Course Materials

Material status
Required
Optional

# Module 01

Introduction

Learning objectives: Distinguish statistical modeling, machine learning, and artificial intelligence; compare prediction-focused and explanation-focused analysis; explain how analytical goals, assumptions, data, and evaluation criteria shape a quantitative research workflow.

Before class

During class

After class

# Module 02

Machine Learning for Classification

Learning objectives: Distinguish classification from regression and supervised from unsupervised learning; explain how decision trees use entropy and information gain to select splits; describe bagging and random forests; evaluate binary classifiers using a confusion matrix, accuracy, precision, recall, and F1.

Before class

During class

After class

# Module 03

Logistic Function

Learning objectives: Convert among probabilities, odds, and log-odds; explain how the logistic function maps a linear predictor to a probability; fit binary logistic regression and interpret coefficients, odds ratios, and predicted probabilities; explain maximum likelihood estimation and evaluate model predictions.

Before class

During class

After class

# Module 04

Ordinal Logistic Regression

Learning objectives: Identify when an ordinal outcome calls for ordinal logistic regression; explain cumulative logits and the proportional-odds assumption; fit an ordinal logistic regression model and interpret its coefficients, odds ratios, and predicted probabilities; assess model assumptions and communicate results for ordered outcomes.

Before class

After class

# Module 05

Workshop 1: Proposal/Pitch

Learning objectives: Clarify research questions, assess project feasibility, and offer constructive feedback to prioritize proposal revisions.

Before class

After class

# Module 06

Multinomial and Count Models

Learning objectives: Distinguish multinomial logistic regression from binary and ordinal models; fit a multinomial logistic regression model using an appropriate reference category; interpret coefficients, relative risk ratios, and predicted probabilities for nominal outcomes.

Before class

After class

# Module 07

NLP Foundations

Learning objectives: To be added.

Before class

    # Module 08

    NLP Applications

    Learning objectives: To be added.

    Before class

      # Module 09

      Machine Learning for Regression

      Learning objectives: To be added.

      Before class

      After class

      # Module 10

      Workshop 2: Data & Methods

      Learning objectives: To be added.

      Before class

        # Module 11

        Factor Analysis and PCA

        Learning objectives: To be added.

        Before class

          # Module 12

          Structural Equation Modeling

          Learning objectives: To be added.

          Before class