DSC-2025-27 | Applied Statistical Learning using R

Why is the topic important?

Statistical learning, using R, provides a modern toolkit for modeling, prediction, and inference. It bridges the gap between traditional statistics and machine learning, offering powerful methods for analyzing complex data. Mastering concepts such as linear regression, generalized linear models, resampling techniques, and survival analysis in R is crucial for carrying out high-quality analysis, especially in data-driven fields.

Workshop Goal

• Understand the foundations of statistical learning and its role in modern research.
• Apply linear regression and generalized linear models using R.
• Learn and implement resampling methods such as cross-validation and bootstrap.
• Analyze survival and censored data using Kaplan-Meier estimators and Cox models.
• Gain practical experience with R for hands-on data analysis.

Workshop Content

This 1.5-day workshop introduces participants to the key concepts and methods in statistical learning using R. Sessions combine lectures with hands-on exercises, ensuring that participants not only understand the theory but also gain practical coding skills for analyzing data.

Covered topics:

• What is Statistical Learning: Overview of prediction, inference, and the trade-off between accuracy and interpretability.
• Linear Regression: Modeling relationships between variables and assessing model assumptions.
• Generalized Linear Models (GLM): Extending regression to binary and count data (logistic and Poisson regression).
• Resampling Methods: Cross-validation and bootstrap techniques for model validation and selection.
• Survival and Censoring Data: Analyzing time-to-event data with Kaplan–Meier and Cox proportional hazards models.

Target Audience & Prior Knowledge

This workshop is designed for a broad audience, including Data Analysts, Data Scientists, Researchers, Biologists, Economists, and others. Basic familiarity with R is recommended, but the workshop will include guided exercises to support participants with varying levels of prior experience.

Technical Requirements

• Participants are requested to bring their own laptop for the lab sessions and ensure that R and RStudio are installed. Additionally, participants should have an internet connection available to download data and additional packages during the workshop.
• Participants should have a fundamental understanding of data science and R programming, along with a strong interest in scripting and programming in R.

About the Trainer

Dr. Maryam Movahedifar is a data scientist for training and consulting at the DSC.