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Network Analysis for Marketing Analytics

University of Colorado Boulder via Coursera

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Overview

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Use generative AI and large language models as auditable classifiers for marketing text. Learners connect language-model training and contextual embeddings to classification, design strict prompts and machine-readable label contracts, validate outputs before batch inference, compare prompting with fine-tuning, and evaluate results against audited gold-standard labels using accuracy, macro F1, and class-level errors.

Syllabus

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.