ADEC 7430 Big Data Econometrics
This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, as the rapid advancement of computational methods provides unprecedented opportunities for understanding "big data." This course will provide a hands-on experience with the terminology, technology, and methodologies behind machine learning with economic applications in marketing, finance, healthcare, and other areas. The main topics covered in this course include: advanced regression techniques, resampling methods, model selection and regularization, classification models (logistic regression, Naïve Bayes, discriminant analysis, k-nearest neighbors, neural networks), tree-based methods, support vector machines, and unsupervised learning (principal components analysis and clustering). Students will apply both supervised and unsupervised machine learning techniques to solve various economics-related problems with real-world data sets. No prior experience with R or Python is necessary.
Course overview
- Department
- Advancing Studies
- School
- ADV
- Credits
- 3
- Level
- Graduate
- Offered
- Periodically in the Fall
Catalog details
- Prerequisites
- ADEC7301/ADAN7301 or ADEC7310/ADAN7309 or ADAN7310/ADEC7309
Requirements fulfilled
No source-backed degree requirement is attached to this course yet.
Official evaluation summary
Data freshness
Instructors
- Arvind Sharma
- Ashuraj Sirohi
- Haydar Evren
- Nathaniel Bastian
- Razvan Veliche
- Stefano Parravano
Sections
No current section details are available.