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Geoprocessing with Python - Detailseite

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  • Online Belegung noch nicht möglich oder bereits abgeschlossen
Grunddaten
Veranstaltungsart Masterseminar Veranstaltungsnummer 3312126
Semester WiSe 2019/20 SWS 4
Rhythmus jedes 2. Semester Moodle-Link  
Veranstaltungsstatus Freigegeben für Vorlesungsverzeichnis  Freigegeben  Sprache englisch
Belegungsfrist - Eine Belegung ist online erforderlich
Veranstaltungsformat keine Angabe

Termine

Gruppe 1
Tag Zeit Rhythmus Dauer Raum Raum-
plan
Lehrperson Status Bemerkung fällt aus am Max. Teilnehmer
Di. 13:00 bis 17:00 wöch 15.10.2019 bis 10.02.2020  Alfred Rühl-Haus - 1.230 Rudower Chaussee 16 (RUD16) - (Medienunterstützter Unterrichtsraum)   findet statt     16
Gruppe 1:
Zur Zeit keine Belegung möglich


Zugeordnete Personen
Zugeordnete Personen Zuständigkeit
Baumann, Matthias , Dr. verantwortlich
Pflugmacher, Dirk , Dr. verantwortlich
Studiengänge
Abschluss Studiengang LP Semester
Master of Science  Global Change Geography Hauptfach ( Vertiefung: kein LA; POVersion: 2016 )   10  -  
Zuordnung zu Einrichtungen
Einrichtungen
Mathematisch-Naturwissenschaftliche Fakultät, Geographisches Institut
Mathematisch-Naturwissenschaftliche Fakultät, Geographisches Institut, Abteilung Physische Geographie, Biogeographie
Inhalt
Kurzkommentar

The main objetive of this seminar is to teach the students with the ability to solve common problems in big data processing using Open Source programing languages (python) and Geodata Libraries (OGR, GDAL). The seminar will start by providing an introduction into basic scripting techniques (execute scripts, building loops, using lists), and will later use these techniques to solve complex, yet in modern geodata science common, processing tasks.

Students will have to submit (nearly) weekly labs, and the MAP will be constituted of a complex programing problem. Students of all MSc-levels are welcome, yet the class is, because of the workload, recommended for people close to, or already in, their MSc-Thesis. The class will be taught in the PC-pools using departmental infrastructure, but students are welcome to bring their own equipment (e.g., laptop).

As the class is centered around the application of large amounts of geospatial data, it is strongly recommended for all class participants to have at least some experience with the two most common types of geodata - shapefiles, and rasterfiles - for example through basic GIS classes.

Kommentar

The main objetive of this seminar is to teach the students with the ability to solve common problems in big data processing using Open Source programing languages (python) and Geodata Libraries (OGR, GDAL). The seminar will start by providing an introduction into basic scripting techniques (execute scripts, building loops, using lists), and will later use these techniques to solve complex, yet in modern geodata science common, processing tasks.

Students will have to submit (nearly) weekly labs, and the MAP will be constituted of a complex programing problem. Students of all MSc-levels are welcome, yet the class is, because of the workload, recommended for people close to, or already in, their MSc-Thesis. The class will be taught in the PC-pools using departmental infrastructure, but students are welcome to bring their own equipment (e.g., laptop).

As the class is centered around the application of large amounts of geospatial data, it is strongly recommended for all class participants to have at least some experience with the two most common types of geodata - shapefiles, and rasterfiles - for example through basic GIS classes.

Literatur
  1. Jake vanderPlas (2016) Python Data Science Handbook. Essential Tools for working with data. O'Reilly Media.
  2. Criss Garrard (2016). Geoprocessing with python. Manning Publications.
Prüfung

The MAP will consist of a complex programing problem, for which the students will have to submit a report and a code example.

Zielgruppe

The format of the class and the programming problems to be solved require an understanding of general data types used in geographic research, such as shapefiles or rasterfiles. As such, the class is ideal for Geography students. External students, e.g., from other departments and/or universities, are welcome as well - though it would be recommended for these students to have at least some background in Geographic Information Systems. 

Strukturbaum

Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester WiSe 2019/20. Aktuelles Semester: SoSe 2020.
Humboldt-Universität zu Berlin | Unter den Linden 6 | D-10099 Berlin