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Faglig dag 2017: An Introduction to Data Science

indsendt 2. apr. 2017 06.55 af Hans-Henrik Kaaber   [ opdateret 20. apr. 2017 02.30 ]
Time: 26 April, 15-17
Place: Auditorium 5, HCØ,  2100 København Ø

At the annual Faglig Dag event we plan to focus on the discipline of Data Science. Three selected speakers will provide an introduction to the subject followed by example applications from their respective domains.
Please note: The presentations will be given in English.

The preliminary program looks as follows:
  • A Short Introduction to Data Science, by assistant professor Fabian Gieseke, DIKU
    The field of data science has gained considerable attention over the past years. One reason for this phenomenon is the fact that the data volumes in various domains have increased dramatically. This is the case, for instance, in astronomy, where current projects produce data volumes in the terabyte range. Upcoming ones will produce such data volumes per day, week, or even per hour. A similar "data flood" can be observed in many other disciplines including medicine, biology, finance, business intelligence, energy systems, or social media. In most cases, the sheer data volumes render a manual analysis impossible. Data science techniques aims at extracting knowledge in an automatic manner and have been identified as one of the key drivers for future discoveries and innovation. The presentation will give a quick introduction to this field and will cover various topics including big data, data science problems, data science workflow, and large-scale data analysis.

  • Business Applications of Data analytics, by assistant professor Raghava Rao Mukkamala, CBS
    This talk is about stop talking about Big Data and start doing Business Data Analytics to create business value. First, we discuss three paradigms for generating competitive advantages and business value from new technologies. Second, we present the Centre for Business Data Analytics' framework for transforming big data sets into business assets by creating meaningful facts, actionable insights, valuable outcomes, and sustainable impacts. Third and last, we present illustrative business cases.

  • Medical Applications of Data Analytics, by professor Mads Nielsen,DIKU
    I will give a short introduction to data science and machine learning and a couple of research examples. How may a data science approach help prognosis of diseases in breast cancer, Alzheimers, arthritis. These will include a brief introduction to deep learning and its application to medical sciences. Then I will focus on some of the bottlenecks of getting this to work in practice in the healthcare sector and potential technical solutions.
Moreover, we hope to present some suggested further readings as well as examples of offered or planned courses on the subject.
For practical reasons, if you expect to participate, please register at Doodle no later than 19 April.
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