online course
Analytics Engineer
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24/7 access - study
when it suits you
Experienced experts - learn
from practicing professionals
Practical assignments -
work with real cases
Self-paced - no
strict deadlines
From anywhere in the world -
no geographical restrictions
Certificate - get
proof of your skills
What does a data
analyst do?
A data analyst turns disparate numbers
into strategic decisions. He or she:
  • Extracts and analyzes data (SQL, Google Sheets)
  • Calculates metrics, builds forecasts and cohort analysis
  • Visualizes insights (Superset, Python)
  • Formulates conclusions that drive the business forward.
15 000+
open vacancies on
the labor market
> $1,100
salary per month immediately
upon completion of the course
85%
of students find a job in the
first month after graduation
Become
an analyst
After the training, you will be able to
choose an area that matches your skills
Data Analyst
You will be able to explore business metrics and create clear reports with visualization in Tableau or Power BI.
BI Analyst
Creates powerful dashboards for executives, automates reporting and turns raw data into strategic insights.
Product Analyst
A specialist who uses data to improve digital products by analyzing user behavior and testing new features.
Marketplace Analyst
Researches the competitive landscape, optimizes product cards, and helps increase site sales.
E-commerce Analyst
Analyze sales, forecast demand, and optimize advertising campaigns to increase profits for online stores.
Marketing Analyst
Evaluates the effectiveness of advertising channels, customizes end-to-end analytics and calculates real return on marketing investments.
Web Analyst
Specialists are in demand for setting up data collection systems, analyzing website visitor behavior and finding conversion growth points.
Analyst in Fintech
Work with transactional data, identify fraudulent schemes and help banks make informed risk decisions.
Demand exceeds
supply - salaries rise
According to Linkedin, there is a shortage of
data analysts in the market in 2025, although
companies are willing to pay - the average
salary of a specialist has reached $1100.
In the course, you will learn
  • Master the full range of technologies from storage design to ETL processes to data visualization.
  • Deepen your understanding of methodologies and choose the best approaches to meet your business objectives.
  • Learn to design and justify data architecture with scalability and performance in mind.
  • Master the Data Build Tool to automate data transformation, model creation, and documentation.
  • Gain hands-on experience designing, deploying, and populating storage.
  • Understand how to build efficient storage schemas that enable fast access and analytics.
  • Learn how to translate business requirements into conceptual, logical, and physical data models.
  • Realize "stars" and "snowflakes" and learn how to work with facts, dimensions, and slowly changing dimensions (SCD).
Course program
Module 1. Introduction
  • Course Overview
  • How to get the most out of this course
  • Resources
Module 2. What is a Database?
  • Database Introduction
  • Database definition
  • SQL Example
  • Database Management System (DBMS)
  • Sheets vs Database
  • OLTP
  • OLTP ACID
  • OLAP
  • OLTP vs OLAP Summary
  • NoSQL Introduction
  • Key Value Store
  • Document Store
  • Wide Columns
  • Graph Database
  • Search Engines
  • SQL vs NoSQL
  • On-Prem vs Cloud
  • Quiz
Module 3. What is a Data Warehouse?
  • Data Warehouse Introduction
  • Data Warehouse Definition
  • Data Warehouse Benefits
  • Data Warehouse Architecture
  • Data Source
  • Data Lake
  • Data Warehouse Layer
  • Business Intelligence Introduction
  • Business intelligence tools
  • ETL - ELT Introduction
  • ETL
  • ELT
  • ETL vs ELT
  • Quiz
Module 4. Data Modelling & ERD Notation
  • Data Modelling & Entity Relationship Diagram (ERD) Introduction
  • Data Modelling Overview
  • ERD Overview
  • Entity Attributes Relationships
  • Steps to Create an ERD
  • Build ERD using Chen's Notation Style
  • Build ERD using Information Engineering Notation Style
  • Data Modelling Concepts
  • Different Type of Keys
  • Recommended Tools for Creating ERD
  • Quiz
Module 5. Normalisation and Denormalisation
  • What is Normalisation?
  • 1st Normal Form
  • 2nd Normal Form
  • 3rd Normal Form
  • Pros & Cons of Normalised Model
  • What is De-Normalisation?
  • De-Normalisation Techniques
  • Pros & Cons of De-Normalised Model
  • Quiz
Module 6. Data Warehouse Design Methodologies
  • Data Warehouse Design Methodologies Introduction
  • Inmon Methodology
  • Corporate Information Factory (CIF) Architecture Explained
  • Inmon Architecture
  • Pros & Cons of Inmon Methodology
  • Kimball Methodology
  • Processes of Kimball Methodology
  • Kimball Architecture
  • Pros & Cons of Kimball Methodology
  • Inmon vs Kimball
  • Hybrid Architecture
  • Data Vault Methodology Introduction
  • Data Vault Components
  • Data Vault Architecture & Example
  • Pros & Cons of Data Vault
  • Inmon vs Kimball vs Data Vault
  • One Big Table (OBT) / Wide Table
  • Pros & Cons of OBT
  • Data Modelling Then, Now & Next
  • Quiz
Module 7. Dimensional Modelling
  • Dimensional Modelling Introduction
  • What is Dimensional Modelling?
  • Data Warehouse LifeCycle Overview
  • Program/Project Planning
  • Requirement Gathering
  • Concept & Steps of Dimensional Modelling
  • Select Business Process & Declare the Grain
  • Dimensions (Types)
  • Conformed Dimensions
  • Junk Dimensions
  • Degenerate Dimensions
  • Role Playing Dimensions
  • Slowly Changing Dimensions (SCD) - Intro
  • Type 0 - SCD (Slowly Changing Dimensions)
  • Type 1 - SCD (Slowly Changing Dimensions)
  • Type 2 - SCD (Slowly Changing Dimensions)
  • Type 3 - SCD (Slowly Changing Dimensions)
  • Type 4 - SCD (Slowly Changing Dimensions)
  • SCD - Store as Snapshots
  • Bridge Tables
  • Facts
  • Additive Facts
  • Semi-Additive Facts
  • Non-Additive Facts
  • Transaction Facts Tables
  • Periodic Facts Tables
  • Accumulative Facts Tables
  • Star Schema
  • Snowflake Schema
  • Quiz
Module 8. Setting up Introduction
  • BigQuery Setup Introduction
  • BigQuery Tables Setup using CSV
  • BigQuery Tables Setup Using SQL Script
  • Setting up WSL2 for Windows
  • Git Repository Setup
  • dbt setup & Installation
Module 9. (Hands-on dbt) Building Dimensional Data Warehouse
  • Introduction
  • Hands-on overview
  • Use-Case Introduction
  • Use-Case Detailed Discussion
  • Requirements Gathering
  • Data Profiling - Introduction
  • Data Profiling - Completed
  • AE Workbook - Walkthrough
  • Bus Matrix - High Level Entities
  • Conceptual Model
  • Architecture Design
  • Dimensional Modelling Introduction
  • Bus Matrix Detailed
  • Source to Target Mapping (Source to BQ Data Lake)
  • Source to Target Mapping (BQ Data Lake to Staging)
  • Dimensional Model (Attributes & Measures)
  • Source to Target Mapping (Data Lake to Data Warehouse)
  • Source to Target Mapping (Data Warehouse to OBT)
  • Logical Model Design
  • Physical Model Design
  • dbt overview
  • Physical Implementation (Staging Layer)
  • Physical Implementation (Staging Layer) Cont.
  • Physical Implementation Dim Tables (Data Warehouse Layer)
  • Physical Implementation Fact Tables (Data Warehouse Layer)
  • Physical Implementation (Analytics OBT)
  • Debugging (dbt)
  • Adding Tests (dbt)
  • Hands-on Complete
Module 10. Accelerate dbt development with Power User for dbt (Date Pilot)
  • Introduction to dbt Power User
  • dbt Power User pre-requisites
  • Installation and Configuation
  • Generate dbt Models from source file or SQL
  • Query Translation
  • Query Explanation
  • dbt Actions tool and Query results preview
  • Update dbt Model
  • Project Governance
  • Write dbt Tests Automatically
  • Impact Analysis with Column Lineage
  • Documentation Generation
  • Collaboration Workflow
  • Defer to prod
Module 11. Conclusion
  • Glossary
You will solve real tasks: and
formalize them in the portfolio
1/8
Analyze business
metrics
Analyze a case study of an online store: calculate conversion to purchase, average check, LTV of customers, identify the reasons of sales decline.

Build a dashboard in Tableau/Power BI with key metrics for the marketing department.
2/8
Set up end-to-end
analytics
On real data, set up a Google Analytics 4 + CRM bundle (e.g. for a SaaS service).

Determine which advertising channels are profitable and which are unprofitable.
3/8
Product analytics
Analyze mobile app user behavior: where customers are lost in the funnel, which buttons don't work.

Conduct an A/B-test of a new feature and statistically evaluate its effectiveness.
4/8
E-commerce and
Marketplaces
Make ABC-XYZ-analysis of the assortment: which products are profitable to promote, and which ones should be removed from sales.

Calculate how competitors' price dynamics affect your sales (data parsing + visualization).
5/8
Working with SQL
and BigQuery
Write complex queries to analyze data: from calculating retention to identifying anomalous transactions.

Create data showcases for real-time reporting.
6/8
Financial analytics
Analyze transactions of bank customers: find suspicious transactions (suspicion of fraud).

Build a company's cash flow forecast based on historical data.
7/8
Dashboards
and automation
Develop an interactive BI-dashboard for the CEO of the company with KPIs by departments.

Set up automatic sending of reports to Telegram/Slack.
8/8
Graduation Project
Optimize marketing budget (with justification of where to redirect money).

Analyzing user retention in a mobile game.

Building a predictive demand model for retail.
Alexey Sevruk
Expert in Data Engineering
Experience: 8+ years working with Big Data, including storage design for large banks and retail.

Specializes in ETL process optimization and DBT implementation in enterprise environments.
Ekaterina Pegova
Senior Product Analyst
Experience: 6 years in product analytics. Deep knowledge of A/B testing, unit economics and CJM.
Zakhar Burak
BI Architect
Experience: 10 years in business analytics. Expert in Power BI, Tableau and Snowflake.
Anna Belova
Fintech Analyst
Experience: 7 years in risk analysis and forecasting.
Maxim Sokolov
E-commerce guru (Wildberries, Lamoda)
Experience: 5 years in marketplace analytics.
Choose a favorable rate and start
your journey in the world of IT
Introductory
  • 2 modules
  • Video tutorials
  • Homework
  • Error checking assignments
  • General chat for students and mentors
  • Access - 1 week
  • No certificate
$11
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Basic
  • 9 modules
  • Study materials
  • Homework
  • Practice
  • Check assignments with errors
  • General chat for students and tutors
  • Access to course - 2 months
  • Certificate
$59
Start now
Advanced
  • 11 modules
  • Study materials
  • Homework
  • Practice
  • Check assignments with errors
  • General chat for students and tutors
  • Access to course - 6 months
  • Certificate
$70
Start now
Premium
  • Individual support from a mentor
  • 11 modules
  • Study materials
  • Homework
  • Practice
  • Error checking
  • General chat for students and mentors
  • Access to course - 12 months
  • Certificate
$85
Start now
Corporate
  • Groups of 5 to 10 people
  • 11 modules
  • Study materials
  • Homework
  • Practice
  • Checking assignments with errors
  • General chat for students and supervisors
  • Course access - 12 months
  • Certificate
$750
Start now
Certificate
of Completion
Upon successful completion of the Analytics Engineer course, you will receive an official certificate that validates your skills, adds value to your resume - employers see that you have received structured training.
It doesn't matter how old
you are or what background
you have - the program is
built in such a way that you
will succeed.
We will refund your
money in full if the
course is not suitable
Money can be returned at any time.
During the first three lessons, we will
refund you the full amount, and
starting from the fourth, we will
calculate the refund amount or help
you choose another course instead.
You will learn from practicing experts
with extensive commercial experience
Mikhail
32 years old
The course gave me all the skills I needed to start my career as a 1C developer. I really liked the presentation of the material - immediately after the training I was able to start real tasks at work. Thank you for the clear structure and support!
Alina
25 years old
After university I couldn't find a job - I needed experience everywhere. Thanks to my portfolio from the course (analysis of user behavior for a startup) I was hired as a product analyst in an IT-company. Now I earn 2 times more than my classmates!
Artem
41 years old
I thought it was impossible to get into IT after 40. I was wrong! The course gave me concrete skills: SQL, dashboards, forecasting. After 2 months I did a project on warehouse optimization and was hired by SberLogistics as a BI-analyst.
Ekaterina
28 years old
I wanted to understand data more deeply, not just look at reports. During the course I learned how to set up end-to-end analytics and calculate ROMI. Now I save the company 300+ thousand on advertising every month - I got a promotion!
Dmitry
19 years old
I took the course in parallel with my studies. Final - analysis for food delivery (found why clients were lost). I found a job BEFORE my diploma! My analytics colleagues were shocked that I had no experience and understood DBT and Python so quickly.
Questions and answers about the course
What are the requirements to enroll in the course?
No specialized knowledge of data analytics is required for the course. However, basic computer
skills and an understanding of basic math will help you learn the material more easily.
What topics are covered in the program?
The course covers the key areas of SQL and Python for data analysis, working with Google Tables, end-
to-end analytics, visualization in Power BI/Tableau, predictive modeling, and building dashboards.
Is a certificate issued after completing the course?
Yes, upon successful completion of the training, you will receive a certificate to
prove your skills as a data analyst. You can add it to your resume or LinkedIn.
How is the training: face-to-face or online?
The training is fully online - you will be able to study the materials at your convenience. Access
to lectures, practical assignments and mentor support through our education platform.