Moses - Programmierpraktikum: Quality Data Science

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WiSe 2023/24 - WiSe 2023/24. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#41138 / #1

Validity:
WiSe 2023/24 - WiSe 2023/24

Default display language:
English

Module title:
Programmierpraktikum: Quality Data Science
Programming Lab: Quality Data Science

Credits:
6

Responsible person:
Haufe, Stefan

Grading:
unbenotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34352700 FG S-Professur Unsicherheit, inverse Modellierung und maschinelles Lernen

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 4-4

Contact person:
Wilming, Rick

E-mail address:
rick.wilming@tu-berlin.de

Website:

http://tu.berlin/uniml

Learning Outcomes

Knowledge: Students will be familiar with basic concepts of practical software engineering techniques and their applications in scientific software/data science projects. Further, students will know about different quality dimensions of software/machine learning systems and have a clear understanding of technical means for assuring their quality and reproducibility. Skills: The project will provide students with practical skills to develop and debug data science applications by analyzing data and code. They will know how to read and critically discuss papers related to machine learning, data science, and biomedical and other applications. Moreover, students will know to implement methods from scientific papers and perform basic quality checks to ensure the correctness of the implementations. Specifically, students will acquire experience in basic software engineering techniques such as unit testing, integration testing, and Git version control and rudimentary skills to develop high-quality software systems. Competencies: By the end of the software project, students will have developed the ability to implement procedures and tests to decide whether their implemented software will function as intended. Students can systematically structure the literature of a well-circumscribed field and gain experience in presenting the outcome to a critical audience and participating in scientific discussions. Overall, the software project will equip students with a comprehensive set of skills necessary for developing high-quality scientific software and investigating a small and well-circumscribed research question.

Content

Students will carry out a software project, where they will collaborate on a software development project replicating a typical scientific data science/software project. The project will cover various topics, such as mathematical/algorithmic problems (especially data science, machine learning, and explainable AI), software design, and engineering aspects including data processing, data analysis, databases, and software architectures. Depending on the group size, we will specify desired features and behavior of the software to be developed. The students use their implemented methods to explore a clearly defined research problem. The project will entail but is not limited to selected topics relevant to applications in biomedicine, e.g., neuroscience or intensive care medicine.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Uncertainty in Machine Learning (UNIML)	PPK		WiSe/SoSe	German	4

Workload and Credit Points

Uncertainty in Machine Learning (UNIML) (PPK):

Workload description	Multiplier	Hours	Total
Attendance	5.0	6.0h	30.0h
Project work	15.0	6.0h	90.0h
Preparation for presentations and consultations	15.0	4.0h	60.0h
			180.0h(~6 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Students will self-organize and divide the project work in terms of implementation and presentation of their results. Throughout the semester, we will provide assistance and hold weekly discussions to address any issues and talk about progress. Students will prepare an introductory presentation to a specific topic based on a collection of published material provided. The presentations will be framed as scientific discussions. The self-organized framework will foster discussions about issues and progress updates. In final presentations, the students will summarize their results and contributions related to their research question.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Successful completion of an introductory module on data science or machine learning is recommended. Programming skills in at least one language (e.g., Matlab, Python, R) are highly recommended.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

ungraded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
(Deliverable assessment) Project journal	40	written	15-20 pages
(Deliverable assessment) Documented github repository	30	practical	project specific
(Deliverable assessment) Demonstration	30	written	project specific

Grading scale

At least 50 points in total needed to pass.

Test description (Module completion)

1. The quality of a project journal (40%) 2. The quality and completeness of a documented github repository for code and data generated within the project (30%) 3. The quality of a demonstration of the final software (30%)

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 15.

Registration Procedures

Students can sign up for the course in MOSES.

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Miscellaneous

No information

Programmierpraktikum: Quality Data Science