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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!

    • Target group: For whom is the program made?

      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).

    • Learning outcomes

      • Get in-depth knowledge and hands on experience in data science, which can be translated into practice immediately
      • Learn about best practices and actual use cases from various domains
      • Get relevant know-how in order to set up data science projects in your own company
      • Make informed decisions on the basis of data models
      • Learn from expert data scientists with long-standing experience
    • Modules: dates & content

      Module 1: What is Data Science? Concepts & Application Domains (4 Days)
      November 8 - 11, 2017

      • Online tutorial: R data camp (one of the standard tools used in Data Science)
      • 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 & simple methods
      • Presentation of selected data science projects and scenarios in depth

      Module 2: From Data Science to Big Data (4 Days)
      December 13 - 16, 2017

      • Data science project kick-off: Development of project proposal from own enterprise context and preparation of a "data science problem pitch"
      • Legal and ethical foundations and data security
      • Big data methods and algorithms and data distribution - in theory and applied hands-on
      • Data workflows and advanced techniques (e.g. semantic technologies, text extraction)
      • Commercial data science tools fair with Oracle, IBM, SAP, Microsoft, etc.
      • Advanced data analytics
      • Application of learnt methods on project context

      Module 3: Data Science in Practice and in the Future (4 Days)
      February 28 - March 3, 2018

      • Preliminary project report on legal and ethical aspects, scalability problems, application of advanced data analytics methods, etc.
      • Data processing and data analytics trends and outlook
      • Application of data science on practical use cases
      • Special guest talks by distinguished academic speakers and experienced experts from practice (senior data scientist)
      • Data science project presentations

      Please note: This is only a preliminary outline of the program due to the fact the lecturers will adapt the content according to participants' progress and their needs in the course of the program (dynamic program design). This will allow for the provision of tailored packages so that participants can benefit from the program in the best possible way.

    • Academic director & faculty

      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

      Faculty

      Andreas Mild
      Deputy Head of Institute, Production Management, WU Vienna

      • Quantitative models in Marketing and new product development
      • Application of forecasting methods and decision support systems in the field of revenue management
      • Application of prediction markets

      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, Institute for Production Management, WU Vienna

      • Operations and Supply Chain Management
      • Marketing engineering
      • Knowledge management
      • Industry 4.0 and Big Data
      • Lean start-ups

      Ronald Hochreiter
      Managing Director, Quant4Market
      Lecturer, Institute for Statistics & Mathematics, WU Vienna

      • Data analytics
      • Data warehouse & data mining
      • Advanced marketing research
      • Data-based management
      • Quantitative optimization methods in finance
      • Final decision science

      Sabrina Kirrane
      Assistant Professor, Institutes for Management Information Systems & Operations and Information Business, WU Vienna

      • Digital forensics
      • Data analytics
      • Semantic web and linked data
      • Privacy risk analysis

      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
      • (RDF) Data management
      • Linked data

      Claudio Di Ciccio
      Assistant Professor, Institutes for Management Information Systems & Operations and Information Business, WU Vienna

      • Process mining
      • Declarative process modeling
      • Complex event processing
      • Service-oriented computing

       

      (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.

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