be able to effectively use paradigms of parallel processing
data such as MapReduce and Apache Hadoop, Apache Spark,
relevant Amazon Web Services cloud and IBM Bluemix; deploy
reliable and fast repositories for super-large volumes of data; use
software libraries and frameworks with efficient algorithms
processing large amounts of data. Be able to analyze and effectively
Apply cloud processing systems to large data.

Databases
System analysis
Discrete Math

big data, data science and data analysis (data analytics): mathematical, algorithmic, hardware and software to solve the main tasks of the subject area of big data. Analysis and building systems for processing of big data: settings and creation of hardware and software.

Дэви С. Основы Data Science и Big Data. Python и наука о
данных.//С.Дэви, М.Арно, А.Мохамед — СПб.: Питер, 2017. —
336 е.: ил.
C. B. B. D. Manyika, “Big Data: The Next Frontier for
Innovation, Competition, and Productivity,” McKinsey
Global Institute, 2011. URL:
http://www.mckinsey.com/~/media/McKinsey/dotcom/Ins
ights%20and%20pubs/MGI/Research/Technology%20an
d%20Innovation/Big%20Data/MGI_big_data_full_report.
ashx
Data Science and Big Data Analytics: Discovering, Analyzing,
Visualizing and Presenting Data // EMC Education
Services. 2015. — 432p. — ISBN: 978-1-118-87613-

laboratory work - 20;
settlement work - 30;
written component - 30;
oral component - 20.