What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
Article
This course is part of Text Marketing Analytics Specialization
Instructors: Chris J. Vargo
Included with
Recommended experience
Intermediate level
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Recommended experience
Intermediate level
Basic Python proficiency, including Python's built-in functions, logic, and data structures, is recommended.
Describe the concept of network analysis and related terminology
Apply network analysis to marketing data via a peer-graded project
Visualize a network based on centrality and other statistics via homework
Extract marketing insights from a network via a peer-graded project
Add to your LinkedIn profile
3 assignments
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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.
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.
2 videos3 readings1 discussion prompt
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.
2 videos1 reading1 assignment
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.
1 video2 readings1 assignment1 programming assignment
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.
1 video2 readings1 assignment1 programming assignment
CU Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
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Rutgers the State University of New Jersey
Specialization
Rutgers the State University of New Jersey
Course
University of Colorado Boulder
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This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
University of Colorado Boulder
Degree · 2 years
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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