| Day | Time | Lecture Hall | Session Type |
|---|---|---|---|
| Thursday | 10:00–11:30 | XII (Main Building) | Exercises |
| Thursday | 12:00–13:30 | XII (Main Building) | Lecture |
| Friday | 10:00–11:30 | XII (Main Building) | Lecture |
Statistics for Data Analytics
Data Analytics I
Winter 2026 – University of Cologne
Organization of the Course
Statistics for Data Analytics is a graduate-level introductory course in econometrics, focusing on estimation and inference in linear models, with practical illustrations in R.
Timetable
See KLIPS for a detailed schedule.
Note: The 10:00–11:30 session on Thursday, 15 October 2026, will be a lecture instead of an exercise session. The final lecture will take place on Friday, 27 November 2026. There will be no classes on Thursday, 3 December, or Friday, 4 December 2026, because the first exam takes place on 3 December.
Lecture Material
This online script and its PDF version
eWhiteboard lecture and exercise materials (links will be added before the course begins)
Problem Sets and R Scripts (link will be added before the course begins)
ILIAS course (link will be added once the course is available)
Literature
The script is self-contained. To prepare well for the exam, it’s a good idea to study this script.
The course is based on James H. Stock and Mark W. Watson’s Introduction to Econometrics (Fourth Edition). The Stock and Watson textbook is available for download: PDF by chapter (Uni Köln VPN connection required).
Further recommended textbooks are:
- Econometric Theory and Methods, by Russell Davidson and James G. MacKinnon. PDF.
- Probability and Statistics for Economists, by Bruce E. Hansen
- Econometrics, by Bruce E. Hansen
Printed versions of the books are available from the university library.
Assessment
The course will be graded by a 90-minute exam. For detailed information, please consult ILIAS once the 2026 course is available.
Communication
An ILIAS Statistics Forum will be available for discussing lecture topics and asking questions once the course is online. Please let me know if you find any typos in the lecture material. You can also reach me via e-mail: sven.otto@uni-koeln.de
Important Dates
| Registration deadline for Exam 1 | Thursday, November 19, 2026 |
| Exam 1 | Thursday, December 3, 2026, 12:00–13:30 |
| Registration deadline for Exam 2 | Monday, February 1, 2027 |
| Exam 2 (alternate date) | Monday, February 15, 2027, 13:00–14:30 |
Registration closes 14 calendar days before each exam. Please register on time; if you miss the deadline, you will not be able to take the exam.
R Packages
The best way to learn statistical methods is to program and apply them yourself. Throughout this lecture script, we will use the R programming language to illustrate how econometric methods are applied in practice.
For those of you who are new to R and want to learn more about it, here is an introductory tutorial that contains many valuable resources: rintro.svenotto.com. I also recommend the interactive R package swirl, which offers an excellent way to learn directly within the R environment.
To run the R code of the lecture script, you will need to install some additional packages via the command install.packages():
install.packages(c("AER", "fixest", "moments", "dynlm", "modelsummary", "scatterplot3d", "remotes"))Some further datasets are contained in my package TeachData, which is available in a GitHub repository. It can be installed using the following command:
remotes::install_github("ottosven/TeachData")