Advanced Data Analysis

Type of study	Doctoral
Language of instruction	Czech
Code	460-6031/01
Abbreviation	PAD
Course title	Advanced Data Analysis
Credits	10
Coordinating department	Department of Computer Science
Course coordinator	doc. Mgr. Miloš Kudělka, Ph.D.

Subject syllabus

• Algorithm. Problem-solving strategies using algorithms. Significant types of solved problems.
• Sorting and searching algorithms.
• Linear and tree data structures.
• Complexity of algorithms and complexity of problems.
• Vector data and their algebraic and geometric interpretation.
• Clustering algorithms, K-means and Hierarchical Clustering.
• Classification algorithms, Naïve Bayes, K-nearest Neighbors.
• Network data and their representation.
• Algorithms for transformation vector data to network data.
• Measuring of network properties, algorithms and interpretation.
• Network clustering algorithms.

Literature

• Levitin, A. (2012). Introduction to the design & analysis of algorithms. Boston: Pearson.
• Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques (Fourth Edition). Morgan Kaufmann Series in Data Management Systems.

Advised literature

• Libeskind-Hadas, R., Bush, E. (2014). Computing for biologists: Python programming and principles Cambridge University Press.
• Barabási, A. L. (2016). Network science. Cambridge university press.

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