Data-Driven Digital Marketing & Analytics

via Coursera Specialization

Overview

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.

Syllabus

Course 1: Digital Marketing: Platforms, Data, and Technologies
Course 2: Applying Data Analytics in Marketing
Course 3: Business Analytics Executive Overview

Courses

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Successfully marketing brands today requires a well-balanced blend of art, science, and technology. This course introduces students to the systems and infrastructures that underpin modern digital marketing, from recommendation engines and visual analytics to blockchain-enabled transparency and advanced data measurement frameworks. The goal is to equip learners with the tools and reasoning needed to design, evaluate, and govern marketing technologies that drive personalization, trust, and performance at scale. Digital Marketing/Platforms, Data, and Technologies is the second in a two-part series of complementary courses. It focuses on how marketers use data architectures, algorithms, and integrity systems to understand consumers, predict behavior, and ensure fairness and accountability in a rapidly evolving digital ecosystem. Together, these insights form the technological backbone of modern marketing strategy. This course is part of Gies College of Business’ suite of online programs, including iMBA and iMSM. Learn more about admission into these programs and explore how your Coursera work can be leveraged if accepted into a degree program at https://degrees.giesbusiness.illinois.edu/idegrees/.
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Businesses run on data, and data offers little value without analytics. The ability to process data to make predictions about the behavior of individuals or markets, to diagnose systems or situations, or to prescribe actions for people or processes drives business today. Increasingly many businesses are striving to become “data-driven”, proactively relying more on cold hard information and sophisticated algorithms than upon the gut instinct or slow reactions of humans. This course will focus on understanding key analytics concepts and the breadth of analytic possibilities. Together, the class will explore dozens of real-world analytics problems and solutions across most major industries and business functions. The course will also touch on analytic technologies, architectures, and roles from business intelligence to data science, and from data warehouses to data lakes. And the course will wrap up with a discussion of analytics trends and futures.
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This course introduces students to marketing analytics as a data-driven approach to solving real-world marketing problems. It covers four key areas: causal analysis (identifying cause-and-effect in marketing interventions), predictive modeling and AI (forecasting customer behaviors using machine learning), social media analysis (extracting insights from online consumer interactions through text and network analysis), and consumer demand and preference analysis (estimating preferences, demand, and customer lifetime value). Students will gain hands-on experience using Python to analyze diverse data sources, apply advanced analytics techniques, and generate actionable insights to support strategic marketing decisions.