Moses - Programming Project: Data Science in Python and R

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Module / Version:
#40997 / #2

Validity:
Seit WiSe 2020/21

Default display language:
English

Module title:
Programming Project: Data Science in Python and R

Credits:
6

Responsible person:
Maertens, Marianne

Grading:
unbenotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Technische Informatik und Mikroelektronik

Area of expertise:
34341900 FG Computational Psychology

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 5-5

Contact person:
Aguilar Cornejo, Guillermo Andres

E-mail address:
guillermo.aguilar@mail.tu-berlin.de

Website:

http://www.psyco.tu-berlin.de/teaching

Learning Outcomes

Students finishing the module... * can propose and plan a project in the field of data science with clear research goals, * know and can use modern tools of data science: R, Python, relevant extension libraries, version control with git, and data visualization techniques, * can work in groups, following agile software development principles, * can orally present their project and communicate their findings, * can deliver professional written academic-style report of research methods and findings using LaTeX, * can interact with the open-source software ecosystem, both by using available software and by sharing their own software publicly.

Content

* R, and relevant extension libraries (tidyverse) * Python, and relevant extension libraries (pandas, numpy) * Typesetting using LaTeX * Agile software development * Version control and software collaboration with git * Visualizing principles and techniques (ggplot) * Publishing of software online (GitHub)

Module Components

Pflichtgruppe:

1 from the following courses must be completed.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Programming Project: Data Science in Python and R - 1	PR	3434 L 10614	WiSe/SoSe	English	4

Workload and Credit Points

Programming Project: Data Science in Python and R - 1 (PR):

Workload description	Multiplier	Hours	Total
Attendance time	15.0	4.0h	60.0h
Preparation / Follow-up	15.0	8.0h	120.0h
			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

In this module participants will learn to plan and conduct a research project in the field of data science. Participants will receive tutorials on the tools of data science (e.g. python, R, latex, etc). They will choose and plan a project working in groups (3-4 people), and they will be continuously supervised throughout the semester on their project progress. During the semester they will give three short presentations (10 min each). In the first they will present a project proposal and the plan for the semester. In a second interim presentation they will show their progress, evaluate and discuss the upcoming necessary steps to finish the project. At the end of the semester they will show their finished project in a final presentation. Additionally, participants will publish a final research report which documents their project (methodology and research findings), together with the software implementation, on an open source software platform (e.g. github).

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Programming skills and willingness to quickly learn Python and R.

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
(Learning process review) Proposal presentation	10	oral	10min (presentation + questions)
(Learning process review) Interim presentation	10	oral	10min (presentation + questions)
(Learning porcess review) Final presentation	20	oral	20min (presentation + questions)
(Deliverable assessment) Software implementation	30	practical	13 weeks
(Deliverable assessment) Documentation	30	written	13 weeks

Grading scale

At least 60 points in total needed to pass.

Test description (Module completion)

A total of 100 portfolio points can be achieved. The module is passed if at least 60 portfolio points are reached.

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 24.

Registration Procedures

Please see: https://www.psyco.tu-berlin.de/teaching and ISIS course site

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
	Informatik (B. Sc.)	1	8	WiSe 2020/21	SoSe 2024
	Wirtschaftsinformatik (B. Sc.)	2	14	WiSe 2020/21	SoSe 2024

Miscellaneous

No information

Programming Project: Data Science in Python and R