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Master’s degree program “Computational Sciences”

7M06104 - Computational Science

The educational program “Computational Sciences” involves the use of modern models, methods and approaches in the field of data analytics, machine learning, artificial intelligence, as well as modeling and analysis of processes in the learning process. The educational program involves the use or application of acquired knowledge in one of the areas of the economy, thereby covering not only the scientific component of the program, but also the applied part. As a result of training in the educational program “Computational Sciences”, graduates will have the opportunity to work in scientific and industrial projects as a data analyst, business intelligence expert, process engineer.

Contacts

Admission Committee

(7172) 64-57-10
info@astanait.edu.kz

Mon-Fri 9:00 – 18:00

Classifier of directions

  • Group of educational programs: M094 – Information and communication technologies
  • Educational program "Computational Sciences"
  • Awarded academic degree: Master of Science in the educational program 7M06104 "Computational Sciences"

Scientific and pedagogical direction - 2 years

Purpose of the educational program

The goal of the educational program is to provide training of highly qualified scientific and applied specialists and software engineers in the field of modeling, algorithms and analysis of large data, as well as managers and managers of software and information systems for the information technology industry and interdisciplinary industries related to the protection and processing of data in various sectors of the economy of the Republic of Kazakhstan.

Objectives of the educational program

  1. To train highly qualified scientific and applied specialists and engineers in the direction of computational sciences and the application of the acquired knowledge in various sectors of the economy.
  2. To educate on conducting research work related to the objects of professional activity and to give the ability to analyze existing algorithms, models and methods of data analysis, as well as general concepts, theory and approaches to computational sciences;
  3. To develop students’ skills to create new models and methods of computational sciences, as well as improve existing methods and algorithms of computational sciences in information and computing systems;
  4. To teach students to apply the received theoretical and practical knowledge in solving practical problems in the field of information and communication technologies and interdisciplinary industries, as well as to successfully carry out management and research activities.
  5. To instill in students the skills of independent work, as well as to show the importance of continuous development and application of professional knowledge, skills and abilities to solve non-standard tasks.
  6. To teach undergraduates to apply the knowledge of pedagogy and psychology of higher education required in their professional teaching activities, as well as to give the ability to use interactive teaching methods to improve the availability of presentations and materials.
  7. To acquaint undergraduates with methods of conducting a research and systems analysis to solve complex technical problems and apply the results of the analysis for the greatest optimization of the process of computational sciences.
  8. To teach students to optimize algorithms and models of computational sciences in order to obtain the required result in solving problems in the minimum number of iterations and the required computing resources.
  9. Train undergraduates to summarize the results of research and analytical work in the form of a dissertation, scientific articles and reports at scientific and technical conferences, as well as provide assistance in writing academic reports, analytical notes and others.

Requirements for the results of mastering the educational program

The student, after mastering the entire educational program, should be able to perform the following points:
  • Formulate and solve problems arising in the course of research activities that require in-depth professional knowledge. To formulate the problem, both mathematical apparatus and computer tools can be used;
  • Choose the necessary approaches and research methods, as well as modify existing ones and develop new ones, depending on the objectives of a particular study or case;
  • Apply methodological and methodological knowledge in conducting scientific research, as well as in pedagogical and educational work;
  • Apply psychological methods and means to improve the efficiency and quality of education in the learning process;
  • Know a foreign (English) language at a professional level, allowing students to conduct scientific research at a qualitatively high level and to teach special disciplines in universities;
  • Model and design complex systems using mathematical and computer models and methods;
  • Apply quantitative and qualitative methods and techniques to collect primary information for research, as well as develop effective solutions to problems;
  • Analyze and design data analysis software tools, as well as algorithms, models and methods required for effective data analysis and knowledge extraction from data;
  • Manage a team of analysts in the process of developing software systems for data analysis, as well as models and methods for data analysis;
  • Choose standards, methods, technologies, tools and technical means for carrying out work on further maintenance of data analysis software systems;
  • Apply methods of designing and developing software systems to solve a wide class of applied problems in various fields, including interdisciplinary industries;
  • Program and test various solutions (models, methods) for data analysis, take part in the creation and management of data analysis systems at all stages of the system development life cycle;
  • Create relational and non-relational databases for efficient storage and management of data in various large organizations, government agencies and other companies;
  • Create analysis models for structured, semi-structured and partially unstructured data;
  • Develop programs and applications for analytical processing of structured and semi-structured data of huge volumes;
  • Analyze the complexity of calculations and the possibility of parallelization (optimization) of the developed algorithms and programs;
  • Evaluate the main parameters of the resulting parallel programs, such as numerical indicators of the required computing resources, acceleration, efficiency and scalability;

The list of competencies and the results of the educational program

Competencies

OK1. The ability to understand the driving forces and patterns of the historical process, the place of a person in the historical process and the ability to understand philosophy as a methodology of human activity, readiness for self-knowledge, initiative, development of cultural wealth as a factor in harmonizing personal and interpersonal relationships
OK2. The ability to form and develop skills and competencies in the field of organization, planning and production management, the ability to apply the acquired knowledge to comprehend the surrounding environmental reality, the ability to summarize, analyze, predict when setting goals in the professional field and choose ways to achieve them using the scientific research methodology
OK3. Ability for written and oral communication in the state language and the language of interethnic communication, as well as in a foreign (English) language. The ability to use foreign sources of information, to have communication skills, to public speaking, argumentation, conducting discussions and polemics in a foreign language
OK4. The ability to be competent in the choice of ICT and mathematical modeling methods for solving specific engineering problems, the ability to be ready to identify the natural science essence of problems arising in the process of professional activity, and the ability to attract the appropriate mathematical apparatus to solve it
PC1. The ability to use acquired knowledge for original development and application of ideas in the context of scientific research.
PC2. The ability to critically analyze existing concepts, theories and approaches to the analysis of processes and phenomena.
PC3. The ability to independently and constantly acquire, develop and apply professional knowledge, skills and abilities to solve non-standard problems (interdisciplinary, etc.).
PC4. The ability to apply the knowledge of pedagogy and psychology of higher education in his teaching activities, and is also able to apply interactive teaching methods.
PC5. Knowledge of a foreign language at a professional level, which allows conducting scientific research and teaching special disciplines in universities PC6. The ability to design the architectures of components of information systems, including the human-machine interface of hardware and software systems, and to select operating systems and information protection methods.
PC6. The ability to select and develop methods for analyzing objects of professional activity based on general trends in the development of the field of computational sciences.
PC7. Ability to apply the acquired theoretical and practical knowledge in solving practical problems in the field of ICT, successfully carry out management and research activities.
PC8. Ability to independently formulate the subject area when solving data analysis problems, determine the requirements and expectations of the end user or customer, draw up a phased plan and develop documentation for the data analysis software system and its components.
PC9. The ability to conduct systems analysis to solve complex technical problems and apply the results of the analysis to the greatest optimization of the algorithm for solving problems in computational sciences.
PC10. The ability to apply effective methods to manage a computational science project in a specific environment, distribute tasks and manage a team of analysts.
PС11. The ability to develop software system architectures for computational sciences with a high level of continuity and quality of complex software developments using advanced solutions and trends in the field of ICT.
PС12. The ability to analyze the requirements to solve complex software (technical) problems and ensure the implementation of the most optimal solutions.
PC13. The ability to implement innovative methods and improvements that enhance the competitiveness and efficiency of computational science models and methods at all stages of the software system development life cycle.
PC14. Capability to optimize computational science algorithms while minimizing all required resources, including computational resources.
PC15. The ability to generalize the results of research and analytical work in the form of a dissertation, scientific article and reports at scientific and technical conferences.

Learning Outcomes

LO1. Developing methods and algorithms for computational mathematics based on the approximation of differential equations by methods of finite differences, volumes or elements
LO2. Conducting a fundamental analysis of computational methods and difference schemes for convergence and correctness, including in the case of high-performance algorithms using elements of mathematical logic and the theory of computability
LO3. Solving computational problems with complex geometry of regions by building and using correct structured, curvilinear, unstructured computational grids
LO4. Using data mining techniques based on deep learning, reinforcement learning, generative adversarial networks to effectively predict outcomes
LO5. Developing parallel computational algorithms for engineering problems and implementing them in high-performance systems, developing quantum computing algorithms
LO6. Developing and conducting computational simulations of probabilistic processes from various industries using stochastic modeling methods
LO7. Using methods of data analysis in various areas of production, on real data for the selection of parameters, adaptation and testing of computing systems based on real experiments
LO8. Conducting independent research, solving modern urgent problems, publishing results

Requirements for evaluating learning outcomes

#

Exam form

Recommended weight, %

1

Computer testing

20%

2

Writing

10%

3

Oral

5%

4

Project

30%

5

Practical

30%

6

Complex

5%

Educational program documents

«Astana IT University»

Course Curriculum

The cycle of general education disciplines

Mandatory component

History and philosophy of science

The content of the course is aimed at obtaining knowledge about the properties of science as a type of cognition and a socio-cultural phenomenon in its historical development by a master student; formation of system ideas about the general laws of scientific knowledge in its historical development and changing socio-cultural context.

Pedagogy of higher education

The content of the course is aimed at acquiring knowledge about the foundations of pedagogical theory and pedagogical skills, the management of the educational process for teaching in higher education, the formation of an understanding of the main categories of pedagogy, the place, role and significance of higher education pedagogy, understanding the basic principles of modern pedagogy and methodological approaches to solving pedagogical problems of higher education.

Foreign language (professional)

The aim of the course is to develop professional competences of specialists; to form professionally oriented communicative competence of master’s students, which allows them to integrate into international professional environment and use professional English as a means of intercultural and professional communication.

Psychology of management

The content of the course is aimed at the formation of systemic ideas about the psychological laws of management, the specifics of using social and psychological knowledge and mastering the skills of analyzing the social and psychological principles that underlie effective management.

The cycle of basic disciplines

Component of choice

Algorithms in Graph Theory

The discipline “Algorithms in Graph Theory” involves the study of the basic concepts of a graph: connectivity, finding paths in a graph, tree concepts and algorithms associated with trees, placement problems, matchings and flows, as well as common algorithms for solving these problems.

Numerical geometry

This discipline is aimed at studying methods of programmatically representing geometric objects using advanced object-oriented programming and design tools for their further use in the study of complex algorithms of two-dimensional and three-dimensional geometry.

Introduction to Partial differential equations

This discipline involves the study of the foundations of partial differential equations, their types and some methods for the analytical solution of such equations. After studying the discipline, the student must know: types of partial differential equations; analytical and numerical solution concepts; basic methods of analytical solution of partial differential equations; be able to: determine the type of equation; identify and apply simple methods for the analytical solution of an equation.

Numerical methods and computer modeling

This discipline involves the study of the fundamentals of numerical methods in the field of modeling physical processes, including algebraic numerical methods, numerical integration and numerical solution of partial differential equations, it also studies the introduction to methods of finite differences.

Heterogeneous parallelization

The discipline will help you understand, use and design the latest parallel and heterogeneous systems. In it, students will learn how modern systems work, and review recent research in this area, consider both hardware and software aspects, from computer architecture to programming models. They will have a holistic view of what successful approaches need to take into account both hardware and software.

Introduction to neural networks

This discipline involves the study of the sections of mathematics and computer science necessary to introduce to the theory of machine learning and its section the theory of deep learning based on the backpropagation algorithm, which allows the AI system to learn itself. This section contains the tasks of image recognition, image and 3D generation , text , sounds, recognition and etc., it is one of the most common areas in modern machine learning.

High-performance computing

The aim of the discipline is to study the fundamental techniques for developing HPC applications, the commonly used HPC platforms, the methods for measuring, assessing and analysing the performance of HPC applications, and the role of administration, workload and resource management in an HPC management software. The students will be introduced to the issues related to the use of HPC techniques in solving large scientific problems.

Genetic algorithms

This discipline is aimed at studying genetic algorithms. Such algorithms are interesting for computational experiments, giving an understanding of the development of complex structures that depend on simple parameters, and can also work to improve the efficiency of classical algorithms.

Markov chains and decision-making processes

This discipline involves the study of Markov chains, in which each element is completely determined by the previous one. These chains are widely used in the formulation of problems of linking artificial intelligence to the behavior of an agent in a certain environment, for example, a robot in a real environment, on which, for example, reinforcement learning is based. As a result of studying the discipline, the student must know: methods of constructing probabilistic models describing the stochastic dynamics of processes; queuing systems; be able to establish the properties of solutions to stochastic systems.

Fractional step methods

This discipline is aimed at studying some approaches of the finite difference method, namely, fractional step methods for solving boundary value problems for partial differential equations. Such methods include methods of alternating directions, stabilizing corrections, longitudinal-transverse sweep, etc. Upon mastering the discipline, the student must know: basic methods of fractional steps, algorithms for iterative solution of boundary value problems for parabolic and elliptic equations; be able to: solve practical problems using the described methods, investigate the convergence of a solution, etc.

Stochastic modeling

This discipline is directed to the basics of stochastic modeling, practical application of Monte Carlo methods, solving stochastic differential equations, probabilistic modeling for solving practical problems.

Reinforcement learning

This course will introduce students to the basics of reinforcement learning. Upon completion of this course, the student will be able to: Formalize problems as Markov decision-making processes; Understand basic exploration techniques and trade-offs between exploration and exploitation; Understand value functions as a universal tool for making optimal decisions; Know how to implement dynamic programming as an effective approach to solving industrial control problems.

Applied mathematical models

This discipline focuses on common mathematical models used in manufacturing and their solution using numerical methods. Upon mastering the discipline, the student must know: basic mathematical models such as “predator-prey”, the equation of heat conduction, etc .; basic models of hydrodynamics, filtration, chemical reactions; be able to: approximate and investigate the convergence of the model; apply basic numerical methods to solve applied problems.

Quantum computations

This discipline involves the study of quantum computing methods and their advantages over classical computation methods. The course covers the main provisions of the classical theory of computational complexity, the gate model of quantum computing, universal sets of gates, quantum computing algorithms based on the quantum Fourier transform, in particular, Shor’s algorithm, quantum search algorithms, algorithms for quantum simulation of physical systems, an introduction to quantum error correction, and error-resistant computations, hybrid quantum-classical algorithms, in particular, variational quantum algorithms.

Generative adversarial networks

This discipline focuses on the latest techniques for generative adversarial networks and their use to create realistic images and three-dimensional structures. Upon mastering the discipline, students should know: the concept and organization of the generative model; the concept and organization of the discriminative model; be able to: train generative adversarial networks and generate images with their help, starting from basic handwritten numbers, to restoration, correction, coloring of photographs; generate 3D.

Theory of adaptive computational grids

This discipline is devoted to methods of constructing unstructured and structured grids that adapt to certain properties of the area and their use for solving numerical problems in these areas. Such methods of structured grids as methods of equidistribution, Thompson”s method, and such methods of unstructured grids as Delaunay triangulation, Voronoi diagram are considered.

Mathematics for Computational Science

This discipline covers an introduction to mathematical courses necessary for mastering specialized disciplines of computational science based on numerical solutions of deterministic and probabilistic equations of mathematical physics and applied models used in technical production and the financial sector. Namely, it covers the theory of ordinary differential equations, their typification and basic methods of analytical solution and an introduction to partial differential equations.

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