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Магистратура 2020/2021

Программирование наукоемких вычислений

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»
Лучший по критерию «Новизна полученных знаний»
Направление: 01.04.04. Прикладная математика
Когда читается: 1-й курс, 1, 2 модуль
Формат изучения: с онлайн-курсом
Прогр. обучения: Суперкомпьютерное моделирование в науке и инженерии
Язык: английский
Кредиты: 4

Course Syllabus

Abstract

Numerical computing is an integral part of modern-day scientific research, data analysis and engineering. The art of scientific computing - and the skill of an engineer, researcher or analyst - is a blend of understanding the basic principles of numerical computing, the knowledge of specialized libraries that package individual computational routines with their strengths and limitations, and the ability to mix-and-match these computational primitives into a coherent computational systems that solve business domain problems. This course belongs to the group of adaptation courses, and is designed to bring students up to speed with numerical methods and scientific computing at the level required for further study at the master's level.
Learning Objectives

Learning Objectives

  • The main objective for the course is the development of students' skills of designing and implementing computational models with Python programming language (or other high level programming language of the student's choice), and using contemporary development tools.
Expected Learning Outcomes

Expected Learning Outcomes

  • Upon successful completion of the course, a student will be able to •Demonstrate the ability to analyse information and synthesise mathematical models. •Demonstrate the ability of self-directed learning. •Demonstrate the ability to develop non-trivial computational algorithms based on specialized literature and implement them in software. •Use modern development environments, tools and software packages. •Independently develop computational models.
Course Contents

Course Contents

  • The subject of numerical analysis. Building computational pipelines.
  • Numerical linear algebra
  • Finite difference schemes for solving ordinary differential equations.
  • Building compiled extensions for Python programming language.
  • Integration of functions. Methods of solving integral equations.
Assessment Elements

Assessment Elements

  • non-blocking Онлайн-компонента
  • non-blocking Домашнее задание
  • non-blocking Вопрос по выбору
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.3 онлайн компонента + 0.4 домашнее задание + 0.3 вопрс по выбору
Bibliography

Bibliography

Recommended Core Bibliography

  • Программирование на PYTHON. Т. 1: ., Лутц, М., 2013

Recommended Additional Bibliography

  • Linear algebra : concepts and methods, Anthony, M., Harvey, M., 2012