If you are interested in creating data products and exploring the power of data to turn raw information into valuable insights for business growth, then this training program is what you need.
By participating, you will have the opportunity to acquire new skills or expand existing knowledge through hands-on experience in three major areas:
- Data Integration – development and support of a wide range of data transformations and migrations
- Data Visualization – creation of interactive and complex data visualizations and analysis tools
- Data Quality – data validation and transformation at every stage of the project
What do we offer?
- Industry-based education. As a leading software engineering company, we will help you explore emerging technologies and best practices that the market demands.
- Top-notch learning materials. Our Data & Analytics specialists with extensive project experience have designed and tested the educational content in numerous training runs.
- Practice-oriented approach. This comprehensive program focuses on providing you with hands-on experience and practical application of the concepts learned.
- Deep dive into the specialization. Our graduates become highly skilled specialists ready to face complex technical challenges and work with the world's leading customers.
- Support from experienced mentors. We will guide you at all training stages, covering your open questions and sharing feedback on assigned tasks.
- Career advancement. Upon successful completion of both Fundamentals and Specialization stages, you will gain market-oriented soft and hard skills to kickstart your career journey as a Data Analytics Engineer and work on real projects in the IT industry.
Training process
The program consists of two stages:
Fundamentals stage: It will last 3 months and require ~12 hours of weekly engagement.You will explore self-study materials, complete one assigned task each week, ensure its approval by mentors and discuss your questions during weekly group sessions led by experts. Additionally, you will participate in 2-3 individual assessments with mentors, which will involve a theoretical review and live coding exercises. If you show good results and successfully pass a technical interview, we will invite you to the next stage.
Specialization stage: It will last 4 months and require ~20 hours of weekly engagement. At this stage, learning will become more intensive, involving daily assignments, daily group Q&A sessions and exploration of self-study materials.
What is required for training?
- English level from B2 (Upper-Intermediate) and higher
- Basic knowledge of Relational Database Management System (DBMS) theory
- Understanding of Structured Query Language (SQL)
- Familiarity with Python basics
Nice to have:
- Degree from a technical university or other educational institution with a technical specialization
What will you learn?
Our program will cover both fundamental and advanced skills in Data Analytics Engineering:
Stage 1: Fundamentals (3 months with ~12 hours/week)
- Database Development Concept (~2 weeks)
- DB Components
- DB Modeling
- Normalization
- SQL Foundation (~6 weeks)
- DML Statements (Data Manipulation Language)
- TCL Statements (Transaction Control Language)
- Theoretical and Practical Assessment
- DDL Statements (Data Definition Language)
- DCL Statements (Data Control Language)
- Theoretical and Practical Assessment
- SQL for Analysis (~3 weeks)
- Introduction to OLAP. OLAP vs OLTP
- Window Functions / Frames
- Theoretical and Practical Assessment
- Python Core (recommended self-study course)
- Technical Interview on Data Development and SQL
Stage 2: Specialization (4 months with ~20 hours/week)
- Data Integration Techniques (~11 weeks)
- Python Theoretical and Practical Assessment
- Introduction to Data Warehousing and ETL + Theoretical Assessment
- PostgreSQL for Data Warehouse and ETL
- DWH/ETL: Live Project Presentation + Theoretical and Practical Assessment
- Cloud Technologies: AWS + Theoretical Assessment
- Databricks (self-study course)
- Data Analytics Reporting Tool (~2 weeks)
- Data Visualization: Power BI + Theoretical Assessment
- Data Quality Concepts (~3 weeks)
- Introduction to Quality Assurance
- Data Warehousing and ETL Testing
- Cloud for DQE
- Big Data for DQE
- Test Automation
- Big Data Concepts (self-study course)
- Spark
- Kafka
- Technical Interview
