2019-Mar
Start date
26
Duration (weeks)
2-3 times a week in the evenings
Frequency
15
Number of students
Gomel, Belarus
City
EPAM office in 80 Rechitsky av
Location
Download training program
Basic description

Data Engineering is working with data and Big Data: programming of data collecting, storing, processing, searching and visualization.

Required skills

We are waiting for

·         Specialists who are ready to change their profession and start career in IT;

·         Graduates of technical universities without restrictions on distribution after the university;

·         Students of upper grades (technical specialties).

Experience in banking, engineering or any other technical sphere will be a benefit.

Requirements for candidates

·         Basic knowledge of relational Database management system (DBMS) theory;

·         Basic knowledge of SQL (we recommend learning exercises on SQL here);

·         Basic knowledge of network and virtualization technologies (you can get acquainted  VirtualBox);

·         English proficiency level - Pre-Intermediate - Intermediate (A2-B1).
Willingness to level up English skills in a short time;

·         Ability to work in a team, good communication skills, readiness for self-development and learning a big amount of material in a short time.

Training details

We will give you an opportunity to acquire / expand your knowledge of working with data:

·         analysis, forecasting and data integration;

·         building data warehouses in tens of terabytes;

·         data visualization to solve business problems;

·         quality assurance in data storage and management systems.

If you successfully pass the training, you will become a Data Development / Consulting / Testing / Data Analysis Specialist with the opportunity of further employment in EPAM.

Conditions:

Training: 2-3 hours in the evening 2 times a week on weekdays at Rechetskiy, 80 (4 months)

List of recommended literature:

·         К. J. Date. Introduction to Database Systems. — p. 1328. — ISBN 5-8459-0788-8.

·         Ralph Kimball. The data warehouse ETL toolkit - practical techniques for extracting, cleaning, conforming, and delivering data. Wiley, 2004

P.S. To consolidate skills, use materials for self-preparation "Materials for self-studying (for future Data Engineers)"