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    Computational Methods for Data Analysis

Computational Methods for Data Analysis

Yeliz Karaca
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      This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.
      Format: Paperback / softback CONTRIBUTORS: Yeliz Karaca EAN: 9783110496352 COUNTRY: Germany PAGES: WEIGHT: 753 g HEIGHT: 240 cm
      PUBLISHED BY: De Gruyter DATE PUBLISHED: 2018-12-17 CITY: GENRE: BUSINESS & ECONOMICS / Econometrics, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Data Analytics, MATHEMATICS / Applied, MATHEMATICS / Probability & Statistics / General WIDTH: 170 cm SPINE:

      Book Themes:

      Anthropology, Econometrics and economic statistics, Probability and statistics, Applied mathematics, Data capture and analysis, Data mining, Computer science

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      Yeliz Karaca, University of Massachusetts, Worcester MA, USA; Carlo Cattani, University of Tuscia, Viterbo, Italy.
      This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.
      Format: Paperback / softback CONTRIBUTORS: Yeliz Karaca EAN: 9783110496352 COUNTRY: Germany PAGES: WEIGHT: 753 g HEIGHT: 240 cm
      PUBLISHED BY: De Gruyter DATE PUBLISHED: 2018-12-17 CITY: GENRE: BUSINESS & ECONOMICS / Econometrics, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Data Analytics, MATHEMATICS / Applied, MATHEMATICS / Probability & Statistics / General WIDTH: 170 cm SPINE:

      Book Themes:

      Anthropology, Econometrics and economic statistics, Probability and statistics, Applied mathematics, Data capture and analysis, Data mining, Computer science

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      Yeliz Karaca, University of Massachusetts, Worcester MA, USA; Carlo Cattani, University of Tuscia, Viterbo, Italy.

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