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Data Sciences: Predictive and Real-Time Analytics

Location: London, United Kingdom
Date: December 6, 2016 12:00 amfloq.to/QwFax

Analytics in general and Data Science in particular have seen a phenomenal growth in the last few years. With continuous advances in technology and the multifaceted use of the Internet and Social Media, organisations have access to massive volumes of internal and external data sets. In order to compete in a dynamic and evolving market the management analysts in these organisations have a requirement to ‘make sense’ out of these large unstructured data.’

This conference focuses on the areas of real-time analytics and predictive analytics and sets out to show how they are crucial for your business and how they can help you achieve your business goals.’

Case studies and thought provoking presentations explore real-life experiences and provide valuable insights into the many opportunities presented by Predictive and Real-Time Analytics.’

Why attend this event?

  • Beginners‘understand how Predictive and Real-time analytics can help your business. What are the options? How did other people exploit them? Also, take advantage of the discussion sessions and networking breaks to bring up your point of view, concerns and meet like-minded people.
  • Practitioners, Data Scientists and companies already familiar with Predictive and Real-time Analytics will find this is a unique opportunity to meet like-minded people and learn and share experience with subject-experts. As this topic area continues to evolve rapidly and incessantly it presents many challenges.

Topics to be covered:

  • Innovations and business applications with Real-Time Analytics
  • Predictive Analytics: Scope and Business Applications
  • Unstructured/Structured Big Data
  • Tools and applications which exploit Data and Machine Learning
  • Culture and organisational changes triggered by Data Science
  • Real Life case studies
  • Google Calendar
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  • Outlook 365
  • Outlook Live

Tags: analytics Big Data big data analytics Data

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