Network Analysis for Marketing Analytics
Master advanced network analysis techniques using Python for marketing data interpretation and social network analysis in business applications.
Master advanced network analysis techniques using Python for marketing data interpretation and social network analysis in business applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Text Marketing Analytics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Understand network analysis fundamentals and terminology
Create and visualize networks from marketing data
Analyze social networks using Python and related tools
Extract valuable marketing insights from network analysis
Implement semantic network analysis for text data
Skills you'll gain
This course includes:
1.3 Hours PreRecorded video
3 quizzes, 2 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores network analysis methodology for marketing applications, focusing on text datasets and social networks. Students learn to analyze relationships between words and actors in broader networks using Python. The curriculum covers both conceptual understanding and practical implementation, including data preparation, visualization, and analysis of both social and semantic networks. Through hands-on projects and tutorials, learners master techniques for extracting marketing insights from network data.
Network Analysis Introduction and Terminology
Module 1 · 1 Hours to complete
Network Analysis Data Structures and Calculations
Module 2 · 59 Minutes to complete
Preparing and Visualizing Social Networks
Module 3 · 4 Hours to complete
Preparing and Visualizing Semantic Networks
Module 4 · 4 Hours to complete
Fee Structure
Instructors
Associate Professor
Dr. Chris J. Vargo is an Associate Professor at the University of Colorado Boulder, specializing in data analytics and digital advertising. His research integrates computer science techniques to analyze social media data through the lenses of communication, psychology, and political science. With expertise in data mining, machine learning, and predictive analytics, Dr. Vargo employs advanced methodologies such as network analysis and information retrieval to investigate contemporary media landscapes. His goal is to enhance the quantitative analytical skills of students, preparing them for the demands of the industry.In the classroom, Dr. Vargo teaches various courses related to advertising analytics at both undergraduate and graduate levels, including "Introduction to Digital Advertising" and "Programmatic Advertising." He also directs the CMCI/Leeds Marketing and Business Analytics partnership, fostering collaboration between departments. His scholarly contributions have been published in notable journals such as the Journal of Communication and New Media & Society. Additionally, he serves as the Editor of The Agenda Setting Journal, focusing on agenda-setting theory in new media contexts. With a robust academic background that includes a Ph.D. from The University of North Carolina at Chapel Hill and practical experience in public relations and digital marketing, Chris J. Vargo is dedicated to advancing research and education in the rapidly evolving field of digital advertising.
Senior Engineer
Scott Bradley is an Instructor at the University of Colorado Boulder, specializing in marketing analytics and data science. He teaches several courses, including "Network Analysis for Marketing Analytics," "Supervised Text Classification for Marketing Analytics," and "Unsupervised Text Classification for Marketing Analytics." His expertise lies in applying advanced analytical techniques to enhance marketing strategies and improve decision-making processes within organizations.With a strong background in data analysis and machine learning, Scott combines theoretical knowledge with practical applications to equip students with the skills necessary for success in the rapidly evolving field of data science. His courses focus on the intersection of data analytics and marketing, providing students with valuable insights into how data can drive effective marketing strategies. Through his teaching, Scott Bradley plays a crucial role in preparing the next generation of data-driven marketers at CU Boulder.
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