In this course, you will be introduced to the importance of gathering, cleaning, normalizing, visualizing and analyzing data to drive informed decision-making, no matter the field of study. You will learn to use a combination of tools and techniques, including spreadsheets, SQL, and Python to work on real-world datasets using a combination of procedural and basic machine learning algorithms. You will also learn to ask good, exploratory questions and develop metrics to come up with well thought-out analyses. Presenting and discussing analyses of datasets you have chosen will be an important part of the course.
Students without programming experience interested in data science may want to consider ES 2114 instead of this class.
Learning Outcomes:
Delivery Method: Hybrid in-person and remote, with faculty in-person
Prerequisites:Prior coursework in computer science or permission of instructor.
Course Level: 4000-level
Credits: 4
M/Th 10:00AM - 11:50AM (Full-term)
Maximum Enrollment: 16
Course Frequency: Once a year
Categories: All courses , Computer Science , Hybrid In-Person and Remote , Mathematics , Physics
Tags: