Nr. | LV-Typ(en) | LV-Titel | |
5312 | PI | Data Processing I
Anmeldung über LPIS vom 22.02.2024 14:00 bis 25.02.2024 23:59 |
LV-Leiter/in | Assoz.Prof PD Dr. Stefan Sobernig, Univ.Prof. Dr. Axel Polleres |
Planpunkte Bachelor | SBWL Kurs I (Grundkurs) - Data Science Course I - Data Science Kurs I - Data Science |
Semesterstunden | 2 |
Unterrichtssprache | Englisch |
Termine | ||||
Di, | 12.03.2024 | 12:00-15:00 Uhr | D5.1.001 (Lageplan) | |
Di, | 12.03.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 19.03.2024 | 12:00-15:00 Uhr | D5.1.001 (Lageplan) | |
Di, | 19.03.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 09.04.2024 | 12:00-15:00 Uhr | D5.1.001 (Lageplan) | |
Di, | 09.04.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 16.04.2024 | 12:00-15:00 Uhr | D5.1.001 (Lageplan) | |
Di, | 16.04.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 23.04.2024 | 12:00-15:00 Uhr | D5.1.001 (Lageplan) | |
Di, | 23.04.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 30.04.2024 | 12:00-15:00 Uhr | D5.1.001 (Lageplan) | |
Di, | 30.04.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 14.05.2024 | 17:00-18:30 Uhr | TC.-1.61 (Lageplan) | |
Di, | 21.05.2024 | 13:00-18:00 Uhr | Online-Einheit | |
Mi, | 22.05.2024 | 13:00-15:00 Uhr | Online-Einheit | |
Termindownload (ical) | Termine abonnieren |
Weitere Informationen | https://learn.wu.ac.at/vvz/24s/5312 |
Kontakt: | ||
dp1-team@alice.wu.ac.at | ||
Inhalte der LV: | ||
This fast-paced class is intended for getting students interested in data science up to speed: We start with an introduction to the field of "Data Science" and into the overall Data Science Process. The primary focus of the rest of the course is on gaining fundamental knowledge for Data processing, that is, preparation, cleansing and storage of data, which typically takes the largest part of any data science project. We will learn how to deal with different data formats and how to use methods and tools to integrate data from various sources, plus how to resolve quality issues such as duplicates, encoding errors, missing values, etc. within raw data. The integrated data can then be used for further data analytics tasks (cf. course 2 in this SBWL). The students will practice approaches and techniques using the Python programming language in an interactive environment. All course material will be available at: https://datascience.ai.wu.ac.at/ |
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Lernergebnisse (Learning Outcomes): | ||
Overall, students shall gain fundamental knowledge for dealing with different data formats and in using methods and tools to integrate data from various sources in this course. This includes: |
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Regelung zur Anwesenheit: | ||
The attendance of at least 80% of the course units is a mandatory criterion. Presence in the first lesson is required. |
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Lehr-/Lerndesign: | ||
The course will focus on in-class code walkthroughs to present high-quality, well-commented code that students can later reference. |
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Leistung(en) für eine Beurteilung: | ||
The assessment will be based on the following:
The attendance of at least 80% of the course units is a mandatory criterion. |
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