Data Analysis
Fall 2019, CMU 10718

Lectures: MW, 4:30-5:50pm, 4307 Gates and Hillman Center (GHC)

Instructor: Leila Wehbe

Assistant Instructor: Fabricio Flores

Teaching Assistants:
Aria Wang
Jacob Tyo

Communication: Piazza will be used for discussion about the course and assignments.

Office Hours:
  • Leila: by appointment
  • Fabricio: TBA
  • Aria: TBA
  • Jacob: TBA

Course Description

In this course students will gain exposure to practical aspects of machine learning and statistical data analysis. Through a series of case studies of real problems, students will learn to appreciate the intricacies involved in the practical application of machine learning. The course will focus on formalizing research questions, data exploration, identifying potential pitfalls, using machine learning for science and decision making, reproducibility and fairness. The outcome of the course will be a write up of the various case studies that will be shared between all students and possibly posted online (subject to agreement between students).

Course components

Please refer to the syllabus for details of the course graded components.


This is a breakdown of the topics with a sample of the suggested readings. Every week, there will be two specific papers assigned to read (or one paper and one or a couple newspaper articles). The assigned papers will be communicated on Piazza. Students are encouraged to suggest other reading material or problems from their research.

Date Note Topic     Resources


08/26 Lecture 1: Introduction - slides
08/28 Lecture 2: Introduction
09/02 Labor day, no class
09/04 Lecture 3: Introduction

Week 1 - Formulating a research question


Week 2 - The importance of domain expertise and understanding your data


Week 3 - Data exploration


Week 4 - Evaluating classifiers, robustness


Week 5 - Constructing a baseline


Week 6 - Overfitting


Week 7 - Methods and Results Reproducibility


Week 8 - Model Interpretability


Week 9 - Causality


Week 10 - Fairness and bias


Week 11 - Accountability, transparency, Societal impacts


Week 12 - Introduction / Conclusion


Course Policies

Please refer to the syllabus for details of the course policies.

Accommodations for Students with Disabilities

If you have a disability and are registered with the Office of Disability Resources, I encourage you to use their online system to notify me of your accommodations and discuss your needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, I encourage you to contact them at

Take care of yourself

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
  • CaPS: 412-268-2922
  • Re:solve Crisis Network: 888-796-8226

If the situation is life threatening, call the police
  • On campus: CMU Police: 412-268-2323
  • Off campus: 911