PHYSICS89L
Introduction to Data Analysis, with Python and Jupyter
Physics
H&S - Humanities & Sciences
Course Description
How do we draw conclusions about fundamental physics from experimental data? This course covers basic data analysis techniques and practical statistics used in experimental and computational physics research. Weekly Python-based labs will allow students to explore topics including data visualization, error propagation, evaluating hypotheses, and fitting analytical models. These labs incorporate real and simulated data from existing experiments such as a gamma-ray telescope and a detector that searches for dark matter. Students will learn to use Python libraries running in Jupyter Notebooks to analyze data and will, for example, study the rate at which the universe is expanding using existing data from multiple telescopes. No prior coding experience is required.Corequisite: Physics 71
Grading Basis
RSN - Satisfactory/No Credit
Min
1
Max
1
Course Repeatable for Degree Credit?
No
Course Component
Lab Section
Enrollment Optional?
No
Course Component
Lecture
Enrollment Optional?
No
Courses
PHYSICS89L
is a
prerequisite
for:
Programs
PHYSICS89L
is a
completion requirement
for: