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Chris J. Vargo and Scott Bradley

Marketing data are complex and have dimensions that make analysis difficult. Large unstructured datasets are often too big to extract qualitative insights. Marketing datasets also are relational and connected. This specialization tackles advanced advertising and marketing analytics through three advanced methods aimed at solving these problems: text classification, text topic modeling, and semantic network analysis. Each key area involves a deep dive into the leading computer science methods aimed at solving these methods using Python. This specialization 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.

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What's inside

Three courses

Supervised Text Classification for Marketing Analytics

Marketing data often requires categorization. In today’s age, marketing data can also be very big. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python.

Unsupervised Text Classification for Marketing Analytics

Marketing data is often too large for humans to analyze. This course uses unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners will gain a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python.

Network Analysis for Marketing Analytics

Network analysis is a methodology to understand relationships between words and actors in broader networks. 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.

Learning objectives

  • Understand the concepts of topic modeling, text classification, and network analysis
  • Learn to use topic modeling on large unstructured text datasets
  • Learn to use network analysis to create network graphs, produce network statistics, and extract qualitative insights

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