ҚазРусEng
ҚазРусEng

Applied Data Analytics

7M06103 Applied data Analytics

Profile subjects: algorithms and data structures, as well as databases.

The graduate program “Applied Data Analytics” is a comprehensive and specialized educational program that equips students with the necessary skills and knowledge to analyze and interpret complex datasets. By combining statistics, mathematics, computer science, and business, students learn various data analysis techniques and gain proficiency in programming languages and tools commonly used in the field. The program emphasizes ethical considerations and practical applications, preparing graduates to tackle data-related challenges across industries. Through hands-on experience, students apply their knowledge to real-world scenarios, fostering a deep understanding of data analytics in different domains. Ultimately, graduates are well-prepared for roles such as data analysts, scientists, and consultants, making valuable contributions to organizations in an increasingly data-driven world.

Contacts

Admission Committee

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

Mon-Fri 9:00 – 18:00

Objective of Educational Program

Provide training of highly qualified scientific and applied specialists and software engineers in the field of large-volume data analysis, as well as managers and managers of software and information systems for the information technology industry and interdisciplinary industries related to data processing in various sectors of the economy of the Republic of Kazakhstan.

List of a specialist’s positions

Сareer opportunities
  • Data Analyst
  • Data Scientist
  • Big Data Analyst
  • Big Data Engineer
  • Software Developer
  • Head of the organization
  • Deputy head of the structural unit
  • Head of the structural unit
  • Expert of the republican center
  • Employee of the national scientific and practical center, university.

M094 – Informational technologies

Group of educational programs

Master of Technical Science in Educational Program "7M06103 Applied data Analytics"

Awarded degree

2 years

Duration of study

Learning outcomes

  • Formulate tasks arising in the course of research activities, apply methodological, psychological approaches in research
  • Possess critical thinking and academic language at a professional level that allows conducting scientific research and teaching special disciplines in universities
  • Apply data analysis methods to solve various applied data analysis and analytical processing tasks
  • Manage the team and lead the software development and data analysis process
  • Apply methods of designing and developing software systems to solve a wide class of applied problems in the field of analysis in various fields.
  • Formulate, modify and develop machine learning and artificial intelligence methods
  • Understand the fundamental principles and possess the mathematical knowledge underlying the methods of data analytics and machine learning
  • Have knowledge in related fields of project management, computer science, information security

Documents

Module Handbook

"Applied Data Analytics"

Development Plan

"Applied Data Analytics"

Academic disciplines

Cycle of fundamental disciplines

University’s component

History and Philosophy of science

This discipline involves the study of the main directions, problems, theories and methods used in the history and philosophy of science, as well as the content of modern philosophical discussions on problems of social development.

Higher Educational Pedagogy

This discipline involves the study of the main categories of pedagogy, methods of pedagogical reality,
the categorical structure of the science of pedagogy, etc.

Foreign language (professional)

This discipline involves the study of the functional features of oral and written professionally-oriented texts, including scientific and technical nature, requirements for documentation (within the program), accepted in professional communication and in the countries of Europe and the language being studied.

Psychology of Management

This discipline involves familiarizing students with the basic resource capabilities of the human factor in the management of organizations in modern conditions. Within the framework of the discipline, the psychological characteristics of management objects, both personnel and the organization as a whole, and management subjects, which are managers of different levels, are also considered in order to reveal the psychological mechanisms that ensure the effectiveness of management systems.

Teaching Practicum

Pedagogical practice is a type of practical activity of undergraduates, including the teaching of special disciplines, the organization of educational activities of students, scientific and methodological work on the subject, obtaining skills and abilities in the work of a teacher.

Cycle of fundamental disciplines

Elective component

Methods and tools for data analysis

This discipline involves the study of data processing methods and technologies that include structured and unstructured data of huge volumes and significant diversity. While studying the discipline, you will also consider horizontally scalable software tools that are alternatives to traditional databases.

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.

Data processing and understanding

These disciplines involve learning the basics of understanding how to work with data, and extracting the required information from the data. The discipline “making decisions based on data” involves the study of the business component, i.e. how to apply data analysis to make the right management decisions.

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.

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.

Business analytics

These disciplines involve the study of basic quantitative and qualitative methods in the field of data Analytics applied to various industries, including business Analytics, digital Finance, and the digital business ecosystem.

Cycle of major disciplines

University’s component

Programming for data analysis and databases

This discipline involves the study of the most current programming technologies, such as the Python programming language and the R statistical data processing language. The course will also cover the basics of managing relational and non-relational databases.

Teaching methods and strategies

This course explores innovative pedagogical approaches and effective techniques for engaging and facilitating learning in diverse educational settings.

Mathematics for data science

These disciplines involve the study of the basics of statistics, linear algebra, mathematical analysis and discrete mathematics required to form the mathematical basis of data Analytics.

Machine learning and artificial intelligence

This discipline involves the study of the basics of machine learning and artificial intelligence, and the application of this knowledge to solve real-world applications. The discipline covers many topics of teaching with and without a teacher. A third type of machine learning task, called attachment learning, is partially covered.

Research Practice

Research practice

Case study on data Analytics

A case study on data Analytics is designed to give students the opportunity to apply first-year competence to a real-world project, preferably with professional training. A case study is basically an analytical and descriptive task involving the selection and analysis of a suitable business process or production process in the workplace. This process is described, modeled, and improvement goals are defined. Students decide where and what data to collect in the process chain. They also generate the corresponding data set.

Cycle of major disciplines

Elective component

Big Data Methods and Tools

The purpose of the discipline “Big Data Methods and Tools” is to provide master students with an in-depth understanding of big data concepts, technologies, and methodologies, enabling them to process, analyze, and derive insights from large-scale datasets in various domains

Making decisions based on data

This discipline involves learning the basics of understanding how to work with data, and extracting the required information from the data. The discipline “making decisions based on data” involves the study of the business component, i.e. how to apply data analysis to make the right management decisions.

Generative algorithms

This course introduces students to various generative modeling techniques and algorithms, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models. Students will learn how to design, train, and evaluate generative models, as well as explore their applications in fields like computer vision, natural language processing, and data synthesis

Service model in project management

The course covers the area of the service approach in organizing the company’s activities; service tools and services provided by internal divisions and/or external contractors

Business process analysis

This discipline involves learning the basics of business process analysis and design using the most popular design methodologies, such as BPMN 2.0, EPC, and others.

Project Stakeholder

The course is designed to study the management of stakeholders (stakeholders) of the project. Undergraduates will consider the basic principles and analysis of the external and internal environment of the project, aimed at identifying and systematizing the main stakeholders, assessing their goals, collecting information about them and using this data in the project management process. It will also consider negotiating and engaging stakeholders to collaborate with managing the expectations of key stakeholders.

IT Project Management

Discipline that focuses on the strategic planning, development, and successful execution of products throughout their lifecycle. It includes a wide range of activities and responsibilities aimed at creating valuable and marketable products that meet customer needs and meet business goals.

Heterogeneous Parallelization

This course is a concept, languages, methods and patterns for programming heterogeneous, massive parallel processors. It covers heterogeneous computing architectures, software programming models, memory launching methods and parallel algorithms on the example of CUDA and OpenCl.

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.

Research work of a master's student, including an internship and the completion of a master's thesis