Bachelor courses
Speciality Code:
6B06107
Speciality Name:
Data Science
Faculty:
Information technology
Qualification:
- First Stage of Higher Education - Bachelor in Information and Communication Technology
- Model of graduating student
- Mandatory disciplines
- Elective disciplines
- Professional
1. Analyze the features of social, political, cultural institutions in the context of their role in the modernization of Kazakhstani society, describe the stages of the formation of an independent Kazakhstani statehood in the context of the world and Eurasian historical process.
2. Be able to understand and apply modern methods of management, mining and analysis of big data in various fields.
3. Apply concepts and methods of data science to solve problems in real-world settings and will effectively communicate these solutions to your data management skills.
4. Determine suitable tools and methods for solving the main classes of machine learning problems and interact with developers.
5. Use modern computer technologies such as machine learning, artificial intelligence, parallel and distributed computing, information security to solve practical problems characterized by large-scale data
6. Acquiring professional skills in working with big data and building analytical models for the financial sector of the economy.
7. Perform design, development and testing of software, develop web applications with an ergonomic user interface based on an agile methodology.
8. Apply artificial intelligence methods in solving problems and making decisions, testing, implementing and maintaining artificial intelligence systems.
9. Use the data collection process to ensure the completeness and interconnectedness of data from different sources and to develop solutions to optimize current processes.
10. Model logical data structures, defining data composition, structure and data sources, ensuring data protection.
11. Conduct big data analysis, design and develop software for storing, processing and analyzing big data, use cloud platform services to support modern application architectures.
12. Work in a team, tolerantly perceiving social, ethnic and cultural differences, critically assess their own activities, the activities of the team.
2. Be able to understand and apply modern methods of management, mining and analysis of big data in various fields.
3. Apply concepts and methods of data science to solve problems in real-world settings and will effectively communicate these solutions to your data management skills.
4. Determine suitable tools and methods for solving the main classes of machine learning problems and interact with developers.
5. Use modern computer technologies such as machine learning, artificial intelligence, parallel and distributed computing, information security to solve practical problems characterized by large-scale data
6. Acquiring professional skills in working with big data and building analytical models for the financial sector of the economy.
7. Perform design, development and testing of software, develop web applications with an ergonomic user interface based on an agile methodology.
8. Apply artificial intelligence methods in solving problems and making decisions, testing, implementing and maintaining artificial intelligence systems.
9. Use the data collection process to ensure the completeness and interconnectedness of data from different sources and to develop solutions to optimize current processes.
10. Model logical data structures, defining data composition, structure and data sources, ensuring data protection.
11. Conduct big data analysis, design and develop software for storing, processing and analyzing big data, use cloud platform services to support modern application architectures.
12. Work in a team, tolerantly perceiving social, ethnic and cultural differences, critically assess their own activities, the activities of the team.
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Physical Training
- Number of credits: 2
- Type of control: РК(с оценкой)
- Description: The purpose of the discipline is the formation of social and personal competencies of students, ensuring the targeted use of the appropriate means of physical culture and sports for preservation, preparation for professional activities. As a result of studying the discipline, the graduate should know the role of physical culture in human development.
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Kazakh (Russian) Language
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description:
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Module of socio-political knowledge (Sociology/ Political science/ Culture/ Psychology)
- Number of credits: 8
- Type of control: RK + Exam (100)
- Description: Aim оf discipline: to develop the ability to explain and interpret subject knowledge in all fields of science, shaping of the discipline. Sociology and sociological perspectives, social structure, form of policy, organizational structure, institutions, the legal and organizational rules, content, purpose, value, policy, concept and essence of culture, semiotics of culture, psychology of personality, psychology of interpersonal communication will be studied.
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Mathematics-1 (Mathematical Analysis)
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the study of the discipline "Mathematics-1" is to study methods, problems and applicability of the application of mathematical analysis, mastering the results of their solution to problems of applied mathematics and informatics. The discipline is aimed at the formation of skills in solving mathematical and applied problems of natural science, at the development of logical thinking, the ability to analyze the application of theory in different situations, to compare, contrast the results.
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Introduction to Data Engineering
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to form the ability to collect, process, analyze and visualize data, which allows you to effectively make decisions and act on data. As a result of studying the discipline, the ability of students to form: 1. To carry out storage, management and preliminary processing of data, as well as to perform research analysis of data. 2. Collect structured and unstructured data using API and web processing. 3. Configure analytics, systems and methods, oriented to the user. 4. Present the results of the analysis using visualization methods. 5. Observe the ethical principles of collection and manipulation of data. Within the discipline the following aspects will be considered: : Life cycle of data engineering. Ecosystem engineering data. Type of data storage. Showcase data and lake data. Process ETL and ELT. Data conveyor. Data integration platform. Big data. Aspect of safety. Life cycle data management. Compliance with data privacy rules.
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Higher Mathematics
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to use the knowledge of the branches of higher mathematics in applied problems. The discipline contains the following sections: Elements of linear algebra. Matrices and determinants. Systems of linear algebraic equations. Vectors. Equations of the line. Second order equations. Function limit. Continuity of function. Derivative function. Differentiation rules. Functions of several variables. Extreme functions of several variables. Indefinite integral. The main methods of integration. Certain, improper integrals. Applications of a certain integral.
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Discrete mathematics and mathematical logic
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal is the formation of knowledge and skills of future specialists in the use of apparatus and methods of discrete mathematics in the analysis, management and programming of modern processes and systems and to form the ability using the mathematical logic for the study of mathematical objects.
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Methods of Optimisation and Research of Operations
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to apply optimal search methods based on mathematical modeling and various heuristic approaches to solving practical pro blems. As a result of studying the discipline, form the following abilities: to formulate the objectives of operations research; build a meaningful model of the object (process) under consideration; develop a mathematical model of the object (process) under consideration; solve problems formulated on the basis of the constructed mathematical model; check the results obtained for their adequacy of the original model. Within the discipline the following aspects will be considered: Methods of mathematical programming. The concept of convex sets. Convex set theorems. The general task of linear programming. The main theorems of linear programming. The method of successive improvement of the plan (simplex - method). Duality theory in linear programming. Distribution method. Model and theorem on the solvability of the problem. Parametric linear programming. Discrete programming. Integer programming. Dynamic programming. Nonlinear programming.
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Applied Data Science Project
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline that emphasizes hands-on implementation and management of data science projects. Students gain experience in data-driven problem-solving, teamwork, and communication, thereby learning how to apply data science methods and principles to real-world scenarios. The course aims to introduce students to the hands-on management and implementation of data science projects, to develop their skills in data-driven problem-solving, and to enhance their teamwork and communication skills in a data science context. Additionally, it seeks to enable students to apply data science methods to real-world scenarios and to foster a strong understanding of the data science project lifecycle. On completion, students are expected to manage and implement data science projects effectively, showcase expertise in data-driven problem-solving, and demonstrate improved teamwork and communication skills in a data science context. They will also be prepared to apply data science methods to real-world scenarios and will possess a strong understanding of the data science project lifecycle. Within the discipline the following aspects will be considered: the management and implementation of data science projects, data-driven problem-solving techniques, teamwork and communication in a data science context, application of data science methods to real-world scenarios, and an understanding of the data science project lifecycle.
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Big Data ecosystems
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability to create and use new generation information technologies designed to cost-effectively extract useful information from large volumes of various data by high-speed collection, processing and analysis to provide information and analytical activities, decision support, and the creation of innovative products. and services to improve management efficiency and competitiveness of organizations in any industry economy. As a result of studying the discipline, form the following abilities: extract useful information from large amounts of diverse data; to design innovative products and services in order to improve the management efficiency and competitiveness of organizations in any sectors of the economy; process data using big data technologies; analyze to support decision making. The discipline covers the following aspects: Types of big data processing ecosystems. Hadoop. Hbase. Hive. Pig. Sqoop. Spark. Shark. MySQL. MongoDB.
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Data Security
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to develop the ability to ensure data protection at rest, during processing, and during transmission in data-driven applications. Upon completion of the discipline, students will be able to: Describe the methods of using cryptography in data transmission. Demonstrate mathematical understanding of encryption algorithms. Select cryptographic protocols, tools, and methods that are suitable for a given situation. Explain how to secure data transmission over networks or the Internet. Conduct threat analysis for real-time applications that consume/produce data. Within the discipline the following aspects will be considered: Cryptographic concepts. Encryption/decryption, message authentication, data integrity. Classification of attacks. Private key. Public key. Threat models for data-driven applications. The role of mathematical methods in gaining useful knowledge about encryption. Public key cryptography for data protection.
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Business Analytics and Data Visualization
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability to operate with the capabilities of tools for conducting business analysis and building visual data models for analyzing the state of the business. Introduction to Business Intelligence, Data Visualization. Big data in trade. Big data security. Big data in banking.
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Introduction to Data Science
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to leverage key technologies in data science and analytics, including data mining, machine learning, visualization techniques, predictive modeling, and statistics. Mathematical Instruments of Science. Software science tools. Machine learning: training with a teacher. Machine learning: training without a teacher.
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Web programming
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to design and develop web applications. As a result of studying the discipline, students have the ability to form: - design web applications according to the terms of reference; - develop data models, apply the principles of object-oriented design and programming; - develop web applications on the .NET platform; - design databases for web applications, write SQL queries; - perform adaptive layout on the client side, administer the IIS server and publish web applications. Within the discipline the following aspects will be considered: Architecture of client-server technologies: client side, server side. Designing a database of web applications, an object-oriented and subject-oriented approach in the design and development of web applications, developing MVC and web API applications, the basics of FRONTEND development (HTML / CSS / JavaScript). Application of the latest version of the BootStrap JS framework for the layout of a mobile site and Comet technology.
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Military Training
- Number of credits: 6
- Type of control: MC
- Description: Military Training
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Deep Learning
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to apply deep neural networks to solve computationally intensive tasks. The device of deep neural networks. Network architectures. Existing software systems for deep learning. Assessment of the quality of education. The problem of computer vision.
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Data Mining
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability of students to apply the main approaches in data mining and the development of an algorithm for solving analytical problems. Basics of data analysis. Collection and processing of data. Search for outliers and anomalies. Regression analysis. Linear regression. Polynomial Regression.
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Information-Communication Technologies
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: During the study of the discipline students will learn following aspects:ICT role in key sectors of development of society. Microsoft Office Windows Cybersafety. Internet technologies. Cloud and mobile technologies. Multimedia technologies. Smart Technology. E-technologies. Electronic business. E-learning. Electronic government. Information technologies in the professional sphere.
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Operations Research and Optimization Methods
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to apply optimal search methods based on mathematical modeling, various heuristic approaches to solving practical problems. Methods of mathematical programming. The concept of convex sets. Convex set theorems. The general task of linear programming. The main theorems of linear programming
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Нistory of Kazakhstan
- Number of credits: 5
- Type of control: [РК1+MT+РК2+ ГЭК] (100)
- Description: The purpose of the discipline is to give objective knowledge about the main stages in the development of the history of Kazakhstan from ancient times to the present. Expected learning outcomes: 1) demonstrate knowledge and understanding of the main stages in the development of the history of Kazakhstan; 2) to correlate the phenomena and events of the historical past with the general paradigm of the world-historical development of human society through critical analysis; 3) to possess the skills of analytical and axiological analysis in the study of historical processes and phenomena of modern Kazakhstan; 4) be able to objectively and comprehensively comprehend the immanent features of the modern Kazakh model of development; 5) Systematize and give a critical assessment of historical phenomena and processes in the history of Kazakhstan. During the study of the discipline students will learn following aspects: Ancient people and the formation of a nomadic civilization, Turkic civilization and the Great Steppe, Kazakhstan in modern times (XVIII - early XX centuries), Kazakhstan as part of the Soviet administrative-command system, Kazakhstan in the world community (1991-2022).
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Applied statistics
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to solve inverse problems of mathematical and applied statistics, to choose the optimal methods of analysis of experimental data and interpretation of results, the use of modern computing tools and mathematical software packages for analysis and visualization. Within the discipline the following aspects will be considered: Statistics. Statistics by order. Sample median and quartiles. Examples of moments. Sample selection. Examples of characteristics as ratings. Asymptotic normality of sample characteristics. Estimation of parameters and fitting of probability distributions. Least square parameter estimates. Maximum probability, Method of moments. Some statistical distributions: χ ^ 2-distribution, Student's t-distribution. Fisher's distribution. Interval estimates. Hypothesis testing and conformity assessment. Tests for the average. Likelihood ratio test. Chi-square of Pearson. Summarizing. Comparison of two samples. Analysis of variance. Analyzing categorical data. Linear regression. Testing hypothesis, confidence interval in linear regression models. Multiple regression.
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Political Science
- Number of credits: 2
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The academic discipline “Political science” forms knowledge of the laws and laws of world politics and modern political processes, explaining the essence and content of the policy of national states, on the basis of ensuring national security and the realization of national interests.
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Computer Networks
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is the organization of computer networks, the acquisition by students of knowledge and skills in the development of a local network, the solution of problems, the practical application of tools that allow practical implementation, debugging and commissioning. The following aspects are considered: Configuring the router. IPv4 and IPv6 network addresses. Calculation of the mask. TCP and UDP protocols.
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Culturology
- Number of credits: 2
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Aim of discipline is to form a bachelor's understanding of the specifics of the development of national culture in the context of world culture and civilization, need to preserve the cultural code of the Kazakh people, ability to pursue in independent professional activity a strategy of preserving the cultural heritage.
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Module of socio-political knowledge (Culture)
- Number of credits: 2
- Type of control: RK1+RK2 (100)
- Description: Aim оf discipline: to develop the ability to explain and interpret subject knowledge in all fields of science, shaping of the discipline. Sociology and sociological perspectives, social structure, form of policy, organizational structure, institutions, the legal and organizational rules, content, purpose, value, policy, concept and essence of culture, semiotics of culture, psychology of personality, psychology of interpersonal communication will be studied.
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Module of socio-political knowledge (Political science)
- Number of credits: 2
- Type of control: RK1+RK2 (100)
- Description: Aim оf discipline: to develop the ability to explain and interpret subject knowledge in all fields of science, shaping of the discipline. Sociology and sociological perspectives, social structure, form of policy, organizational structure, institutions, the legal and organizational rules, content, purpose, value, policy, concept and essence of culture, semiotics of culture, psychology of personality, psychology of interpersonal communication will be studied.
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Module of socio-political knowledge (Psychology)
- Number of credits: 2
- Type of control: RK1+RK2 (100)
- Description: Aim оf discipline: to develop the ability to explain and interpret subject knowledge in all fields of science, shaping of the discipline. Sociology and sociological perspectives, social structure, form of policy, organizational structure, institutions, the legal and organizational rules, content, purpose, value, policy, concept and essence of culture, semiotics of culture, psychology of personality, psychology of interpersonal communication will be studied.
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Module of socio-political knowledge (Sociology)
- Number of credits: 2
- Type of control: RK1+RK2 (100)
- Description: Aim оf discipline: to develop the ability to explain and interpret subject knowledge in all fields of science, shaping of the discipline. Sociology and sociological perspectives, social structure, form of policy, organizational structure, institutions, the legal and organizational rules, content, purpose, value, policy, concept and essence of culture, semiotics of culture, psychology of personality, psychology of interpersonal communication will be studied.
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Mathematics-2 (Algebra and Discrete Mathematics)
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to use the knowledge of the algebra and discrete mathematic in applied problems. Complex numbers. Matrices and determinants. Systems of linear algebraic equations. Polinoms. Sets and relations and operations on them. Elements of number theory and combinatorics. Boolean functions.
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Machine Learning
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to select an acceptable machine learning method for solving a specific data analysis problem, perform data preprocessing, configure the parameters of the analysis method and interpret the results obtained. An introduction to machine learning. Logic Methods: Decision Trees, Decision Forests.
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Operating Systems and Computer Networks
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to work with the structures and mechanisms of various operating systems, as well as in the Linux operating system. Linux. Functions and architectural requirements for the OS. General principles of resource management. Processes. File system architecture. Memory management. Input management.
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Artificial Intelligence Fundamentals
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline: is to form a holistic view of the current state of the theory and practice of building intelligent systems for various purposes. Within of the discipline the following aspects are considered: Introduction to artificial intelligence. Methods of heuristic programming. Heuristic search in the state space. Game model of heuristic search. Knowledge representation models. Logical representation of knowledge. Representation of knowledge by semantic networks. Frame model of knowledge representation. Possibilities of artificial neural networks. artificial neuron. Multilayer neural networks. Genetic algorithms. genetic programming. Principles of development of expert systems. Classification of expert systems. Development of expert systems based on Bayesian belief networks.
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Psychology
- Number of credits: 2
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of studying the discipline is to provide scientifically grounded training of highly qualified specialists on the basis of studying the fundamental concepts of psychology management, creating the necessary prerequisites for theoretical understanding and practical application of the most important management problems related to the process of professional development.
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Foreign Language
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Purpose: to form the improvement of knowledge of foreign language communicative competence. The main methods of speech skills and foreign language communication skills are considered as a basis for the development of communicative competence; implementation of acquired speech skills in the process of searching, selecting and using material in English.
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Programming Technologies
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to build the ability to develop application programs using methodology, basic paradigms and modern programming languages. General characteristics of programming languages. Basic constructions of modern programming languages (C ++, C #). Operations using data types and operators.
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Philosophy
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Purpose: to form a systematic understanding of philosophy and its main problems and methods in the context of future professional activities. The main content of ontology and metaphysics is considered in the context of the historical development of philosophy; the importance of key worldview concepts in the modern world.
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Theory of Probability and Mathematical Statistics
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability of a scientific understanding of the methods of studying random phenomena and the application of the studied methods to build probabilistic and statistical models. Within the discipline the following aspects will be considered: Distribution function of a random variable. Events and random variables. Moments of random variables. Conditional probabilities. Poisson distribution and some other distributions. Sampling research. Interval estimates. Analysis of variance. Joint distribution function of several random variables. Markov chains. Linear inhomogeneous differential equations of the n-th order. Linear boundary value problem for a linear differential equation of the second order. Green function, construction of the Green function. Integrals. General solution and general integral. Partial linear equations of the first order.
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Sociology
- Number of credits: 2
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The course presents general questions of theory and history of sociology, methodology and methods of sociological research, special sociological theories. This course is aimed at shaping the sociological imagination of students, basic ideas about the subject and methods of sociological research, topical problems and sociology branches.
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Database Theory
- Number of credits: 6
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to implement the conceptual, logical and physical design of databases; use querying languages to organize and manage data.Within the discipline the following aspects will be considered: Data abstraction and introduction to data management. Infological modeling and the "entity-relationship" model. Datalogical design and relational data model. SQL data manipulation language. Designing and testing a relational database. Ensuring data integrity. Data in non-relational form and knowledge. Modern data access technologies.
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Algorithms and Data Structure
- Number of credits: 9
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the course: to familiarize students with the classical and current state of the content of the subject «Algorithms and data structures», as well as applications of the content of the subject to various tasks, to show the relationship between the algorithms, data structures that process these algorithms.
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Operating systems
- Number of credits: 5
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to work with the structures and mechanisms of various operating systems, as well as in the Linux operating system. As a result of studying the discipline, students have the ability to form: - provide the basic setting of the operating system in the environment of its functioning - know the basic architectural concepts of building and distributions of operating systems - to select the distribution of the operating system and install it on a personal computer - know access rights management, word processing utilities and text editors in LINUX. Within the discipline the following aspects will be considered: Linux. Functions and architectural requirements for the OS. General principles of resource management. Processes. File system architecture. Memory management. Input control. Data management system. Network operating systems.
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Computer vision
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to offer students a comprehensive understanding of computer vision, a key area in artificial intelligence that focuses on enabling machines to extract, analyze, and understand data from images or multidimensional data. Within the discipline the following aspects will be considered: The course delves into the fundamentals of image processing, feature extraction and selection, object detection and tracking, image recognition and understanding, deep learning techniques for vision tasks, and practical implementation using popular computer vision libraries.
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NoSQL Databases
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to develop the ability to select and apply NoSQL database technology that best suits specific use cases. Upon completion of the discipline, students will be able to: Describe various types of NoSQL databases. Explain the differences between NoSQL and relational databases from a theoretical perspective. Develop NoSQL database management systems. Select the appropriate NoSQL database for specific use cases. Develop applications and perform integrations with NoSQL databases. Within the discipline the following aspects will be considered: Distinctive characteristics of NoSQL databases. Key concepts of NoSQL databases. NoSQL database technologies. Core data models of NoSQL. Development and utilization of NoSQL databases based on business needs. Criteria for choosing between relational and non-relational databases.
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Introduction to Smart Contract Architecture
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to develop the ability to create blockchain applications that automate the interaction of the network of participating entities. As a result of studying the discipline, the ability of students to form: - Configure the Ethereum platform with multiple nodes. - Apply tools for creation, deployment and monitoring of decentralized applications. - Write program code in Solidity for development of smart contracts. - Use web3.js, meteor for user interaction with blockchain. - To support the implementation of management, authorization, and authentication of entities in a decentralized application. Within the discipline the following aspects will be considered: Ethereum ecosystem and language of solidity software for the creation of smart contracts. Theproblem of scaling up technologies of distributed registers and their solutions is the implementation of smart contracts on the web and in the system.
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Introduction to the blockchain business model
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to describe blockchain-related business models, use blockchain technologies in various business scenarios. As a result of studying the discipline, to form students' abilities: - Describe various blockchain business models. - Identify specific business situations in which blockchain technology can be deployed to solve important problems. - Choose a particular blockchain technology that has the best chance of success in solving a particular problem. - Apply the principles of managing digital assets, conducting secure and transparent transactions, creating decentralized applications and much more. - Detail the risks associated with blockchain technology and apply blockchain security mechanisms. Within the discipline the following aspects will be considered:The concept of business models and their role in the context of blockchain. Different types of business models. The model is a network effect. Model Token omics. Decentralized autonomous organizations (DAO). Solutions for blockchain scaling. Network security. Security is critical. Introduction to Hyperledger. Security in blockchain. Blockchain security mechanism.
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Introduction to Blockchain
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to take into account the conceptual principles of blockchain technology. As a result of the study disciplines, students will be able to: · Take into account the conceptual principles of blockchain technology. · Explain the variety of components that were put into the blockchain. · Explain the modern blockchain technology concepts. · To analyses the appetition of blocks in structural form. · Create and use blockchain-primitives. Within the discipline the following aspects will be considered: Decentering. The concept of the basis of the blockchain. Cryptography in block , Algorithm of consensus and mining blocks. Introduction to the block map UVM. Introduction of cryptocurrency in smart contracts. Blockchain primitive.
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Feature Engineering
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline students with a comprehensive understanding of feature engineering, an essential part of machine learning and data science. Students will learn to extract, create, and transform features from raw data to improve model performance. They will be guided through various techniques including encoding categorical data, dealing with missing data, feature scaling, and feature selection methods. The course also aims to develop the students' skills in using programming languages like Python and R for feature engineering. By the end of the course, students will have gained proficiency in understanding, creating, and transforming features to improve machine learning model performance; they will also have developed practical skills in implementing these techniques using popular data science programming languages. Within the discipline the following aspects will be considered: essential concepts and techniques of feature engineering including data preprocessing, handling categorical and numerical data, imputation of missing data, feature scaling and normalization, feature extraction, feature selection methods, and the application of these techniques through programming languages like Python and R.
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Classification and clustering
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline provide a thorough understanding of classification and clustering algorithms used in machine learning. Students will learn to analyze and interpret complex datasets, develop predictive models, and apply these techniques to real-world problems. Through a blend of theoretical instruction and practical exercises, students will master the foundations of these methods, explore their applications in various fields, and learn to use relevant software tools for data analysis. This course covers a broad range of topics including foundational machine learning principles, supervised and unsupervised learning, various classification and clustering algorithms (like k-nearest neighbors, support vector machines, decision trees, k-means, hierarchical clustering), feature selection, dimensionality reduction, model validation, and practical applications using relevant programming languages and libraries. Within the discipline the following aspects will be considered: selecting, and applying appropriate classification and clustering techniques for data analysis, predictive modeling, and problem-solving in various contexts; they will also develop practical skills in using computational tools and software to implement and validate these models.
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Fundamentals of decentralized applications
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to form the ability to use the main components and tools of blockchain technology for the development of decentralized applications. As a result of studying the discipline, the ability of students to form: - Describe the concept of decentralized applications. - Develop smart contracts using Solidity. - Use advanced features of Solidity. - Interface development using ethers.js. - Integration with web frameworks - Create decentralized full-stack web3 applications. Within the discipline the following aspects will be considered: Decentralized applications. Introduction to DApps and smart contracts. Introduction to Solidity. Development of smart contracts using Solidity. Advanced features of Solidity. Introduction to ethers.js. Integration with web frameworks. Introduction to hardhat. Introduction to GraphQL. Creating a decentralized web3 application with a full stack. Renewable smart contract.
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Software Design and development
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to develop the ability to perform all stages of the software development life cycle, write reproducible, reliable, and scalable code for data science projects. Upon completion of the discipline, students will be able to: Describe data structures and the concept of object-oriented programming. Perform software development stages. Document and package software code. Conduct testing, error handling, and logging. Design software systems using client-server architecture and distributed systems. Within the discipline the following aspects will be considered: Software process models. Agile development model. Software requirements engineering. System modeling. Software architectural designs. Real-time system design. Component-based engineering. Software testing. Software cost estimation.
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Al-Farabi and Modernity
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Aim of the discipline: to form students' ideas about the scientific and philosophical heritage of the great Turkic thinker Abu Nasr al-Farabi in developing the world and national culture. Learning outcomes: explain the main philosophical contents al-Farabi's heritage and his influence on the formation of Turkic philosophy; influence European Renaissance.
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Data analysis in applications
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to apply knowledge, theoretical and practical aspects in the field of data analysis, as well as improve their skills in related fields such as mathematics, project management and entrepreneurship. Within the discipline the following aspects will be considered: Examples of data analysis applications, common tasks and methods. Methods for solving the problem of classification and regression. Clustering. Conversion of traits. Introduction to Text Mining, Pandas Libraries, Numpy, Matplotlib, Plotly.
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Business Intelligence Tools
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to use software tools for data analysis technologies in solving problems of intellectual support for managerial decisions. Business intelligence functions: identification, modeling, forecasting, decision optimization, sensitivity analysis. Business intelligence techniques. Business intelligence platforms. Analytical applications in corporate information systems.
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Business Process Management
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline: to form the ability to manage a company and manage production, marketing, innovation, personnel and financial activities of an enterprise based on the methodology of the management of business process. As a result of studying the discipline, form the following abilities: - to apply the conceptual and categorical apparatus in the field of business process management; - to form an idea of the process approach to management and its difference from the traditional functional approach; - master modern methods of diagnosing parameters of business process models and software tools - to model and analyze business processes. Within the discipline the following aspects will be considered: Introduction to Business Process Management. The evolution of the concept of a business process approach to management, on the methodology and principles of the business process of management, on methods of analysis and reengineering of business processes, will acquire skills and abilities to assess the merits and demerits of various types of management and the consequences of their application, modeling business processes and using information technologies to optimize business processes.
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Scientific Research Methods
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Objective - to develop skills in cognitive activity in the field of science. To use methods of scientific research for understanding and assimilating information. To be able to describe the object of research. To master methods of search, processing of scientific information, systematization, analysis, synthesis to obtain an objective content of scientific knowledge. To apply analytical and practical research methods and argumentation systems for justification, assertion, evaluation.
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Models and Methods of Practical Predictive Analytics
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability of students to use the methods of predictive analytics to predict the future behavior of objects, and entities in the business area. Big Data and traditional sampling. The classifier of predictive analytics tools by the type of problem being solved.
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Big Data Modeling
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability of professional competence in the development and use of systems for processing and analyzing large amounts of data. Introduction to Big Data Modeling. Review the meaning and competence of data. Process management. Analysis of large arrays. Use of processing systems.
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Monitoring of Banking Processes
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to understand the theoretical foundations of business process modeling, familiarize oneself with methods of business process analysis, and gain knowledge in the field of business process management in banking processes. Upon completion of the discipline, the following abilities will be developed: Identify business processes and determine their boundaries and process owners. Create a high-level and detailed map of the organization's business processes. Develop business process documentation. Model and provide detailed descriptions of business processes. Within the discipline the following aspects will be considered: Business Process Management (BPM) is a systematic approach that encompasses reflection, design, execution, programming, documentation, measurement, monitoring, and control of both automated and non-automated processes to achieve the goals and business strategies of a company. BPM involves a conscious, comprehensive, and increasingly technology-driven approach to defining, improving, innovating, and sustaining end-to-end processes. Through this systematic and deliberate process management, companies achieve better results faster and more flexibly.
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Cloud Computing
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to develop the ability to apply concepts, technologies, architectures, applications of cloud computing to research and solve modern fundamental problems. The main trends in the development of cloud computing, technologies. Cloud architecture. Methods, features of designing «cloud» services. Basic cloud computing service delivery models.
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Cloud Technologies in Data Science
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to conduct exploratory data analysis using the Google Cloud Platform (GCP). Within the discipline the following aspects will be considered: Configuring Cloud DataLlb for Exploratory Data Analytics. Segmentation and profiling. Reading and writing data from BigQuery. Cloud storage segment management. Build BigQuery data visualizations using the GCP Charting API. What's in the data pipeline? GCP Data Pipeline Products. Data Science Modules Covered. GCP data pipeline parameters. Cloud Dataproc. Cloud data stream. Cloud Pub / Sub. What is Apache Beam? Pcollections. Conveyor input / output. Configuring GCP for Data Stream. Setting up Python. Creating a simple pipeline. Setting up data flow. Execution in the data stream. Data processing with beam and data stream. Streaming with Dataflow.
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Cloud Data Warehouses
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to study modern methodologies and technologies for creating cloud software tools; mathematical foundations of real-time systems, principles of organizing modern cloud services and systems that can be applied in the development, research of new software. Introduction to service-oriented technologies. Clouds concept. Cloud services concept.
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Natural Language Processing (NLP)
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to use libraries that are relevant and useful for NLP in Python. As a result of studying the discipline, form the following abilities: manipulate text, text search, word count, text splitting into words, lexical variance Extract information from text Analyze sentence structure (parser, grammar) Manage linguistic data. Within the discipline the following aspects will be considered: Overview of Python packages related to NLP. Introduction to NLP .Simple Text Manipulation. Processing complex structures. Natural . Machine translations (statistical, rule based, literal, etc...) NLP in Python in examples. Accessing Text Corpora and Lexical Resources. (Common sources for corpora.ъConditional Frequency Distributions.Counting Words by Genre.Creating own corpus. Pronouncing Dictionary.Shoebox and Toolbox Lexicons. Senses and Synonyms. Hierarchies. Lexical Relations: Meronyms, Holonyms.Semantic Similarity) Categorizing and Tagging Words. Text Extracting Information from Text. Analyzing Sentence Structure. Building Feature Based Grammars. Analyzing the Meaning of Sentences. Managing Linguistic Data.Data Formats (Lexicon vs Text) Metadata.
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Processing of Internet data
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to process Internet data using Google Analytics and Yandex Metrics. The methodological basis for mastering the discipline is to improve the skills of analytical thinking and the use of Internet data. Within the discipline the following aspects will be considered: An introduction to Internet data processing. Review of Internet data processing methods. Process management. Mutual exclusions and synchronization. Information processing management. I / O control. Purposes of processing internet data. Security management. Principles of Internet data processing. Internet data protection. Case studies: Google Analitics, Yandex Metrics.
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Parallel Computing for Data Science
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to form the ability to run several instructions simultaneously with the use of parallel processing. Introduction to Parallel Processing in R. Two Representative Hardware Platforms: Multicore Machines and Clusters. Principles of Parallel Loop Scheduling. All Possible Regressions, Improved Version. The Message Passing Paradigm.
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Search Engines
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to provide an open source real-time full-text search and analysis engine. Elastic Stack functionality. Оn the effective construction of data pipelines that allow you to load terabytes and petabytes of information for search and logging into Elasticsearch and Logstash.
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Legal Bases of Corruption Control
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to form a responsible attitude and the ability to demonstrate in practice the application of the principles and norms of anti-corruption legislation in order to prevent corruption offenses, to form intolerance towards corruption, an anti-corruption culture in everyday life and at the workplace, civil liability. The following will be studied: anti-corruption legislation, the system and activities of anti-corruption subjects, causes and conditions conducive to corruption, anti-corruption policy, international experience in combating corruption.
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Entrepreneurship
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: Purpose: formation of practical skills for carrying out entrepreneurial activities. Student able to: use market opportunities that correspond to their interests and abilities; make an initial decision about business; work effectively within the framework of legal norms; evaluate the potential market opportunities of a startup.
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Applied Data Science
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to know the fundamentals in the foundational elements of data science theory and systems, including data transformation, database systems and practical data processing. Basic concepts of data collection and systematization technologies. Data preprocessing, visualization, primary statistical analysis. Correlation, regression analyzes.
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Python Project for Data Engineering
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to present Python concepts and packages that are useful for solving applied problems of data preparation, applying machine learning methods, and building neural networks. NumPy, SymPy, Pandas packages. Data visualization: Matplotlib, seaborn, plot.ly. Git / GitHub. Recommendations for coding style. Relational databases. SQL queries.
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Design and Construction of Software
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to apply, the basic methods and tools for designing Internet banking / mobile banking using an object-oriented approach and its implementation with various DBMS. Object-oriented analysis of Internet banking / mobile banking. The main elements of OOD: abstraction, encapsulation, modularity...
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Recommendation Systems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of this discipline is to develop the ability to create recommendation systems using the Python programming language. Upon completion of the discipline, students will be able to: Understand the principles of machine learning Create and apply functions in Python Work with recommendation systems Transfer projects to Hadoop. Within the discipline the following aspects will be considered: Python, Quick Start: data types, functions, loops, classes, errors. Data analysis libraries: Pandas, Numpy, Matplotlib, Plotly. Introduction to machine learning. Steps in building an ML system. Key machine learning models: linear and logistic regression. Decision trees and k-nearest neighbors (KNN). Clustering algorithms and quality evaluation. Feature engineering, feature selection.
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Stat Computing & Data Analysis
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to organize and conduct statistical observation, statistical methods of processing and analysis of statistical data. Introduction to statistics. Subject, method and tasks of statistics. Stages of implementation and program-methodological issues of statistical observation. Forms, types, methods of organizing statistical observation.
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Distributed data streaming technology
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to ensure the collection and processing of data, user behavior on the site, information flows from many IoT and IioT end devices, aggregation of application operation logs, aggregation of statistics from distributed applications for corporate data marts (ETL storage), event logging. The discipline covers the following aspects: Basic Kafka concepts and basic operations. Kafka architecture. Basic terms. Exploring the main components of Kafka, how they interact and the killer features of this Kafka workflow technology. Work on the Pub / Sub and Queue / Consumer Group model. Study of 2 possible modes of kafka operation, as well as their differences and the main reasons for choosing one or the other. Topic concept in Kafka. Management of Topics from the console. Learning to split messages into groups and manage them both from code and from the console. Kafka Producer. Posting messages using code. Let's dive into the detailed configuration of Kafka Producer and best practices on the Kafka Consumer message producer side. Receiving point-to-point messages. Learn to receive messages and complete in practice the minimal scenario of the application. Kafka Broadcasting and Groups. Flexible process of receiving messages. Let's learn how to set up groups to work with messages in broadcasting mode.
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Blockchain Technology
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to substantiate, design and apply blockchain technology in practical work. Software architecture and its relationship to blockchain technology. Different ways to define blockchain technology. Blockchain design. Basic concepts of blockchain-based ownership management. Documenting ownership. Hashing data. Stored data protection.
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Database Management
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to develop the ability to build relational database systems, the principles of designing database structures based on (Oracle, PL SQL), methods of reducing database structures to normal forms, learning the basics of the SQL language and performing basic operations on working with data.
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Abais Teaching
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to form in future specialists the ability to self-knowledge, the use of Abai's doctrine as the basis of spirituality and intellectuality of modern Kazakhstan, the application of their professional knowledge, understanding and abilities through the prism of humanism and education in order to strengthen the unity of the country and civil solidarity of society.The following will be studied: the concept of the teachings of Abai; sources of teaching; components of Abai's doctrine; categories of Abai's doctrine; measuring instruments of the teachings of Abai; the essence and meaning of Abai’s doctrine.
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Ecology and Human Life Safety
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The goal of the discipline is to form a number of key competencies based on modern environmental management concepts that implement the principles of harmonious optimization of the conditions for human interaction with nature, including in the process of tourist and recreational activities.The following will be studied: the principles of sustainable development, conservation and reproduction of natural resources to ensure the safety of human life, methods for assessing and minimizing risks, protecting against dangers, including during travel, measures to eliminate the consequences of accidents, anthropogenic disasters, natural disasters, environmental protection and rational environmental management.
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Big Data Ecosystems
- Type of control: [RK1+MT+RK2+Exam] (100)
- Description: The purpose of the discipline is to use information and analytical services, support for solutions, as well as the creation of innovative products and services to increase the efficiency and competitiveness of organizations in any sector of the economy with high speed of collection, processing and analysis of useful information.
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Production
- Type of control: Защита практики
- Description: Formation of a scientific, research approach in the future political scientist's activity, practical application of the methodological approach in research activities, possession of skills to participate in the research process.
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Pre-Diploma
- Type of control: Защита практики
- Description: TThe objective is form the ability to consolidate the theoretical knowledge, analytical and managerial skills, collecting material for the diploma work. The following will be studied: analysis of enterprises' performance effectiveness, advantages and disadvantages of enterprises, list of partners, agreements, contracts, arrangement of meetings, negotiations, recruitment of personnel, team work.
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Professional (educational) practice
- Type of control: Защита практики
- Description: The purpose of the practice is to form the ability to apply in the field in practice their knowledge and skills formed during the development of the discipline "Geodesy", as close as possible to the production conditions of land management. As a result of the internship the student will be able to: 1. use surveying tools; 2. make verification of theodolites, levels, total stations and their installation in the working position; 3. lay theodolite course; 4. make leveling; 5. perform total station survey; 6. to produce laboratory processing of field measurements; 7. make a plan, longitudinal and transverse profiles and other necessary drawings. The practice of geodesy refers to a cycle of training practices. During the practice, the following types of geodetic works will be done: verification of theodolites, levels, total stations and their installation in the working position: centering, horizonting; measurements by theodolite, level, total station; laying of the traverse along the boundary of land use and checking the admissibility of measurement errors; in excess of measurement errors, repeated measurements are made; leveling and checking the admissibility of measurement errors; surveying and manufacturing plan.
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Professional (production) practice
- Type of control: Защита практики
- Description: The purpose of the practice is to form professional knowledge in the field of the chosen specialty, to consolidate the theoretical knowledge obtained in the disciplines of the directions and special disciplines of the program, to master the necessary professional competencies in the chosen direction of specialized training. The practice is designed to create conditions for the formation of practical competencies.