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VU Business Intelligence I (052411)

«Wilfried Grossmann», «Stefanie Rinderle-Ma»
«U:FIND»

Tutor: «Amolkirat Singh Mangat»

First lecture: 07.03.2018 (attendance mandatory), Last lecture: 27.06.2018.

Wed. wkly. from 07.03.2018 to 27.06.2018 09:45 - 13:00

Place: PC2, Währinger Straße 29 1.OG

The course is structured in five sections:

Methodology and modeling techniques in business intelligence
Data models in business intelligence and data quality
Analysis of cross sectional data (data mining)
Analysis of process data (process mining)
Business intelligence tools (OLAP, Visualization, Text mining, Data Quality Management)

Dates

Guest lecture (G. Kaes): Service-based Data Generation ([http://btw2017.informatik.uni-stuttgart.de/slidesandpapers/F6-10-33/paper_web.pdf|background info]), Game-based experiment (participation optional, 2 discussion points)

07.03.	Preliminary talk
14.03.	Introduction to Business Intelligence
21.03.	Data Provisioning I
Osterferien
11.04.	Data Provisioning II
18.04.	Modelling techniques of Business Intelligence 1
25.04.	Modelling techniques of Business Intelligence 2
02.05.	Presentation Exercise I and II
09.05.	Analysis methods for cross-sectional data (Data Mining 1)
16.05.	Analysis methods for cross-sectional data (Data Mining 2)
23.05.	Analysis methods for cross-sectional data (Data Mining 3) Presentation Exercise III
30.05.	Analysis methods for process data (Process Mining 1)
06.06.	Analysis methods for process data (Process Mining 2)
13.06.	Social Network Analysis. Presentation Exercise IV
20.06.	Conclusion and special techniques, Presentation Exercise III & V
27.06.	10:00 written exam

Overall, 5 exercises will be provided. Single submission, i.e., no teamwork.

Handouts

Data Provisioning
Process Discovery
Conformance checking: «Slides» by A. Rozinat
Social Network Analysis

* [file:UseCase1.pdf|Use Case 1] * [file:UseCase2.pdf|Use Case 2] * [file:UseCase3.pdf|Use Case 3] * [file:BusinessIntelligence_Data_SS18.pdf|Data Provisioning] * [file:BI1_Modelle.pdf|Modeling in BI] * [file:Cross-sectional Analysis 1.pdf|Cross-sectional Analysis 1] * [file:Cross-sectional Analysis 2.pdf|Cross-sectional Analysis 2] * [file:Cross-sectional Analysis 3.pdf|Cross-sectional Analysis 3] ** [file:RegressionExample.pdf|DemoExample Regression] ** [file:NaiveBayesDemo1.pdf|DemoExample Naive Bayes] ** [file:LogisticRegressionDemo.pdf|DemoExample Logistic Regression] ** [file:TreeClassification1.pdf|DemoExample Tree classification] ** [file:SupportVectorMachines.pdf|Example SVM] ** [file:Boosting1.pdf|Example Boosting] * [file:TimeSeriesClassification.pdf|Temporal Data Mining] ** [file:Timewarping1.pdf|Dynamic Time Warping] *** [file:Logistic_stacked1.csv|Data Time warping] ** [file:ResonseFeatures1.pdf|Response Feature Analysis] * [file:TextMining.pdf|Text Mining] * [file:BusinessIntelligence_ProcessDiscovery_SoSe17.pdf|Process Mining] * [file:BusinessIntelligence_OrgMining_Conformance.pdf|Org Mining, Conformance Checking] * [file:BusinessIntelligence_SoSe17_SocialNetworkAnalysis.pdf|Social Network Analysis] * [file:NeuralNetsDeepLearning.pdf|Neural Nets and Deep Learning] ** [file:NeuralNetDemo.pdf|Neural nets example] * [file:ImputationMethods.pdf|Missing values and Statistical Matching]

Exercises

** [file:Timeseries.xlsx|Exercise5-Timeseries] ** [file:DWH.7z|Exercise5-DWH] ** [file:DataProcessing.7z|Exercise5-DataProcessing]

Old Exams

Grading

45% Exercises
45% Written exam
10% Presentation and discussion

> 50% overall

Literature

W. Grossmann, S. Rinderle-Ma: Fundamentals of Business Intelligence. Springer-Verlag Berlin Heidelberg, doi: 10.1007/978-3-662-46531-8 (2015)

Abbildung 1: Book Cover

Letzte Änderung: 14.02.2019, 16:40 | 538 Worte

VU Business Intelligence I