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Short course on Data Analysis

Study of data mining techniques, i.e., extraction of knowledge from large volumes of data. The knowledge extraction process includes exploratory analysis, data preprocessing, clustering, and prediction.

This short-course is regularly offered once a year at LNCC under the collaboration between CEFET/RJ and LNCC.

Fill this form to request access to the course.

Slides and schedule available at Moodle.

References:

  1. Han, M. Kamber, and Pei J. Data Mining: Concepts and Techniques, Morgan Kaufmann Publisher, Burlington, MA, USA, 3rd Edition, 2011.
  2. Zaki, M.J. and Jr., W.M. Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, Cambridge, United Kingdom, 1st Edition, 2014.
  3. Witten, I.H., Frank, E. and Hall M.A., Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers, Burlington, MA, USA, 3rd Edition, 2011.
  4. Hastie, T., Tibshirani, R., Friedman, J., (2011), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Publishing, New York, USA, 2 edition: 2013.
  5. James, G., Witten, D., Hastie, T., Tibshirani, R., (2013), An Introduction to Statistical Learning: with Applications in R., Springer Publishing, New York, USA, 1 edition: 2013.
  6. Lantz, B., (2013), Machine Learning with R. Packt Publishing Publishing, United Kingdom, 1st Edition, 2013.
  7. Leskovec, J., Rajaraman, A., Ullman, J.D., (2015), Mining of Massive Datasets. Cambridge University Press, Cambridge, United Kingdom, 2nd Edition, 2015.
  8. Shumway, R.H., Stoffer, D. S., (2010), Time Series Analysis and Its Applications: With Examples. Publisher Springer, New York, USA, 3 edition: 2010.

 

 

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