Statistics AN I

Admission requirements

None

Description

In the era of data science the responsible, principled and accurate use of statistics is of very high importance. You will learn about the foundations of probability theory and statistics with a special focus on applications to astronomical research.

This course introduces the basic concepts and language of probability theory, statistical models, and the basics of estimation and uncertainty quantification. The course lies the foundation for advanced topics in statistical modeling, which are covered in the follow-up course Statistics AN II.

You will apply the new theory during practical classes by solving exercises using both analytical computations and programming with Python.

The course covers the following themes:

• Basics of probability theory

• Statistical models and distributions

• Estimation methods

• Uncertainty quantification (confidence intervals)

Course objectives

The main objective of this course is to give you an overview of the basic concepts of statistics and probability theory. After this course, you will be able to:

• Speak, understand, and reason in probabilistic language

• Answer basic questions on the following statistical topics: estimation methods, uncertainty, confidence intervals

• Carry out a preliminary analysis of astronomical data using analytical computations and the programming language Python.

• Develop critical thinking about statistical data analysis (e.g. you will be aware of possible pitfalls and be able to detect inaccurate or incomplete analysis)

Soft skills

In this course, students will be trained in the following behaviour-oriented skills:

• Problem solving (recognizing and analyzing problems, solution-oriented thinking)

• Analytical skills (analytical thinking, abstraction, evidence)

• Structured thinking (structure, modulated thinking, computational thinking, programming)

• Complex ICT-skills (data analysis, programming, simulations, complex ICT applications)

• Critical thinking (asking questions, check assumptions)

• Integrity (honesty, moral, ethics, personal values)

Mode of instruction

• Lectures

• Exercise classes

• Interactive case study on astronomical applications

Assessment method

• Written exam

Reading list

• Lecture notes (distributed via Brightspace)

• All of Statistics. Larry Wasserman (corrected second printing, 2005), ISBN 9780387402727. Click title to download electronic version through Leiden University Libraries.

Contact

Contactdetails teacher: Dr. M. Cautun , Dr. T.W. Nagler