SS24

Termine

• Dieser Kurs ist als Kurs zum Selbststudium konzipiert.

Einschreibeschlüssel

• Der Einschreibeschlüssel lautet "miniconda3".

o   Content:

Areal data is a data format in which point observations are aggregated over subregions of a predefined space. These subregions are non-overlapping and make up the entire space. This format is predominately common in medical research and one of the central formats in which, for example, data on Corona infections and hospitalizations were available. Due to the loss of information on the specific point of observation, the estimation of the spatial correlation can become a bit trickier than in spatial point processes. In recent years, especially during the pandemic, working with this type of data has become more relevant for data scientists and statisticians not only due to the relevance of their context but also due to somewhat recent advancements in research on network or graph theory, which allowed research on the statistical methods designed to work with this data more diverse.

o   Schedule:

o   Kick off (within the first two weeks of the semester) we will talk about the basics of spatial statistics

o   Phase 1: Foundations (2 weeks)
In the first 2 weeks after the kick off, we’ll meet weekly (!) to discuss a specific section of Spatial Statistics and Modeling (Gaetan) We’ll work through chapter 5.2 and 5.3 as well as some excerpts of Applied Spatial Data
Analysis with R (Bivand) at first to establish a foundation for us to work with.

o   Phase 2: Book club (2 weeks)

You will be asked to present a paper out of a selection of papers to the other students (45 mins to 1 hour) thereafter we will discuss the content of the presentation (30%= 20% presentation+ 10% discussion)

o   Phase 3: Implementation (5 weeks)

You will be asked to pick a dataset out of the ones provided or find your own in which you will apply one of the methods you found interesting in the book club phase.

o   Phase 4: Presentations (1 week)

You will hold a short presentation 10-15 mins about your project. (30%=20% presentation+ 10% discussion)

Phase 5:

You will write and hand in a final report on your implementation; this does not have to be handed in before the end of the semester, but should be within 3 or 4 weeks of the last day of lectures. (Not more than 10 pages) (40%)

The course offers a unique, first-hand introduction to official statistics. It is based on video material produced by colleagues from Eurostat, the German Federal Statistical Office, and the statistics departments of the European Central Bank and the Deutsche Bundesbank. The material is successively made available for self-study and then discussed in classroom meetings, during which some of the lecturers from official statistics and students from Trier University will join us.

Enrolment key: EMOS-B24

Time: Tuesday from 4 pm (s.t.) to 5.30 pm, approximately every second week

Begin: Tuesday, April 19 in hybrid format (F007 (Main Building) and Zoom)

Dates:

• The enrolment key is: "rademacher"

The course Analysis of high-dimensional biological data (formerly Statistische Methoden in Genomik und Proteomik / Statistical methods for biological high-throughput data) will cover important statistical methods and concepts for the analysis of high-dimensional biological high-throughput data. We will focus on bulk RNA-Seq, single-cell RNA-Seq, proteomic, metabolic, and especially microbiome data such as 16S rRNA and other amplicon data. 

Der Einschreibeschlüssel lautet: MatheIINF2024

Instructors:

The lecture aims at providing a basic theoretical and practical understanding of modern neural network approaches.

Enrollment key: DLearn

Format

Class

• Time: Wednesday 10:15-11:45 h
  
• Location: M 105

Enrollment key

• The enrollment key is I2ML.

• Der Einschreibeschlüssel ist StoSta24

Termine

Schedule:

• The enrolment key is: "Pitfalls24"

Schedule

• The enrollment key is "stat_inf_s24"

This course will discuss essential research techniques in statistics and data science, also preparing students for successfully participating in seminars and writing a thesis. The material presented focuses mainly on classical research techniques and a first understanding of research as a social system. Beyond this, classroom discussions will hopefully help combine the classical techniques with "modern personal knowledge management methods", which are currently also intensively disseminated by different YouTube authors. 

The course is part of the Bachelor’s programmes in Statistics and Data Science (150 major or 60 ECTS minors: `Methoden und Techniken des wissenschaftlichen Arbeitens’,  P16.1, and  WP 12.1 / WP 13.1, respectively ). All interested Master’s students and Bachelor's students from the "old PO" are most welcome as well. A certificate can be issued for active personal attendance.

NEW: Minor students and Bachelor's degree Erasmus students who will not attend a seminar can obtain 3 ECTS via an examation at the end of the course.

Time: Monday 12.15 to 14.00, M 110 (main building), on April 22, May 6, May 13, May 20, June 5, and on a further date at the end of June (to be arranged).

Enrolment key: ResTech24

If you are interested in active participation in the course but cannot always come in person for good reasons, you are welcome to contact me (Thomas AugustiIn). We are currently investigating whether we can offer a fallback Zoom solution.  

Dates: Wednesday 24.04.; 08.05.; 29.05.; 26.06.; 03.07.; 10.07.; 17.07.

Time: 09:00 - 12:00 s.t.

Location: A213 main building

Course Description

The second part of the course will provide a detailed introduction to multiple imputation, a convenient strategy for dealing with missing data. We will motivate the concept and illustrate why multiple imputation should generally be preferred over single imputation methods. The main focus of the course will be on strategies to generate (multiple) imputations and how to deal with common problems when applying the methods for large scale surveys. We will also discuss various options for assessing the quality of the imputations. All concepts will be demonstrated using software illustrations in R.

Curricular Embedding

Die Veranstaltung wendet sich an Studierende im Bachelor Statistik (3. / 4. Semester). Das "Grundlegende Praxisprojekt" (BA Statistik und Data Science - PO 2021) ist eine Pflichtveranstaltung (Modul P 11.1).

Die Veranstaltung wird während der Vorlesungszeit angeboten. Diese Die Einführungsveranstaltung mit Anwesenheitspflicht findet am 23.04. 9-11 statt.
Aus organisatorischen Gründen ist eine frühzeitige, separate Anmeldung für die Teilnahme während der Vorlesungszeit nötig -- schreiben Sie sich bitte ein und gehen Sie dann zur Anmeldung auf der Kursseite.

Einschreibeschlüssel: grndlgnprks24

Enrolment key: semml24

Organisation

Einschreibeschlüssel 

LiMo_s24

• Bachelor Statistik und Data Science: WP7 Ausgewählte Gebiete der statistischen Modellierung
• Nebenfach Statistik und Data Science (60 ECTS): WP6 Einführung in die Bayes-Statistik
• Nebenfach Statistik und Data Science (30 ECTS): WP8 Einführung in die Bayes-Statistik
• Bachelor Informatik mit integriertem Anwendungsfach Statistik: WP40 Einführung in die Bayes-Statistik
• Bachelor Wirtschaftsmathematik: P17 Ausgewählte Gebiete der angewandten Statistik
• Andere Studiengänge auf Anfrage

Einschreibeschlüssel: HaroldJeffreys

• A first meeting will take place at the beginning of the semester (will be scheduled in agreement with the participants), where the seminar topics are briefly introduced and assigned to the participants.

• The main part of the seminar with the presentations and discussions will be held as a block during the semester

• Seminar type: Block-type and in-person

• Language: The seminar will be held in English

• Target group: Bachelor and Master in Statistics

• Recognition possibilities: Biostatistics, Methodology and Modeling

The seminar will present and discuss current statistical analyses on the topic of the climate crisis by addressing central topics from a joint research project (CLIMEX II) with the LMU Chair of Physical Geography and Environmental Modelling. There will be two focal points.

1. Statistical modelling of climate change, extreme climatic events and natural hazards. This involves uncertainty quantification of climate models, drought forecasts for Bavaria, modelling of extreme events

2. Effects of climate change on human health. In particular, models that attempt to analyse the current and future adverse effects (e.g., the number of additional deaths) caused by climate change will be discussed. In particular, analyses by the Intergovernmental Panel on Climate Change (IPCC) will be discussed.

Students are expected to engage intensively with the scientific literature and present the statistical methodology for the chosen topic. It is possible to present your topics and, if suitable, to work on them in the seminar.

Key: Climate24

Statistik II für Statistiker: Wahrscheinlichkeitstheoretische Grundlagen der Statistik