Network Analysis for Marketing Analytics

Network analysis is a long-standing methodology used to understand the relationships between words and actors in the broader networks in which they exist. This course covers network analysis as it pertains to marketing data, specifically text datasets and social networks. Learners walk through a conceptual overview of network analysis and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

• Network Analysis Introduction and Terminology
• In this module, we will learn the key concepts in network analysis and the key terminology, including semantic and social networks. We will also survey common network analyses in marketing.
• Network Analysis Data Structures and Calculations
• In this module, we will learn how networks are prepared and the common data formats that represent networks. We will learn the differences between different network calculations and how networks are presented visually.
• Preparing and Visualizing Social Networks
• In this module, we will learn how to parse tweet JSON, extract mentions and text, load connections into edge lists, and visualize the network in Google Colab.
• Preparing and Visualizing Semantic Networks
• In this module, we will learn how to parse tweet JSON, process text into features, load connections into edge lists, and visualize the network in Google Colab.