DA 351 - Advanced Descriptive Methods for Data Analytics

Advanced Descriptive Methods, in parallel with DA 352 and 353, is designed to develop students' understanding of the cutting-edge methods and algorithms of data analytics and how they can be used to answer questions about real-world problems. While all advanced methods for Data Analytics can be applied in a variety of capacities, descriptive analytics emphasizes using Natural Language Processing (NLP) methods to work with text as data, modeling for interpretability, and designing and deploying Computer Vision (CV) systems. In DA 351, students will examine both supervised and unsupervised methods, including topics such as advanced regression, K nearest neighbors, hierarchical clustering, ranked cosine similarity, and deep learning.

Shared Learning Goals for All Advanced Methods Courses:

1. assess model performance under uncertainty
2. align research questions, target data, and methods
3. handle large data and/or computations by writing efficient code
4. remain flexible when encountering and adopting new models and methods
5. maintain well-organized code, project spaces, and documentation

Specific Learning Goals for Advanced Descriptive Methods:

1. establish a working toolkit for advanced data analytics in Python
2. gain experience selecting supervised and unsupervised machine learning models for interpretability, and interpreting the results when such models are used
3. apply natural language processing (NLP) and computer vision (CV) strategies in Data Analytics contexts

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