MICROQUALIFICATION EDUCATIONAL PROGRAM «ML SPECIALIST» (integrated)

EDUCATIONAL PROGRAM PASSPORT

Field of education: 6B06 Information and communication technologies
Field of study: 6B061 Information and communication technologies
Group of educational programs: B057 Information technology
License number for the direction of training: KZ67LAA00019559
Volume of credits: 16
Registration number: MCCE-2022/0011
Registration date: 05/04/2023
Languages of instruction: Russian, English
Partner organization: “Corporate University” – a branch of Kazakhtelecom JSC

BRIEF DESCRIPTION OF THE MICROQUALIFICATION EDUCATIONAL PROGRAM

Main direction of the EP:
The program is aimed at training specialists in the field of machine learning and big data analysis, obtaining the competencies necessary to perform a new type of professional activity – the ability to use the SQL and Python programming languages for collecting, visualizing, analyzing big data (Big data) and building machine learning models , ability to apply specific analytical and product approaches.

The purpose of the microqualification EP:
Developing skills in students to analyze machine learning problems and make a balanced choice of one or another solution for working with big data.

Objectives of the EP microqualification:
– education of information, technical and research culture;
– development of interest in scientific and technical creativity, technology, high technologies;
– development of algorithmic and logical thinking;
– to form theoretical knowledge on the basics of machine learning for constructing formal mathematical models and interpreting modeling results;
– develop skills in the practical application of machine learning methods in solving applied problems in various fields;
– develop skills in using Python libraries to develop machine learning systems.

FORMED LEARNING OUTCOMES

LO 1. select the data correctly and translate it into a machine-readable format;
LO 2. design and train neural networks and other machine learning models;
LO 3. evaluate how well neural networks cope with their tasks;
LO 4. use the PyTorch framework, Tensorflow library and Keras API;
LO 5. use existing knowledge to solve practical deep learning problems;
LO 6. navigate the concepts of the world of Big Data, Machine Learning and the Internet of Things;
LO 7. know the differences between different versions of Hadoop, Spark, NoSQL or Kafka distributions;
LO 8. identify the nuances of cloud solutions;
LO 9. know what the GDPR standard is;
LO 10. determine the features of the Industrial Internet of Things.

INFORMATION ABOUT DISCIPLINES

№	Name of the discipline	Brief description of the discipline	Credits	Acad.hours	Formed competencies (codes)
1.	Machine Learning	The goal of the discipline is to develop students’ theoretical knowledge and practical skills in the basics of machine learning, and students to master the tools, models and methods of machine learning.	5	150	ПК1, ПК2, ПК3, ПК5, ПК6
2.	Deep learning	The goal of mastering the discipline is to master the algorithms and methods of deep learning; developing skills and abilities in solving practical problems using deep learning methods.	5	150	ПК1, ПК3, ПК5
3.	Big data	Formation of professional competence in students in the field of development and use of systems for processing and analyzing large data sets.	5	150	ПК1. ПК3, ПК4
4.	Final examination	The purpose of the student’s final certification is to evaluate the learning outcomes and professional competencies achieved upon completion of the study of the microqualification educational program	1	30