Arman Sabbaghi: Teaching

Teaching

I seek to foster an appreciation of the applications of statistics in every class that I teach. Specifically, in my lectures I emphasize deep conceptual understanding (as opposed to mere knowledge) of statistical methodologies that is crucial for their successful application, and integrate real data with a context and purpose. I also design the coursework and group projects in my classes so as to promote statistical literacy and thinking, the thoughtful collection of real data, and the investigative process of problem-solving and decision-making inherent in statistical applications. The ultimate positive impact of my fostering this appreciation in my classes is that my students learn how to apply statistics to address difficult real-life questions across a wide range of disciplines.

Further information on the courses that I have taught can be found in the syllabi below.

Files for the 2022 Purdue Krannert-Statistics Causal Machine Learning for Novel Settings Boot Camp

Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference

Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference - Jupyter Notebook

Lalonde_data.csv

Lalonde_experiment_data.txt

Lalonde_observational_data.txt

Files for the Fall 2021 Distinguished Seminar Series

The Epiphanies of Sir R.A. Fisher and Jerzy Neyman for Causal Inference

The Epiphanies of Sir R.A. Fisher and Jerzy Neyman for Causal Inference - Jupyter Notebook

Lalonde_experiment_data.txt

Files for the 2021 Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp

Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference