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.
Network Analysis for Marketing Analytics
Network Analysis for Marketing Analytics
This course is part of Text Marketing Analytics Specialization
Instructors: Chris J. Vargo
Access provided by SGCSRC
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
3 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
12 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Text Marketing Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 6 modules in this course
Let's get situated with the course.
What's included
4 readings1 discussion prompt
Let's start by reviewing some advanced concepts in supervised machine learning and how they apply to building state of the art classification models for real world marketing purposes
What's included
1 video1 reading2 assignments
We'll tackle using k-train, a light weight Google tensor flow wrapper, to build a custom model that classifies data based on context. These lightweight models may not perform as well as large language models, but they are lightweight and can be highly effective.
What's included
2 readings2 assignments
Moving further down the model size and technological advancement line, we will introduce generative AI, and how these very large models can help us classify data.
What's included
1 video2 readings3 assignments
Use large language models that are open source and free to all to classify data and solve problems. We will focus on smaller models that fit inside of Google Colab and are free to anyone with a Google account!
What's included
3 readings3 assignments
In this module we'll learn how to make LLMs of our own by fine tuning models to our specific use cases. By taking models that are already generally smart and adapting them to our examples of known inputs and outputs, we can quickly build highly intelligent classifiers that solve specific, tricky problems!
What's included
2 readings2 assignments
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Build toward a degree
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
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