Program Description
The Data Science in Astronomy Minor is designed for students interested in the intersection and applications of data and computer science with topics related to astronomy.
Open to all Cornell undergraduates not enrolled in an Astronomy major. Minimum grades:
- B- in 1000–2000 level courses
- C- in 3000-level and above
- Up to 4 credits of ASTRO 4940 Independent Study in Astronomy may be applied with approval of the Astronomy Director of Undergraduate Studies
To apply for an Astronomy Minor make an appointment to visit the Director of Undergraduate Studies (astrodus@cornell.edu).
Minor Requirements
- 15 total credits
- 1 Astronomy course at or above 3000 level
- 2 Astronomy data analysis courses
- 2 Data Science courses
- 1 Probability and Statistics
- 1 Data Structures/Machine Learning
The Data Science in Astrology minor provides a cross-disciplinary framework linking selected Astronomy courses with appropriate courses in Computer Science, ECE, Information Science, Statistics and Data Science, and ORIE including:
Astronomy
Course List Code | Title | Hours |
ASTRO 3301 | Exoplanets and Planetary Systems | 3 |
ASTRO 3302 | The Life of Stars: From Birth to Death | 3 |
ASTRO 3303 | Galaxies Across Cosmic Time | 3 |
Astronomy Data Oriented
Course List Code | Title | Hours |
| 6 |
| Planetary Image Processing with MATLAB | |
| Data Analysis and Research Techniques in Astronomy | |
| Symbolic and Numerical Computing | |
| Multiwavelength Astronomical Techniques | |
| Modeling, Mining and Machine Learning in Astronomy | |
Computer Science, ECE, Information Science, Statistics and Data Science, and Operations Research and Information Science (ORIE) 1
Course List Code | Title | Hours |
| 3 |
| Statistics I | |
| Introduction to Probability and Inference for Random Signals and Systems | |
| Eng Probability and Statistics: Modeling and Data Science | |
| Basic Probability | |
| Statistics | |
| Eng Probability and Statistics: Modeling and Data Science II | |
| Probability Models and Inference | |
| Statistical Computing | |
STSCI 4780 | Bayesian Data Analysis: Principles and Practice | 4 |
| 3 |
| Object-Oriented Programming and Data Structures | |
| Data Structures and Functional Programming | |
| Data Science for Engineers | |
| Introduction to Data Science | |
| Practical Tools for Operations Research, Machine Learning and Data Science | |
| Statistical Data Mining I | |
| Info Theory, Probabilistic Modeling, and Deep Learning with Scientific and Financial Apps | |