Big Data Analysis

Big Data Analysis

6B06103

The educational program “Big Data Analysis” (big data Analysis) will allow you to develop skills in the field of:
– analysis of large amounts of information;
– data management of the organization, industry;
– introduction of new technologies for data processing and analysis;
– development of new models of the organization’s information infrastructure, taking into account the capabilities of big data technologies.

Profile subjects: informatics + mathematics

Career opportunities

– Analyst in large scientific, research and

consulting projects;

– Machine learning specialist;

– Data analysis specialist;

– Analyst in large areas of industry, retail,

logistics companies business analyst;

– Analyst in banking, telecommunications,

and the public sector.

The goal of the EP

The goal of “Big Data Analysis” educational program is to train students in the theoretical and practical aspects   of data analysis, as well as to improve their skills in related industries such as mathematics, project management and entrepreneurship. Junior(junior)/ mid-middle (middle) data analysts in many sectors of the economy including communications, finance, healthcare, manufacturing, management and so on.

Contacts

Admission Committee

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

Mon-Fri 9:00 – 18:00

Objectives of the EP

  • Providing highly qualified big data analysts to private and public companies.
  • Providing students with a wide range of expertise in big data analysis based on the educational program needed to start working as a Junior Data Analyst in a variety of companies, including small businesses up to 10 people, to large national and private organizations with more than 1,000 employees.
  • The development of the flexible (soft) qualities required in students to develop the leadership and patriotic parties needed to shape them as successful and committed leaders of their industry.

6B06 - Information and Communication Technologies

Education code and classification

057 - Information Technology

Education Programs Group

061 - Information and Communication Technologies

Code and classification of training directions

Requirements for evaluating the learning outcomes of the educational program

A student, after mastering the entire educational program, must be able to fulfill the following points:
  • Formulate and solve problems arising in the course of production activities that require in-depth professional knowledge.
  • To formulate the problem, both mathematical approaches and computer tools can be used;
  • Choose the necessary approaches and methods for analyzing problems, as well as modify existing ones and develop new ones, depending on the tasks of a particular case;
  • To apply in the process of training psychological methods and means of increasing the effectiveness and quality of training;
  • Proficient in a foreign (English) language, allowing students to conduct research at a high-quality level and to teach special disciplines in universities;
  • To model and design complex systems using mathematical and computer models and methods;
  • Apply quantitative and qualitative methods and techniques for collecting primary information for research, as well as developing effective solutions to problems;
  • Analyze and design software tools for data analysis, as well as the algorithms, models and methods required for developing software systems, effective data analysis and extracting knowledge from data;
  • Manage a team of developers in the process of developing software systems, as well as models and methods of data analysis;
  • Choose standards, methods, technologies, tools and technical means for carrying out work on further maintenance of software systems;
  • Apply methods of design and development of software systems to solve a wide class of applied problems in various fields, including interdisciplinary industries;
  • Program and test various solutions (models, methods), take part in the creation and management of systems at all stages of the life cycle of system development.
  • Create relational and non-relational databases for the effective 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.
  • To analyze the complexity of calculations and the possibility of parallelization (optimization) of the developed algorithms and programs.
  • Assess 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

The list of competencies of the educational program

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 understand modern standards, regulatory framework, the basics of economic knowledge, scientific ideas about project management and technological entrepreneurship.
PC2. The ability to professionally use modern computer equipment, network components, computer programs and complex computing systems (in accordance with the objectives of the program), as well as use the safety rules, industrial sanitation, fire safety and labor protection standards.
PC3. The ability to possess the skills of using and applying algorithms, data structures and modern methods for creating (developing) and further supporting various software systems for analyzing big data.
PC4. The ability to use the basic principles and methods for solving managerial problems, the ability to execute project documentation in a software environment using computer graphics for various types of projects.
PC5. The ability to be competent in the choice of mathematical modeling methods for solving specific applied problems in big data analysis, including the willingness 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.
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.
PC7. Ability to develop information and information system software based on modern development methods and tools.
PC8. Ability to collect, process and analyze data using the organization’s methodological and technological infrastructure.
PC9. The ability to manage the life cycle stages of the methodological and technological infrastructure of software development, data analysis, design of IT infrastructure in various organizations.
PC10. The ability to use modern programming environments for the design and implementation of software solutions and databases for information and communication technologies.
PС11. The ability to apply the elements of probability theory and mathematical statistics that underlie the models and methods of data science, to choose the right methods of data analysis, machine learning and artificial intelligence to solve practical problems.
PС12. The ability to develop and implement safe and testable solutions based on new methods and technologies for information security, used when working with information and communication technologies.

Learning Outcomes

LO1. Explain and understand the regulatory framework, including documents, standardization and certification procedures in the development of information and communication technologies.
LO2. Apply domestic and foreign standards for software development in organizations.
LO3. Apply practical programming skills and explain the general methodological foundations of program development, create system programs for various levels of computer systems and software architecture, including low-level programming and microcontroller programming.
LO4. Demonstrate knowledge of the architecture of computer systems, manage operating systems.
LO5. Implement basic network communication between devices, calculate and apply addressing schemes, configure and configure network devices required to ensure the functionality of information and communication technologies.
LO6. Apply project management tools at various stages of the project life cycle, make a qualitative and quantitative assessment of project risks, determine the effectiveness of the project.
LO7. Apply mathematical tools to analyze software systems and data based on statistical and probabilistic models.
LO8. Apply hardware and software services to ensure the continuity of the process of developing software systems.
LO9. Independently analyze modern sources, draw conclusions, argue them and make decisions based on information.
LO10. Develop secure server-side web client applications and mobile applications.

Assessment of learning outcomes

Exam form Recommended ratio, %
1 Computer testing 20
2 Written 10
3 Oral 5
4 Project 30
5 Practical 30
6 Comprehensive 5

Module Handbook

«Big Data Analysis»

DEVELOPMENT PLAN

Educational Program 6B06103 «Big Data Analysis»

DEVELOPMENT PLAN

Educational Programm 6B06103 «Big Data Analysis»

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