Methods of Analytics

Major: System Analysis
Code of subject: 6.124.02.E.102
Credits: 5.00
Department: Information Systems and Networks
Lecturer: Doctor of Sciences., Professor Berko Andriy Yulianovych
Semester: 8 семестр
Mode of study: денна
Learning outcomes: As a result of studying the discipline, the student must be able to demonstrate the following learning outcomes: Know and be able to apply in practice the methods of systems analysis, methods of mathematical and information modeling to build and study models of objects and processes of informatization. Know the methods of revealing uncertainties in the problems of system analysis, be able to reveal situational uncertainties and uncertainties in the tasks of interaction, counteraction and conflict of strategies, find a compromise in revealing conceptual uncertainty, etc. Know and be able to identify (evaluate) the parameters of mathematical models of control objects in real time in terms of changes in its dynamics and the action of random perturbations, using the measured signals of input and output coordinates of the object. Know and be able to implement systems of high-load computing and data processing in the tasks of system analysis and management, and decision support systems.
Required prior and related subjects: Business process analysis
Summary of the subject: Basic principles of data analysis technology; basic concepts and definitions of data analysis; data warehouse concepts; data transformation methods; multidimensional data models; ways of data visualization; data cleaning methods; statistical aspects of analytics; models and methods of building models and analysis of dependencies in data resources; modern software for designing and developing data analysis systems.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, oral examination; • Final control (60%). in written – 50%, verbally- 10%.
Recommended books: 1. White, Tom // Hadoop: The Definitive Guide // O'Reilly Media, 2009. 2. Hadoop. Apache Software Foundation // http://hadoop.apache.org/ 3. Finley, Klint // Steve Ballmer on Microsoft's Big Data Future and More in This Week's Business Intelligence Roundup // ReadWriteWeb, 2011. 4. Fay Chang, Jeffrey Dean, Sanjay Ghemawat & etc. // Bigtable: A Distributed Storage System for Structured Data // Google Lab, 2006. 5. Сухорослов, O. // Новые технологии распределенного хранения и обработки больших массивов данных // Институт системного анализа РАН, 2008. 6. Jeffrey Dean, Sanjay Ghemawat // MapReduce: Simplified Data Processing on Large Clusters // Google Inc., 2004. 7. Judy Qiu // Cloud Technologies and Their Applications // Indiana University Bloomington, 2010 8. The Hadoop Distributed File System: Architecture and Design // http://hadoop.apache.org/common/docs/r0.17.2/hdfs_design.html 9. Созыкин, А. // Параллельное программирование в Hadoop // http://www.asozykin.ru/courses/hadoop 10. Ralf Lammel // Google’s MapReduce Programming Model — Revisited // Microsoft Corp.