Intro Data Science
Builds the foundation: Python statistics, research concepts, GLM, introductory machine learning, data acquisition with AI, t-tests, F-tests, ANOVA, and lab practice.
View folderMeasurement Files
This page is dedicated to a large data-science, statistics, and psychometrics repository I developed over years of teaching data science, taking my PhD qualification exam in measurement, and taking courses in related topics. The psychometrics notebooks were built in collaboration with Sruthi Bommareddy.
Project Overview
Research Resources is a collection of applied notebooks I developed for research and data science courses. The project translates the data science workflow into teachable, reusable modules: introducing Python, building statistical foundations, moving into machine learning, and extending into specialized research domains such as psychometrics and audio analysis.
The curriculum is designed for students and applied researchers as a reference to use alongside applied projects. Each notebook frames data science as a practical research process: acquire data, clean and structure it, select an appropriate model, evaluate results, and communicate evidence clearly.
Core Competencies Demonstrated
Applied Statistical Modeling
Introductory and intermediate notebooks cover t-tests, F-tests, ANOVA, regression, GLM concepts, mediation, moderation, and model comparison.
Machine Learning Workflow
Modules introduce scikit-learn, prediction, classification, support vector machines, decision trees, random forests, ensemble methods, regularization, and feature engineering.
Psychometrics and Measurement
Specialized notebooks cover classical test theory, reliability, validity, item response theory, item information, differential item functioning, person fit, and computer adaptive testing.
Research Translation
The materials are structured as teaching resources, making complex analytics approachable for students in psychology, business, marketing, UX, and data science.
Advanced Modeling with General Linear Models
Advanced modules move into neural networks, recurrent architectures, transfer learning, AutoML, and interactive machine learning.
Domain-Specific Analysis
The repository includes an audio-analysis notebook, showing how the same computational approach can be adapted for specialized signal and behavior data.
Notebook Curriculum
Intermediate Data Science
Expands into scikit-learn, regression, mediation and moderation, time series, dimensionality reduction, LDA, SVMs, trees, forests, ensembles, regularization, and labs.
View folderAdvanced Data Science
Introduces deeper machine-learning topics including neural networks, LSTMs and GRUs, transfer learning, AutoML, and interactive machine learning.
View folderPsychometrics and Audio Analysis
Extends data science into measurement science and specialized signal analysis, including CTT, IRT, SEM, mixture modeling, and audio sampling concepts.
View psychometrics