Overview
Google, IBM & Meta Certificates – 40% Off
One plan covers every Professional Certificate on Coursera.
Unlock All Certificates
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.
Syllabus
- Course 1: Supervised Text Classification for Marketing Analytics
- Course 2: Unsupervised Text Classification for Marketing Analytics
- Course 3: Network Analysis for Marketing Analytics
Courses
-
Build reliable supervised classifiers from marketing text and defensible human labels. Learners create coding rules, reconcile coders into gold-standard labels, transform text into predictive features, train a regularized elastic-net model, separate training from validation evidence, and use errors and learning curves to judge model quality and the value of collecting more labeled data.
Taught by
Chris J. Vargo and Scott Bradley