Навигация

Mathematical and Computational Science

About the program

The educational program offers a comprehensive curriculum that prepares students for three future directions: engineering, bioengineering, and engineering of social processes. In the field of engineering, students gain expertise in mathematical modeling, computational algorithm development, and simulation techniques to solve complex engineering problems and optimize systems. In bioengineering, the program focuses on applying mathematical and computational approaches to analyze biological systems, design data analytics and statistics tools to biological and genetical applications. Lastly, in the engineering of social processes, students learn to employ mathematical modeling, data analysis, and computational tools to study social phenomena, analyze social networks, predict trends, and improve decision-making processes in areas such as economics, public policy, and social sciences. This program equips graduates with the skills to make meaningful contributions in the rapidly evolving technological landscape across these diverse fields.

6B06107 Mathematical & Computational Science

Profile subjects: Mathematics, Informstics.

Threshold Score (Grant): 100.

Objective of Educational Programm

To train specialists in modeling production processes and forecasting social phenomena based on differential and integral equations, computational experiments and big data analysis, who have a solid fundamental knowledge of mathematics and are highly qualified in applied methods based on computational technologies.

List of a specialist’s positions

Database Administrator
Technician
AI Developer
Developer of Mathematical Models
Head of the organization
Head of the structural uniit
Employee of the national scientific and practical center, university.
Software Developer
High Load Application Developer
HPC calculator
Computational Experiment Programmer
Deputy head of the structural unit
Expert of the republican center

Learning outcomes

To be able to think critically and analyze assigned tasks, possessing general flexible skills in the preparation and presentation of results and documents, knowledge of languages and social interactions to ensure fruitful work, both individually and in teams.
To be able to carry out all stages of the development of information systems and software at different scales to prepare parts or entire software products.
To formulate and prove fundamental laws and theorems in the fields of mathematical modeling and computational sciences, analyze and discuss the results of computational experiments for scientific research.
To know and select mathematical models and analyze them for convergence and stability in order to predict the course of real processes in the relevant industry.
To formulate and tune the methods of computational sciences in order to optimize, solve new problems, adapt algorithms to new computing platforms.
To be able to collect, process and analyze data, using the methods of mathematical statistics, data science and machine learning to make forecasts and prepare managerial and operational recommendations.
To apply high-performance computing algorithms based on embedded, medium- and large-scale computing systems to solve real industrial production problems.
Develop mathematical models, develop numerical algorithms and choose computational methods for conducting computational experiments and predicting the course of deterministic and probabilistic processes.
Formulate and apply methods of mathematical modeling, computational methods and data analytics methods for engineering production problems
Formulate and apply methods of mathematical modeling, computational methods and methods of data analytics for problems of bioinformatics and genetics
Formulate and apply methods of mathematical modeling, computational methods and data analytics methods for social engineering problems and social network analysis

B057 – Information Technologies

Educational group

Bachelor of Science in Information and Communication Technology in Educational Program «6B06107 Mathematical & Computational Science»

Awarded degree

3 years

Duration of studies

Program Structure

GED – General Education Disciplines
CC – Compulsory Component
EC– Elective Component

№	Course Cycle	Course Component	Course Code	Course Title	Academic Credits
1	GED	СС	Fiz 1112	Physical Education	2
2	GED	CC	HSS 1162 Cult 1111	Cultural Studies	2
3	GED	CC	IT1115 IKT 1105	Information and Communication Technologies	5
4	GED	CC	HSS 1115 IYa 1103	Foreign Language 1	5
5	GED	СС	HSS1145 (SIK2022)	History of Kazakhstan (State Exam)	5
6	GED	СС	Fiz 1113	Physical Education	2
7	GED	CC	HSS 1122 HSS 1132 (Soz 2109)	Sociology	2
8	GED	CC	HSS 1215 FL2023	Foreign Language 2	5
9	GED	CC	Fiz 1114	Physical Education	2
10	GED	CC	HSS 1132 MSP 2315	Political Science	2
11	GED	CC	HSS 1182 (MSP 2313)	Psychology	2
12	GED	CC	Fil 2102	Philosophy	5
13	GED	CC	Fiz 2116	Physical Education	2
14	GED	CC	K(R) Ya2105	Kazakh (Russian) Language 1	5
15	GED	СС	K(R) Ya2106	Kazakh (Russian) Language 2	5
16	GED	EC	TP 3113 / FL25 / Pred 2116	Technological Entrepreneurship / Financial Literacy / Entrepreneurship	5

BD – Basic Disciplines
UC – University Component
EC – Elective Component

№	Course Cycle	Course Component	Course Code	Course Title	Academic Credits
1	BD	UC	MATH 1115 MA1 1202	Calculus 1	5
2	BD	UC	—	Introduction to Programming	5
3	BD	UC	MATH 2145 DM 2207	Discrete Mathematics	5
4	BD	UC	AG2025	Analytic Geometry	5
5	BD	UC	CS 2155 OOP	Object-Oriented Programming	5
6	BD	UC	MATH 2125 LA 1201	Linear Algebra	5
7	BD	UC	MATH 1215 MA2 1203	Calculus 2	5
8	BD	UC	UP SIS 1211	Educational Practice	2
9	BD	UC	ODE 1111	Ordinary Differential Equations	5
10	BD	UC	CS 2055 ASiD 1205	Algorithms and Data Structures	5
11	BD	UC	ViS 2212	Probability and Statistics	5
12	BD	UC	MA3	Calculus 3	5
13	BD	UC	VM 2205	Есепті математикасы	5
14	BD	UC	PhCFM	Physics: Classical and Fluid Mechanics	5
15	BD	UC	AK 3221	Academic Writing	5
16	BD	UC	CLAIM	Computational Linear Algebra and Iterative Methods	5
17	BD	UC	CFM	Computational Fluid Mechanics	5
18	BD	UC	CFDSAOF	CFD simulation in Altair/OpenFoam	5
19	BD	EC	HP / PA	Heterogeneous Parallelization / Parallelization of Algorithms	5
20	BD	EC	DC 3224 / PPP2025	Distributed computing / Parallel Processing Practices	5
21	BD	EC	PhQM / IMLAI	Physics: Quantum mechanics / Introduction to Machine learning and Artificial Intelligence	4
22	BD	EC	IQC / MLCS	Introduction to Quantum Computing / Machine learning for Computational science	5

MD – Major Disciplines
UC – University Component
EC – Elective Component

№	Course Cycle	Course Component	Course Code	Course Title	Academic Credits
1	MD	UC	DUChP	Partial Differential Equations	5
2	MD	UC	ChMDU	Numerical Methods for Differential Equations	5
3	MD	UC	PP 2305	Industrial Practice	4
4	MD	UC	IOTh	Introduction to Optimization Theory	5
5	MD	UC	SP2025	Stochastic Processes	5
6	MD	UC	Mill 3222	Research Methods and Tools	5
7	MD	UC	VV 3110	High Performance Computing	5
8	MD	UC	AO2025	Advanced Optimization	5
9	MD	UC	MMC	Monte-Carlo Methods	5
10	MD	UC	PP 3306	Industrial Practice	8
11	MD	UC	PP 3307	Undergraduate Practice	4
12	MD	EC	DT / CGNM	Decision theory / Computational Geometry for Numerical Methods	5
13	MD	EC	OV 3222 / MPB	Cloud Computing / Mathematical Population Biology	5
14	MD	EC	GOOP 3210 / QP2025	Deep and Reinforcement Learning / Quantum Programming	5