270225 SE Statistical Methods with Python (2021W)
Continuous assessment of course work
Labels
REMOTE
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from We 01.09.2021 08:00 to Su 26.09.2021 23:59
- Deregistration possible until Su 26.09.2021 23:59
Details
max. 15 participants
Language: German
Lecturers
Classes
Vorbesprechung: 11.10.2021
Veranstaltung: 11.11. - 16.12.2021
Veranstaltung findet in digitaler Form wöchentlich Donnerstags von 10 – 12 Uhr statt
Information
Aims, contents and method of the course
Assessment and permitted materials
Type of performance control:
• Presentation of a chosen topic during the seminar
• Timely submission of a term paper of 6 – 10 pagesTo ensure good scientific practice, the course management can invite students to a grade-relevant discussion after the presentation and submission of the term paper, which must be completed positively.
• Presentation of a chosen topic during the seminar
• Timely submission of a term paper of 6 – 10 pagesTo ensure good scientific practice, the course management can invite students to a grade-relevant discussion after the presentation and submission of the term paper, which must be completed positively.
Minimum requirements and assessment criteria
Minimum requirements:
Attendance at the first unit of the seminar is compulsory. The attendance must be 75% fulfilled.To achieve a positive grade, 60% of the maximum possible number of points must be achieved. Each part of the performance must be completed at the end of the course.Assessment standard
100 points can be achieved for this course. These are divided into:
• Presentation and questions: 50 points
• Term paper on the topic: 50 pointsThe grades are awarded according to the following point scheme:
• 1 (very good): 100 - 91 points
• 2 (good): 90-81 points
• 3 (satisfactory): 80-71 points
• 4 (sufficient): 70-61 points
• 5 (insufficient): 60-0 points
Attendance at the first unit of the seminar is compulsory. The attendance must be 75% fulfilled.To achieve a positive grade, 60% of the maximum possible number of points must be achieved. Each part of the performance must be completed at the end of the course.Assessment standard
100 points can be achieved for this course. These are divided into:
• Presentation and questions: 50 points
• Term paper on the topic: 50 pointsThe grades are awarded according to the following point scheme:
• 1 (very good): 100 - 91 points
• 2 (good): 90-81 points
• 3 (satisfactory): 80-71 points
• 4 (sufficient): 70-61 points
• 5 (insufficient): 60-0 points
Examination topics
Reading list
Literature & References
In order to achieve the above learning goals, supporting literature and references will be provided.
In order to achieve the above learning goals, supporting literature and references will be provided.
Association in the course directory
AN-1, AN-2, AN-5, CHE II-1, A.5, Doktorat
Last modified: We 29.09.2021 14:09
You acquire the ability to write own Python scripts with a special focus on the necessities within the analytical chemistry (t-Test,volcano plots, multiple testing, plotting of results, loading and editing tables).
In this course, the first part will be dedicated to the introduction into Python and its most important libraries (matplotlib, numpy, padnas, scipy). In the second part, statistical and further analytical methods (t-test, multiple testing, ANOVA, statistical power, regression) will be explained with examples.
The contents of the courses are worked out by the participants on own responsibility. The participants can choose if to present their topic within a presentation (40 min) with slides or in an interactive session live on the computer (40 min). The sessions are closed by a discussion round (10 min). Die issues within a topic can either be picked by the participants or will be specified by the supervisor. The participants have to provide a term paper with 6 - 10 pages. At the end of the course, all papers will be merged into a single document by the supervisor and distributed to the participants for further application within or after studies.