460-6031/02 – Advanced Data Analysis (PAD)
| Gurantor department | Department of Computer Science | Credits | 10 |
| Subject guarantor | doc. Mgr. Miloš Kudělka, Ph.D. | Subject version guarantor | doc. Mgr. Miloš Kudělka, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of the course is to provide students an extended view of data analysis, realization of deeper analyzes and advantage of source data sets with regard to interpretation in selected target area. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of his PhD studies and dissertation work.
Teaching methods
Seminars
Individual consultations
Project work
Other activities
Summary
This course provides the students, in its first part, necessary information about basic and advanced algorithms, typical algorithmic problems and their complexity. This part will also contain the introduction of programming techniques and programming and scripting languages. Next, foundations of vector data and network data analysis will be presented including simple algorithms used in both areas. The students will also be familiarized with different tools and libraries suitable for the solution of everyday tasks, primarily focused on biomedical data analysis.
Compulsory 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.
Recommended 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.
Additional study materials
Way of continuous check of knowledge in the course of semester
Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.
E-learning
Other requirements
The student prepares and presents the work on a given topic.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
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.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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