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Data mining
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MOOC
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.
This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
- The major steps involved in tackling a data science problem.
- The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
- How data scientists think!
- Course related:
- LGT6801 Guided Study in Logistics I, LGT6202: Stochastic Models and Decision under Uncertainty, LGT6802 Guided Study in Logistics II, and LGT6803: Guided Study in Logistics III
- Subjects:
- Business Information Technology and Computing
- Keywords:
- Electronic data processing Data mining Problem solving
- Resource Type:
- MOOC
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Presentation
This video was recorded at COIN / PlanetData Winter School on Knowledge Technologies for Complex Business Environments, Ljubljana 2011. Organized by COIN FP7 Integrated Project and PlanetData FP7 Network of Excellence, the school seeks to bring together students, scholars and researchers from industry in order to foster collaboration and interoperability with innovative services and project large-scale data management in business environments. The main topics of the winter school are: Interoperability and collaboration models and solutions, Enterprise interoperability and collaboration services, Innovative knowledge and semantically powered technologies, Knowledge process and context modelling, Pro-active knowledge tools, Large scale analytics and reasoning tools, Business cases and real case studies. Detailed information can be found here.
- Subjects:
- Management and Computing
- Keywords:
- Data mining Real-time data processing
- Resource Type:
- Presentation
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Presentation
This video was recorded at COIN / ACTIVE Summer School on Advanced Technologies for Knowledge Intensive Networked Organizations, Aachen 2010. Part 1. Context Computing. Context is used as a term for packaging information for a particular need. A criterion for selecting or prioritization information from a broader pool of information could be called contextual model. Search can be contextual: http://searchpoint.ijs.si. The relevance of Context in computing seems to be growing. Many application areas see an opportunity in extending its value by introducing "context sensitivity". More details do to be found in ISWC2006 Tutorial on "context sensitivity": http://videolectures.net/iswc06_athens_ga/ Part 2. Text Mining & Light Weight Semantics. Videolectures discusses the following topics: - levels of text representations - modeling the data (Support Vector Machine) - classification into large taxonomies (DMoz) - visual & contextual search (Search Point) - multilingual search - news bias, news visualization - text enrichment (Enrycher) - knowledge based summarization - question answering (AnswerArt) - Cyc knowledge base and reasoning
- Subjects:
- Management and Computing
- Keywords:
- Data mining Information resources management Business -- Data Processing Management information systems
- Resource Type:
- Presentation