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Data Science

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!

Become part of the WU community.

    • Schedule

      • Module 1: What is Data Science? Concepts & Application Domains (4 Days)
        September 26 - 29, 2018
      • Module 2: From Data Science to Big Data (4 Days)
        November 14 - 17, 2018
      • Module 3: Data Science in Practice and in the Future (4 Days)
        January 23 - 26, 2019

      Read more in the Data Science factsheet (PDF)

    • Target group

      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).

    • Topics

      Module 1: What is Data Science? Concepts & Application Domains | 4 days

      • Overview and case studies from different application domains (e.g. Marketing, Supply-Chain/Production Management, Process Management, Finance)
      • Data Processing and Data Analytics: Concepts & Methods
      • Selected case studies 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

       

       

    • Academic director

      Academic Director

      Axel Polleres
      Head of Institute, Information Business, WU Vienna

      • Logic programming
      • Semantic web technologies
      • Linked open data
      • Querying and reasoning about ontologies
      • Rules languages

       

      (Potential) External Speakers

      Elena Simperl

      Elena Simperl is a professor of Computer Science in the Web and Internet Science research group in the department of Electronics and Computer Science at the University of Southampton, UK since 2012. She received her doctoral degree in Computer Science from Freie Universität Berlin as well as a diploma in Computer Science from TU München. She has held numerous research and teaching positions at TU München, Freie Universität Berlin, STI Innsbruck and at the AIFB at the Karlsruhe Institute of Technology (KIT). Her research focuses on the intersection between knowledge technologies, social computing and crowdsourcing and she has published more than 100 scientific papers in various renowned journals.

      Philippe Cudré-Mauroux

      As a full professor at the University of Fribourg (Switzerland), he is in charge of the eXascale Infolab. Before that, he worked with Database Systems lab at MIT. He received his bachelors degree from EPFL and his two master degrees from Eurecom and fron INRIA SOP (University of Sofia-Antipolis). After working at IBM T.J. Watson Research, he went on to get his Ph.D. degree from EPFL (dissertation on emergent semantics). He worked as a visiting scholar at U.C. Berkeley and at Microsoft Research Asia, working on sensor data and novel information-sharing respectively. His research interests range from exascale information management to big data, scientific data and linked data.

      Michael Platzer

      Michael Platzer received a Ph.D. with a focus on Marketing Science from WU Vienna and earned an MSc in Mathematics (with distinction) from TU Vienna. He was a founding partner & CTO at Knallgrau New Media Solutions and a former data science lead at Nokia and Microsoft. He is founder and data scientist at mostly.ai and helps companies to leverage latest advances in machine learning and artificial intelligence for building smart data-powered solutions, with a solid understanding of product and service design.

    • Certificate

      After completing the course, you will receive a certificate including course details from the WU Executive Academy.

Faculty

Claudio Di Ciccio

Claudio Di Ciccio

Assistant Professor | Institutes for Management Information Systems & Operations and Information Business | WU Vienna

 

- Process mining
- Declarative process modeling
- Complex event processing

 

 

Ronald Hochreiter

Lecturer | Institute of Statistics and Mathematics | WU Vienna | Austria

More information

Sabrina Kirrane

Sabrina Kirrane

Assistant Professor | Institutes for Management Information Systems & Operations and Information Business | WU Vienna

 

- Digital forensics
- Data analytics
- Semantic web and linked data

Thomas Reutterer

Thomas Reutterer

Head of Institute | Service Marketing and Tourism | WU Vienna

 

- Retail and service marketing
- Customer value management
- Marketing models for customer-base analysis and decision support

Alfred Taudes

Professor | Department of Information Systems and Operations | WU Vienna | Austria

More information

Jürgen Umbrich

Jürgen Umbrich

Assistant Professor | Institutes for Management Information Systems & Operations and Information Business | WU Vienna

 

- Scalable on-demand data integration/query processing
- Monitoring/observatories (e.g. data dynamics, infrastructure quality)
- Data quality

 

 

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