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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 often involve relational and connected and involve networks. 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.
Overview
Syllabus
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Course 1: Supervised Text Classification for Marketing Analytics
- Offered by University of Colorado Boulder. Marketing data often requires categorization or labeling. In today’s age, marketing data can also ... Enroll for free.
Course 2: Unsupervised Text Classification for Marketing Analytics
- Offered by University of Colorado Boulder. Marketing data is often so big that humans cannot read or analyze a representative sample of it ... Enroll for free.
Course 3: Network Analysis for Marketing Analytics
- Offered by University of Colorado Boulder. Network analysis is a long-standing methodology used to understand the relationships between ... Enroll for free.
