Comprehensive course analysis
Who should attend
This applied program is for analysts, product managers, business managers or simply someone who wants to optimize their and their companies’ decision making through data science. Participants come from a wide range of industries including:
- Marketing, CRM, Business Analysis, Market Research Consulting
- Industry, Supply Chain Management, Manufacturing
- Health Care, Pharma
- Technology, IT, Telecommunications
- Consumer Goods
- Finance, Insurance
Please note: You should have at least 3 years of work experience and a good command of English (as this is the language of instruction).
About the course
Business impact through effective data handling
In times of digitalization, where buzzwords such as Business Intelligence (BI) and Marketing Automation are omnipresent, companies are confronted with huge challenges: How can they optimize their decision-making-process on the basis of an unimaginable mass of data, originating from a variety of sources? The answer lies in data science. To meet this need WU's executive academy has designed a cutting edge program on data science. In just a few months, you will get to know the tools, techniques, and fundamental concepts that you need to know in order to make an impact as a data scientist. You will learn how to unleash the potential of unused data resources within your enterprise - and how to approach this. During the course of the program, you will work through real-life case studies, with datasets from different domains (e.g. marketing, supply chain management) and will gain experience across the entire data science process: explorative data analysis, data munging, modelling, validation and cleansing, visualization, and communication.
Taking your skills to the next level
This applied program takes your data skills to the next level, shows you how to build big data pipelines as well as analytics processes and how to apply what you have learned in the context of real projects. At the end of the program, you will be able to apply all the methods dealt with and will have gathered an overview about the opportunities that open up as a data scientist.
“Data Science” will guide you and your company to the future and provide you with the knowledge and skills necessary to be your organization’s data scientist. Help your company to get on the fast lane – master the big data challenge!
Module 1: What is Data Science? Concepts & Application Domains (4 Days)
- Overview of data science concepts and use cases in different business application Domains (e.g. marketing, supply chain Management, production Management, process Management, finance)
- Data processing and data analytics: Concepts & methods
- Presentation of selected data science projects and scenarios in depth
Module 2: From Data Science to Big Data (4 Days)
- Data science Project kick-off
- Legal and ethical foundations and data security
- Big data methods and algorithms
- Data Workflows, Distribution, advanced techniques (e.g. semantic Technologies, text extraction)
- Commercial data science tools fair
- Advanced data analytics
Module 3: Data Science in Practice and in the Future (4 Days)
- Data processing and data analytics trends and Outlook
- Application of data science
- Special guest talks by distinguished academic Speakers and experienced experts from practice
- Data science project presentations
After completing the course, you will receive a certificate including course details from the WU Executive Academy.
Axel Polleres joined the Institute of Information Business of Vienna University of Economics and Business (WU Wien) in Sept 2013 as a full professor in the area of "Data and Knowledge Engineering". He obtained his Ph.D. and habilitation from Vienna University of Technology and worked at Universit...
Claudio Di Ciccio is an assistant professor at the Institute for Information Business and member of the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business (WU Vienna), Austria. His research interests include process mining, declarative process modelling, and...
2013 Habilitation in Business Administration (WU Vienna University of Economics and Business) 2005 PhD (Computational Management Science), University of Vienna 2001 M.Sc. (Business Informatics), University of Vienna Awards and Honors 2001 Diplomarbeitspreis der ÖGOR (Österreichische Gesellschaf...
Dr. Sabrina Kirrane is postdoctoral researcher working at the Institute of Information Business at the Vienna University of Economics and Business. Sabrina’s PhD, which she completed at the Insight Centre for Data Analytics at the National University of Ireland Galway, focused on the problem o...
Prof. Dr. Jan Mendling is a Full Professor with the Institute for Information Business at Wirtschaftsuniversität Wien (WU Vienna), Austria. His research interests include various topics in the area of business process management and information systems. He has published more than 250 research pap...
Andreas Mild holds a doctoral and an habilitation degree in Social Science and Economics from WU Vienna, Austria. His research interests include quantitative models in marketing and new product development, applications of forecasting methods and decision support systems in the field of revenue m...
Dr. Thomas Reutterer is Professor of Marketing at the Vienna University of Economics and Business (WU Vienna). He is head of WU's Institute for Service Marketing and Tourism and served as founding Academic Director of WU's Master's Program (MSc) in Marketing. His research, teaching and business c...
since 2018 Head WU Research Institute Cryptoeconomics 2005-2008 Coordinator of WWTF-project "Integrated Demand and Supply Chain Management" 2000-2004 Speaker of the Special Research Area Adaptive Models in Economics and Management Science 1997 Visiting Professor, Tsukuba 1993 Full Professor M...
Experienced Full Professor with a demonstrated history of working in the higher education industry. Skilled in Computer Science, Data Visualization, Knowledge Management, Neural Networks, and Text Mining. Strong engineering professional graduated from Karlsruhe Institute of Technology.
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