SDSU HealthLINK Center for Transdisciplinary Health Disparities Research
SDSU HealthLINK Center for Transdisciplinary Health Disparities Research
SDSU HealthLINK Center
Data Science Training Initiative (DSTI)
Augmenting research capacity through advanced data science methodologies, machine learning, and hands-on training for SDSU, San Diego State University Imperial Valley (SDSU IV), and community partners.
About the Program
Funded by the National Institute on Minority Health and Health Disparities of the National Institutes of Health (U54MD012397), our Data Science Training Initiative develops and delivers comprehensive training programs to enhance research capacity across SDSU, SDSU IV, and community partner organizations using advanced data science methodologies including machine learning.
Collaborative Learning
Hands-on training bringing together researchers from SDSU, SDSU IV, and community partner organizations.
Expert Instruction
Learn from experienced faculty using industry-standard tools like MATLAB, AutoML, and Python frameworks.
Practical Application
Work with real datasets and develop skills through in-class activities and hands-on exercises.
Training Workshops
Comprehensive hands-on workshops designed to build data science and machine learning capabilities for health research applications.
Introduction to Machine Learning
- September 8, 2025
- 1:00 PM - 4:00 PM
- September 15, 2025
- 1:00 PM - 4:00 PM
- SDSU HealthLINK Center, Suite 220-01
This two-day (6-hour) workshop covers elementary machine learning techniques in MATLAB and AutoML, utilizing MATLAB Statistics, Machine Learning Toolbox, and Deep Learning Toolbox. Attendees will learn how to use unsupervised learning to uncover features in large datasets and supervised learning to develop prediction models through examples and in-class hands-on activities.
Introduction to Data Science with Python
- September 8, 2025
- 1:00 PM - 4:00 PM
- September 8, 2025
- 1:00 PM - 4:00 PM
- SDSU HealthLINK Center
This two-day (6-hour) training will cover Least Squares Regression for Linear and Nonlinear Functions, Interpolation, Dimensionality reduction using PCA, LDA, and NCA, Gaussian Processes for regression and classification, Time series analysis with RNNs using LSTM and GRU architectures, and 1D CNNs. Learn to generate estimation functions, design GP models, and implement RNNs for sequential data processing.