Courses at UMN:

  • AST1001 Introductory Astronomy
  • AST 4031 Interpretation and Analysis of Astrophysical Data

    Modern astrophysics research relies on sophisticated statistical methods to interpret and analyze the large amount of data characteristic of new experiments. In this course, students will be introduced to statistical analysis techniques to interpret/analyze large data sets from astrophysical experiments. Applications of the principles/methods of analysis to current research will be covered. For senior undergraduate/graduate students in Physics/Astronomy.

    In particular, the first part of the course will cover probability theory and the foundation of statistical inference: hypothesis testing, estimation, modeling, resampling and Bayesian inference, as well as probability distributions and non parametric statistics. The second part of the course will deal with applied statistical techniques that are based on these foundations, including: data smoothing and density estimation, regression (least-square linear regression, weighted least-squares, non linear models), multivariate analysis, clustering, classification and data mining. For each applied statistical technique, the astronomical context will be emphasized with examples based on specialized literature. The statistical methods learned during the course will be put into practice using real-world data sets and python-based codes.

    Course Format

    The course is a mixture of lectures and practical sessions (held in the computer lab). There will be a range of practical sessions and problem solving classes, including review of homework, and guided computer practical sessions. Students are expected to invest additional practical time outside scheduled sessions to consolidate their work.