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.
Coursera
Discover the power of Network Analysis in Marketing Analytics with our expert-led course on Coursera. Gain insights into relationships within your data through text datasets and social networks, all while learning practical skills in Python. Perfect for marketers looking to leverage advanced analytics for strategic decision-making.
Coursera
Discover how to harness the power of unsupervised machine learning to analyze and classify vast amounts of text data relevant to marketing analytics. In this course, you'll learn to train algorithms that can autonomously extract topics and derive actionable insights from your marketing datasets. Perfect for marketers, analysts, and anyone looking to leverage deep learning in their field.