Parallel algorithms for the analysis and synthesis of data

Entry requirements: Basic knowledge of programming, algorithms and data structures. Also would be useful to know parallel and distributed computing.

Credits: 2

Semester: 1

Course: Core

Language of the course: English

Lecturer

Katerina Bolgova

Objectives

Students will learn basic approaches and effective algorithms for data analyzing.
Students will learn parallel processing methods; methods of developing and analyzing conceptual and theoretical models of solvable scientific problems and problems.
Students will learn to develop and implement efficient parallel data processing algorithms, use electronic sources of information in the field of parallel programming, use them in practice, develop and analyze conceptual and theoretical models of solved scientific problems and tasks.
Students will gain self-study research skills; work with modern information technology in conducting research; working with scientific search methods, skills in working with parallel data processing software; skills in working with software supporting the organization of storing big data and mechanisms for their processing; skills in independently finding the necessary information in the field of parallel programming, as well as its application in professional activities.

Contents

The course consists of lectures about algorithms and approaches for data analysing. At a practical part, students will implement described techniques and perform assigned tasks.

Format

Lectures and lab sessions

Assessment

Credit.

Attendance is mandatory. Students cannot miss more than one class. Final assessment is carried out in the form of credit. The necessary condition to be admitted to the credit is a successful completion of all practical assignments.