Introduction to Big Data
Course: Software Engineering
Structural unit: Faculty of information Technology
Title
Introduction to Big Data
Code
ОК 11
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
2 Semester
Number of ECTS credits allocated
6
Learning outcomes
PR-04. To Identify information needs and classify data for software design
PR-06. To Develop and evaluate software design strategies; justify, analyze and evaluate options for project solutions from the point of view of the quality of the final software product, resource limitations and other factors.
PR-09. To Make reasonable choose of programming paradigms and languages for software development; apply modern means of software development in practice.
PR-10. To Modify existing and develop new algorithmic solutions for detailed software design.
PR-18. To develop mathematical and software for scientific research in the field of software engineering.
Form of study
Full-time form
Prerequisites and co-requisites
To know the theoretical foundations obtained by studying the normative disciplines "Algorithms and data structures", "Fundamentals of programming", "Computational methods" and "Object-oriented programing"
Course content
The discipline provides theoretical knowledge and practical skills and abilities sufficient for the successful performance of professional duties in the field of information technologies and prepares students for further employment in the field of Big Data Processing of various nature in distributed software systems. The program is designed to help specialists solve the tasks of collecting and processing large volumes of information.
Recommended or required reading and other learning resources/tools
1. Vince Reynolds. Big Data For Beginners. Createspace Independent Publishing Platform. 2016.
2. Bernard Marr. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. Wiley. 2016.
3. Balamarugan Balusamy, Nandhini Abirami R, Seifedine Kadry and Amir Gandomi. Big Data: Concepts, Technology and Architecture . Wiley.
4. Bill Chambers and Matei Zaharia. Spark: The Definitive Guide: Big Data Processing Made Simple. O'Reilly Media, Inc.
5. Martin Kleppman. Designing Data-Intensive Applications. O'Reilly Media, Inc.
6. Joel Grus. Data Science from Scratch. Published by O’Reilly Media, Inc., 2015.
7. Hadoop and Big Data [Електронний ресурс] – Режим доступу :
http://www.cloudera.com/content/cloudera/en/about/hadoop-and-big-data.html.
Planned learning activities and teaching methods
Lectures, laboratory activities, individual work
Assessment methods and criteria
Control of students' knowledge is carried out according to the modular rating system. The results of students' educational activities are evaluated on a 100-point scale. Work in the semester is divided into two content modules (30+30). Mandatory for the exam is the student's defense of the laboratory works provided for in the work program of the academic discipline, and the passing of two modular control papers. The final control (exam) is conducted in the form of a written work - 40 points.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Faculty of information Technology
Departments
The following departments are involved in teaching the above discipline
Faculty of information Technology