**Adv Big Data Economics**(ECON 5314G- Ph.D.)**Big Data Economics**(ECON 4314 / CMDA 4314 – Upper-level undergraduate)**Data Science for Quantitative Finance**(ECON 4984 / CMDA 4984 – Upper-level undergraduate)**Quantitative Economics with Python**(ECON 4984 – Undergraduate)**Machine & Deep Learning from Theory to Practice**(Module for M.Tech Data Science – Masters)**Machine Learning & Data Classifiers**(Module for program In Business Analytics – Postgraduate)**AI-Based Algorithmic Trading using Python**(Practitioners)**Forecasting in Finance**(Practitioners)**Machine Learning for Econometricians (Practitioners)**

## Adv Big Data Economics (ECON 5314G – Ph.D.)

*Click here for a full course description.*

This intermediate applied econometrics course covers the theoretical, computational, and statistical underpinnings of big data analysis. The focus will be the econometric models and machine learning techniques to analyze the high-dimensional data sets a.k.a. “Big Data” and their implications in research focusing on interesting economic questions that arise from considering the rapid changes in data availability and computational technology. Big data econometric models provide a vehicle for modeling and analyzing complex phenomena and for incorporating rich sources of confounding information into economic models. The goal of this course is to give an applied, hands-on introduction to these methods. At the end of the course, students will be able to read and understand theoretical papers on the subject, implement the techniques themselves in Python, and apply the techniques to data used in economics and business. The data sets we will use for this course are from the World Bank Group, Kaggle, Federal Reserve Economic Data, Google Finance, and several other resources.

Pre: ECON 3254 or ECON 4304 or CMDA 3654 or STAT 3006. (3H, 3C).

**Syllabus:**

*Preliminaries*

- Overview of Big Data and Big Data Visualization
- Python Programming (NumPy, SciPy, pandas, matplotlib, scikit-learn, PyTorch)
- Linear Algebra and Optimization for Machine Learning
- Regression Analysis; (Matrix Formulation, OLS, MLE, SGD, Logistic & Polynomial Regression)
- Bash Scripting and Shell Programming
- High-Performance Computing (VT ARC and Google CoLab)

*Model Selection and Feature Extraction*

- Regression with Many Regressors: Standard Approaches to Model Selection Algorithms
- Penalized Regression Methods: Lasso, Ridge, and Elastic Net
- Linear Dimensionality Reduction with an Emphasis on PCA
- Factor Models; Estimation and Inference
- Economic forecasting in a Big Data environment
- A Brief Introduction to Bayesian Inference and Bayesian VARs

*Deep learning in Big Data Analytics *

- Nonlinearity in Big Data Sets and Nonlinear Dimensionality Reduction
- Neural Networks and Deep Learning Autoencoders
- Double Machine Learning for Treatment and Causal Inference

## Machine Learning and Data Classifiers (C7: Business Analytics)

Finding patterns and relationships in large volumes of data are very useful in marketing, fraud detection, and national security among other applications. Artificial intelligence methods that can lend itself to patterns and relationships in data will be introduced in this module. Applications of classification and learning algorithms will be discussed. The integration of these algorithms to business analytics frameworks will be demonstrated using real-world examples. Different learning techniques like supervised and unsupervised learning, deep learning techniques, text analytics, and recommender systems will be covered.

**Syllabus:**

- Python Crash Course
- NumPy, pandas, matplotlib, SciPy, Sklearn & Pytorch

- Brief Reminders of
- Linear Algebra, Probability Theory, Convex Optimization

- Learning Theory
- Types of Learning (Supervised, Unsupervised, RL), the PAC Learning

- Supervised Learning
- Review of Linear Regression, Least Square Estimation, Logistic Regression

- Moving Beyond Linear Methods
- Polynomial Regression, Regression Splines, Generalized Additive Models,..

- Kernel Methods
- VC-Dimension, Support Vector Machines (SVM)

- Neural Networks and Deep Neural Networks
- Regularization and Model/Feature Selection.
- Classification
- Binary Classification with +/-1 Labels, Multi-Class Classification, Instant-based (e.g. kNN), Generative (e.g. Naive Bayes), Discriminative (e.g. Tree-based methods)

- Unsupervised Learning and Clustering Methods
- k-means Clustering, Hierarchical Clustering, Principal Component Analysis, Autoencoders, and Factor Analysis.

- Reinforcement Learning and Control

- Practical Advice for ML projects
- Examples of AI and Machine Learning in Practice

## Quantitative Economics with Python (ECON 4984 – Online)

This course provides an introduction to exploring, quantifying, and modeling relationships in economic and financial data, by demonstrating techniques such as regression models, and optimization techniques in Python. Combining economic knowledge with Python will allow you to construct some very powerful tools. Python is a widely-used general-purpose programming language, which happens to be well suited to economics, data science, and other more general numeric problems. In this course, you will learn how to pull real-world data from various online APIs like Google or Yahoo! Finance, for historical stock prices, and Federal Reserve and World Bank for macro data. Here you will apply the most powerful modeling tools in the python data science ecosystem, including NumPy, Scipy, pandas, matplotlib, statsmodels, and scikit-learn, to build and evaluate economic models. By exploring the concepts and applications of economic models with python, this course serves as both a practical introduction to programming and as a foundation for learning more advanced modeling techniques and tools in big data and machine learning.