Major: Publishing and Printing
Code of subject: 8.186.00.M.19
Credits: 5.00
Department: Publishing Information Technologies
Lecturer: lecturer of PIT department
Semester: 3 семестр
Mode of study: денна
Learning outcomes: 1. Argue the choice of methods for solving a scientific and applied problem, critically evaluate the results obtained, and defend the decisions made.
2. To be able to communicate in business scientific and professional language, to apply different styles of speech, methods and techniques of communication, to demonstrate a wide scientific and professional vocabulary.
3. The ability to present and discuss the results obtained and transfer the acquired knowledge.
Required prior and related subjects: Prerequisites:
• Methods of analysis and optimization of complex systems.
• Artificial intelligence systems in publishing and printing.
Summary of the subject: The concept of Big Data.
Big Data History.
Volumes of data.
Requirements for Big Data Processing Systems.
The role of Apache Hadoop in processing Big Data.
Big Data Analysis Techniques.
High-speed neural structures of machine learning.
Application of the ICCPR NA to Big Data analytics.
Assessment methods and criteria: performing tasks in practical classes (40%)
final control (exam): written-oral form (60%)
Recommended books: 1. Про основні засади розвитку інформаційного суспільства в Україні на 2007– 2015 роки: Закон України від 9 січн. 2007 р. № 537– V. – Відомості Верховної Ради України. – 2007. –№ 12. – С. 102.
2. Большие данные. Революция, которая изменит то, как мы живем, работаем и мыслим / В. М. Шенбергер, К. Кукьер; пер. с англ. Инны Гайдюк. – М.: Манн, Иванов и Фербер, 2014. – 240 с.
3. Сетевая экономика: учебное пособие / В. Н. Клюковкин, Н. В. Морозова, Л. М. Куимова; Алт. гос. техн. Ун-т, БТИ. – Бийск: Изд-во Алт. гос. техн. ун-та, 2008. – 117 с.
4. Lynch C. How do your data grow? / C. Lynch // Nature. – 2008. – V. 455. №7209.– P. 28-29.
5. Han J. Data Mining: Concepts and Techniques (Second Edition) / J. Han, M. Kamber – Morgan Kaufmann Publishers, 2006. – 800 p.
6. Witten, I. H. Data mining : practical machine learning tools and techniques. / Ian H. Witten, Frank Eibe, Mark A. Hall. – 3rd ed. – Morgan Kaufmann Publishers, 2011. – 630 p.