guide strategy. You’ll begin with an executive view of analytics: what analytics can do (predict, diagnose, prescribe), how analytics projects work in practice, and which technologies and roles support success across business functions.
Next, you’ll explore the platforms and infrastructure behind today’s digital marketing. You’ll study how recommendation systems personalize content while balancing accuracy, fairness, and user trust. You’ll learn how visual and multimodal data can reveal cultural, behavioral, and design trends, and how blockchain-enabled systems can support transparency and authenticity in marketing ecosystems. You’ll also evaluate data integrity, governance, and measurement frameworks that help keep performance reporting reliable at scale.
Finally, you’ll apply marketing analytics to real decision problems using Python. You’ll design experiments and use quasi-experimental approaches to estimate causal impact. You’ll build predictive models to forecast customer behaviors and outcomes, analyze social media using text and network techniques, and estimate demand, preferences, and customer lifetime value to support targeting and growth decisions.
Applied Learning Project
You’ll complete applied exercises that connect marketing questions to the right data, method, and decision. You’ll evaluate real analytics use cases and outline what makes analytics projects succeed, including the roles and technologies involved. You’ll assess modern marketing systems by explaining how recommendation engines drive personalization and how governance and measurement frameworks protect integrity and accountability. You’ll also do hands-on analysis in Python: designing experiments, using quasi-experimental methods to estimate impact, building predictive models to forecast customer behavior, and analyzing social data with text and network techniques. You’ll finish by estimating demand and preferences and calculating customer lifetime value to support practical marketing choices.
