Teaching Experiences Wolfram Höpken

Advanced Tourism Analytics

Dates

Summer semester 2019 / winter semester 2019/20

Content

Introduction to business intelligence and data mining; advanced analytics in tourism – Use Cases; theory and applied exercises on supervised machine learning (general approach, classification, estimation) and unsupervised machine learning (clustering, association rules)

Institution

Department of Social Science (Department of Tourism Studies and Human Geography), Mid Sweden University

Information & communication technologies in advanced tourism management

Dates

Summer semester 2016

Content

Introduction to ICT in tourism, ICT trends and research areas (ubiquitous services, recommender systems), the knowledge destination concept, BI applications for tourism destinations (mining online auctions, sentiment analysis)

Institution

Department of Social Science (Department of Tourism Studies and Human Geography), Mid Sweden University

Innovative ICT applications and business intelligence in tourism

Dates

Winter semester 2014

Content

Introduction to e-Tourism, innovative ICT applications in tourism (semantic web, new markets and online auctions, social media, recommender systems, mobile services), introduction to business intelligence & data mining, preprocessing, explorative data analysis and statistical approaches, association rules, supervised learning (k-nearest neighbor, decision trees), and unsupervised learning (k-means clustering)

Institution

Department of Social Science (Department of Tourism Studies and Human Geography), Mid Sweden University

An Introduction into Business Intelligence – Theory and Applications

Dates

Summer semester 2014

Content

Business Intelligence & Data Mining – Introduction, Data mining toolset RapidMiner, Preprocessing (Data Cleaning & Data Transformation), Explorative Data Analysis and Statistical Approaches, Association Rules (Apriori, FP-Growth), Supervised Learning (k-Nearest Neighbor, Decision Trees, Support Vector Machines, Neural Networks, Series Analysis), Unsupervised Learning (k-Means Clustering) and Real Data Analysis

Institution

Department of Social Science (Department of Tourism Studies and Human Geography), Mid Sweden University

Business Intelligence

Dates

Winter semester 2012/2013 – summer semester 2022

Content

Introduction and practical application of all components of a BI architecture: data warehousing and multi-dimensional data modelling, ETL - extraction, transformation & load and reporting & OLAP.

Institution

Bachelor course Business Informatics, University of Applied Sciences Ravensburg-Weingarten

Data Science

Dates

since summer semester 2011

Content

Advanced concepts of data science and data mining: supervised and unsupervised learning; preprocessing and feature engineering; association rules and sequential patterns (generalized rule induction, FP-Growth, sequential patterns); decision trees (C4.5, CART) & rule induction; naive bayesian classification; support vector machines; neuronal networks and deep learning; forecasting (moving average, exponential smoothing, trend analysis, stationarity & component model, ARIMA, ANN); clustering (hierarchical clustering, k-means, k-medoids, DBSCAN, kohonen networks); text mining (text preprocessing, statistical language models); information extraction (crawler, wrapper induction); sentiment analysis; web usage mining

Institution

Master course Digital Business, University of Applied Sciences Ravensburg-Weingarten

Innovation and Transfer Competency & Research Methods

Dates

since winter semester 2010

Content

Identification of new technologies and potential application scenarios, investigation of the state of the art and acquiring a profound understanding of a problem domain, implementation of innovations based on new technologies and transferring new technologies and research results into practical applications, evaluation of innovations concerning their practicability and value creation

Institution

Master course Digital Business, University of Applied Sciences Ravensburg-Weingarten

Data Mining & Big Data

Dates

since summer semester 2009

Content

Techniques and approaches of data mining and web data mining: preprocessing, association rules and sequential patterns, supervised learning (classification), unsupervised learning (clustering), information retrieval and web search, structured data extraction, information integration, opinion mining and web usage mining

Institution

Bachelor course Business Informatics, University of Applied Sciences Ravensburg-Weingarten

IT Solutions in Tourism

Dates

Winter semester 2008/2009 – winter semester 2021/22

Content

Selected, innovative ICT applications and research areas in tourism: New markets and online auctions, intelligent search and recommender systems, dynamic packaging, software agents, interoperability and semantic web, mobile services and ambient intelligence, Web 2.0 and social computing.

Institution

Bachelor course Business Informatics, University of Applied Sciences Ravensburg-Weingarten

Introduction to eTourism

Dates

Winter semester 2008/2009 – summer semester 2012

Content

Introduction into the field of electronic tourism: Motivation and history of ICT usage in tourism, overview of different ICT applications along the tourism value chain, insight into concrete technical solutions based on selected case studies.

Institution

Bachelor course Business Informatics, University of Applied Sciences Ravensburg-Weingarten

Information Resources and Retrieval

Dates

Winter semester 2008/2009 – summer semester 2010

Content

Introduction to research & engineering, knowledge management in the knowledge economy, scientific practices, information resources and information retrieval techniques.

Institution

Bachelor course Business Informatics, University of Applied Sciences Ravensburg-Weingarten

IT Solutions in Tourism

Dates

Summer semester 2008

Content

Selected IT solutions and innovative IT applications in tourism: electronic customer relationship management, management information systems, harmonisation & interoperability in tourism, semantic web, software agents, recommender systems, mobile applications and ambient intelligence.

Institution

Course Management & IT, Management Center Innsbruck, Austria

eTourism – Yield Management in the Hotel Sector

Dates

Winter semester 2007/2008

Content

Customer segmentation based on clustering mechanisms, yield-optimal pricing strategies, forecasting mechanisms (time series methods), capacity and price management (bid price method), overbooking strategies (binominal overbooking model), system environment and requirements (computer reservation systems), data warehousing and data mining

Institution

Faculty of Business Administration, University of Innsbruck, Austria

Information and Communication Technologies in Tourism

Dates

Winter semester 2006/2007

Content

Introduction into tourism and IT in tourism, areas of IT usage along the tourism value chain (computer reservation systems, property management systems, destination management systems, eCRM), IT usage by tourism management, trends and future applications: harmonisation & interoperability, semantic web, virtual communities, software agents, online auctions

Institution

Faculty of Mathematics, Computer Science and Physics, University of Innsbruck, Austria

Information Technology & Tourism

Dates

Summer semester 2005

Content

Tourism: An information-based industry, ICT definition and development phases, role and effects of ICT on travel and tourism information, decision support systems in tourism (data warehousing, data mining), eCRM in tourism, ICT trends and new developments, mobile services, virtual reality, ambient intelligence, semantic web, recommender systems in tourism

Institution

Course Management & IT, Management Center Innsbruck, Austria

Specialisation Course eTourism

Dates

Summer semester 2005

Content

Tourism as information business, relevance and application fields of ICT in tourism, economic and processual implications, information economy and smart business networks, management information systems in tourism, ICT trends and future developments, mobile services, recommender systems, software agents, ambient intelligence in tourism

Institution

Faculty of Business Administration, University of Innsbruck, Austria

Information and Communication Technologies in Tourism

Dates

Winter semester 2004/2005

Content

Tourism as information business, information technology and tourism, IT application fields in tourism, IT trends, management implications, website development – critical success factors & case studies, website evaluation

Institution

Faculty of Business Administration, University of Innsbruck, Austria