Logistics

• Teaching Team:

  • Prof: Temiloluwa Prioleau

  • Grad: Wayner Barrios Quiroga

  • Postdoc: Prajakta Belsare

• Class:

  • Time: 10A (Tues. & Thurs. 10:10am - 12N)

  • Location: ECSC 008

• Office Hours/Working Session:

  • Fri. 3:30pm - 4:20pm (during x-hours)

  • Location: ECSC 008

Course Overview

This course will cover state-of-the-art methods for data acquisition and analysis in a range of application domains such as cancer, cardiovascular diseases, diabetes, mental health, etc. Students will develop their skills by reading, presenting, and critiquing seminal research papers. The course will include assignments and a term-long project to reinforce concepts and methods widely used in data science.

Structure

• It is critical to note that this is an advanced course that will be conducted in a seminar-style format.

• A large amount of self-learning is required in this course. This includes digging into the details of data science methods presented in the readings to know when and how to use such methods in other applications.

Prerequisites

• COSC 74 (Machine Learning and Statistical Data Analysis) or instructor's permission

• Experience with Python for data wrangling (COSC 1 alone is not sufficient)