Data Engineering (MSC)
Educational program: «Data Engineering»
Type of master's degree: pedagogical direction
Duration of studying: 2 years of training
Duration of studying: 2 years of training
- The Scientific Educational Department "Digital Engineering and Data Analysis" offers a unique educational program for students who wish to enroll in a master's degree in Data Engineering.
What is Data Engineering?
Data Engineering - this is a kind of hybrid of a data analyst and a data scientist. A data engineer is usually responsible for providing a reliable infrastructure for data, managing workflows, processing pipelines and ETL processes. Due to the importance of these functions, Data Engineering has become a popular profession that is actively growing and developing.
The high salary and huge demand are just a small part of what makes this job extremely attractive!
Data engineering is concerned with data, namely its delivery, storage, and processing. If we look at the hierarchy of needs, data engineering takes the first 2-3 stages: collecting, moving and storing, preparing data.
The high salary and huge demand are just a small part of what makes this job extremely attractive!
Data engineering is concerned with data, namely its delivery, storage, and processing. If we look at the hierarchy of needs, data engineering takes the first 2-3 stages: collecting, moving and storing, preparing data.
What does a data engineer do?
With the advent of big data, the scope of responsibility has changed dramatically. If earlier these experts developed large SQL queries and distilled data using tools such as Informatica ETL, Pentaho ETL, Talend, now the demands placed on data engineers have grown.
Thus, the role of a data engineer is quite significant.
Thus, the role of a data engineer is quite significant.
Requirements for the position of Data Engineer:
· Experience with big data: Hadoop, Spark, Kafka.
· Knowledge of algorithms and data structures.
· Excellent knowledge of SQL and Python, Java/Scala.
· Experience working with cloud platforms, in particular Amazon Web Services.
· Good understanding of SQL and NoSQL databases (data modelling, data storage).
· Understanding the basics of distributed systems.
· Experience with data visualization tools such as Tableau or ElasticSearch.
Data engineers are specialists in the development of software and backend. If a company begins to generate a large amount of data from different sources, the task of a data engineer is to organize the collection of information, its processing and storage.
A data product is the result of transforming data into high-quality information that benefits the business.
Is a data engineer more in demand than a data scientist?
Without them the value of the prototype model, most often consisting of a piece of terrible quality code in Python file, received from a data scientist tends to zero.
Without a data engineer, a code will never become a project, and no business problem will be effectively solved.
· Knowledge of algorithms and data structures.
· Excellent knowledge of SQL and Python, Java/Scala.
· Experience working with cloud platforms, in particular Amazon Web Services.
· Good understanding of SQL and NoSQL databases (data modelling, data storage).
· Understanding the basics of distributed systems.
· Experience with data visualization tools such as Tableau or ElasticSearch.
Data engineers are specialists in the development of software and backend. If a company begins to generate a large amount of data from different sources, the task of a data engineer is to organize the collection of information, its processing and storage.
A data product is the result of transforming data into high-quality information that benefits the business.
Is a data engineer more in demand than a data scientist?
Without them the value of the prototype model, most often consisting of a piece of terrible quality code in Python file, received from a data scientist tends to zero.
Without a data engineer, a code will never become a project, and no business problem will be effectively solved.
Studied disciplines
· Python Programming Language (Intro to Data Science)
· Advanced Statistics
· Applied Machine Learning
· Big Data Analysis
· Data Wrangling and Design
· Deep Learning and Artificial Intelligence
· Applied Computer Vision
· Hadoop distributed systems
· Scalable solutions
· DevOps Introduction
· Modernizing DWH
· Development of High Load Applications
· Fraud and Anomaly Detection
· Data Driven Management
· Data Lake Construction
· Advanced Deep learning
· Advanced Statistics
· Applied Machine Learning
· Big Data Analysis
· Data Wrangling and Design
· Deep Learning and Artificial Intelligence
· Applied Computer Vision
· Hadoop distributed systems
· Scalable solutions
· DevOps Introduction
· Modernizing DWH
· Development of High Load Applications
· Fraud and Anomaly Detection
· Data Driven Management
· Data Lake Construction
· Advanced Deep learning
Career opportunity for data engineer:
Career opportunity for data engineer:
Masters who graduate from the program "Data Engineering" can be employed in banks, investment, insurance, telecommunications, trade, manufacturing companies; organizations of various forms of ownership; industry and business that develop and use information systems, intelligent products and services based on technologies related to the processing of big data and blockchain technology.
• Data Engineer (collects and processes the data, runs the processes, and builds services to this data is turned into a data product)
• Data Platform Engineer (preparing platforms: infrastructure, engineering, security and monitoring)
• Data Quality Engineer (combines engineering problems, data analysis, and elements of the test; wider than traditional testing QA)
· Data DevOps Engineer (works with distributed systems, simultaneously processes complex data, bearing in mind a lot of connections between system components, analyzes and fixes problems)
· Data Science Engineer (structures and analyzes large volumes of data, predicts events)
· Search Engineer (defined Search Engineer as a data expert, since modern search engines have become very smart, so today it is much closer to Data Science and working with data)
· ML Engineer (Machine Learning is a special case of Data Platform Engineering, it is necessary not only to work with data, but also to ensure transparency and manageability of the ML product lifecycle; due to the complexity of this process, the ML engineer must have a deeper expertise)
Masters who graduate from the program "Data Engineering" can be employed in banks, investment, insurance, telecommunications, trade, manufacturing companies; organizations of various forms of ownership; industry and business that develop and use information systems, intelligent products and services based on technologies related to the processing of big data and blockchain technology.
• Data Engineer (collects and processes the data, runs the processes, and builds services to this data is turned into a data product)
• Data Platform Engineer (preparing platforms: infrastructure, engineering, security and monitoring)
• Data Quality Engineer (combines engineering problems, data analysis, and elements of the test; wider than traditional testing QA)
· Data DevOps Engineer (works with distributed systems, simultaneously processes complex data, bearing in mind a lot of connections between system components, analyzes and fixes problems)
· Data Science Engineer (structures and analyzes large volumes of data, predicts events)
· Search Engineer (defined Search Engineer as a data expert, since modern search engines have become very smart, so today it is much closer to Data Science and working with data)
· ML Engineer (Machine Learning is a special case of Data Platform Engineering, it is necessary not only to work with data, but also to ensure transparency and manageability of the ML product lifecycle; due to the complexity of this process, the ML engineer must have a deeper expertise)