Data Analysis for Astronomy and Physics
Msc and BSc course, Goethe-University Frankfurt, IAP, 2025
Full course, open to BSc and MSc students. 
Course page, QIS/LSF Link
Course Description
The topics covered in the lecture are listed here. The corresponding lecture notes will be available before the lecture. Please work through the materials before the lecture! The focus of the lecture will be on the application of selected content. The lecture material will be deepened based on pre-calculated examples. Furthermore, the concrete applications should stimulate discussion and the methods should be questioned.
It is important that you come prepared to the lecture so that we have sufficient time to deepen the teaching material.
Content presented during the lecture will be recorded (video, audio, and text) and made available electronically afterwards.
The script is written in English and is provided as PDF and as a Mathematica Notebook. However, the respective German technical terms will also be mentioned during the lecture.
You can also view Mathematica Notebooks without a Mathematica license using the CDF Player.
Syllabus
Syllabus (PDF), Mathematica Notebook
Still unsure if the course is right for you? The following quiz (PDF, NB) offers an initial self-assessment. If you can answer the questions, you are already familiar with some concepts covered in the lecture. If you are uncertain about many answers, the lecture will help you deepen your understanding.
The videos are also collected in this YouTube Playlist.
For in-depth preparation, I try to link external material on selected topics. Examples include educational videos, such as those by Prof. Jörn Loviscach, and others.
Lecture Schedule
1. Introduction to Data & Data Visualization
Date: April 22, 2025 (Q & A: April 29, 2025)
Lecture Notes:
- Organization: Syllabus (PDF), Mathematica Notebook
- Unsure whether to take the course? Take the Quiz
- Introduction to Data (PDF), Mathematica Notebook
- Data Visualization (PDF), Mathematica Notebook
Video Lectures:
- Introduction to Data (Part 1) (24:04)
- Introduction to Data (Part 2) (13:17)
- Data Visualization (Part 1) (21:24)
- Data Visualization (Part 2) (23:57)
- Data Visualization (Part 3) (09:46)
- Data Visualization (Part 4) (10:04)
Additional Materials:
- NEW: TED Talk - How to spot a misleading graph (4:09)
- CSE512 Data Visualization (Winter 2014), University of Washington
- Khan Academy:
- Creating and interpreting scatterplots
- Box and whisker plots
- Frequency tables & dot plots
- Creating a histogram
- Interpreting a histogram
Assignments: Assignments are shared and submitted via GitHub Classroom.
- Invitation link for a starter assignment to learn GitHub basics
- Invitation link for Assignment 1: Intro to data structure and data visualization
Data:
Q & A Materials:
- Plot-data: 2.9,2.45,2.5,3.5,3.7,3.0,1.75,0.75,0.3,0.2,1.9,1.6,0.5,-0.5,-2.8
- Monthly temperature records (CSV)
- Temperature station list (CSV)
- Labor cost chart (PNG)
2. Statistics
Date: April 29, 2025 (Q & A: May 6, 2025)
Lecture Notes:
Video Lectures:
- Statistics (Part 1: Statistics of location) (34:47)
- Statistics (Part 2: Statistics of scatter) (15:47)
- Statistics (Part 3: Samples & bias) (21:07)
Additional Materials:
- Variance, standard deviation (13:37)
- Calculating variance, standard deviation (13:13)
- Normal distribution, central limit theorem (14:58)
- Probability concepts, frequency, Bayes, Laplace
- Kolmogorov axioms of probability
- Variance, standard deviation of a random variable (36:55)
- Averaging reduces variance and standard deviation (21:30)
- Three coins; expected value of the standard deviation of the sample (18:46)
- Standard deviation of lifetime (14:14)
- Expected value, variance, standard deviation for discrete and continuous distributions (18:35)
- Expected value, variance, standard deviation of three coins (13:13)
Q & A Materials:
3. Probabilities
Date: May 6, 2025 (Q & A: May 13, 2025)
Lecture Notes:
- Probabilities Part 1 (PDF)
- Probabilities Part 1 (NB)
- Probabilities Part 2 (PDF)
- Probabilities Part 2 (NB)
Video Lectures:
- Probabilities A 1/5
- Probabilities A 2/5
- Probabilities A 3/5
- Probabilities A 4/5
- Probabilities A 5/5
- Probabilities B 1/4
- Probabilities B 2/4
- Probabilities B 3/4
- Probabilities B 4/4
Additional Materials:
- Probability concepts, frequency, Bayes, Laplace
- Kolmogorov axioms of probability
- Khan Academy:
- Bozeman Science: Standard Error
- Additional probability examples and explanations