Semester:WS 14/15
Art:Modul/LV/Prüfung
Sprache:Englisch
ECTS-Credits:3.0
Plansemester:1-6
Lektionen / Semester:30.0 L / 22.5 h
Selbststudium:67.5 h
Art:Modul/LV/Prüfung
Sprache:Englisch
ECTS-Credits:3.0
Plansemester:1-6
Lektionen / Semester:30.0 L / 22.5 h
Selbststudium:67.5 h
Modulleitung/Dozierende
- Dr. Ruth Jochum-Gasser, MA
(Modulleitung)
Studiengang
Bachelorstudiengang Betriebswirtschaftslehre (01.09.2012)Masterstudium Architektur (01.09.2014)
Bachelorstudium Architektur (01.09.2014)
Lehrveranstaltungen
Beschreibung
- Introduction into "Big Data Analytics"
- Gathering Data with Python
- Cleaning Up Data with Python
- Interpreting Data with R
- Predictive Models with R
Lernergebnisse
- Repeat the fundamental concepts and definitions in the area of big data analytics
- Understand the benefits of using and interpreting large data sets derived from various data sources.
- Solve assignments, especially case studies in the area of smart - Identify relationships between different types of data.
- Describe data an build prediction models.
- Compare solutions with regard to their prediction accuracy.
- Evaluation and select suitable prediction models.
Kompetenzen
Lehrmethoden
- Lecture with interactive elements
- Team project work
Voraussetzungen (inhaltlich)
- Basic programming skills
- Basic skills of descriptive statistics
Literatur
Field, A. P., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage.
Chang, W. (2013). R graphics cookbook. Beijing: O'Reilly.
Downey, A. (2013): Think Python - How to Think Like a Computer Scientist. Green Tea Press.
Arbeitsmaterial
Will be provided in class
Beurteilung
Passed / failed
Assessment tasks:
Part A: 50 % Mid term presentation
Part B: 50 % Final presentation
Kommentar
Fakultätsübergreifendes Wahlfach:
Es gelten spezielle Anmeldemodalitäten.
Prüfungen
- P-FU_Big Data Analytics (WS 14/15, in Bewertung)