-Why blockchain exists and what it solves
-Centralized vs decentralized trust
-Wallets, transactions, block explorers

-Hashes, signatures, immutability
-Proof of Work vs Proof of Stake
-Validators, incentives, finality

-Ethereum, EVM, gas, events
-Solidity fundamentals
-First testnet deployment

-Token standards & OpenZeppelin
-Access control, testing, gas
-Upgradeability trade-offs

-End-to-end dapp architecture
-Indexing events & L2 basics
-Secure integrations

-Build a real EVM feature
-Client, indexer, CI setup
-Testnet deploy & demo

-Why blockchain exists and what it solves
-Centralized vs decentralized trust
-Wallets, transactions, block explorers

-Hashes, signatures, immutability
-Proof of Work vs Proof of Stake
-Validators, incentives, finality

-Ethereum, EVM, gas, events
-Solidity fundamentals
-First testnet deployment

-Token standards & OpenZeppelin
-Access control, testing, gas
-Upgradeability trade-offs

-End-to-end dapp architecture
-Indexing events & L2 basics
-Secure integrations

-Build a real EVM feature
-Client, indexer, CI setup
-Testnet deploy & demo

Data Analytics

Practice-driven upskilling programs by ACA Uzbekistan

-Why blockchain exists and what it solves
-Centralized vs decentralized trust
-Wallets, transactions, block explorers

-Hashes, signatures, immutability
-Proof of Work vs Proof of Stake
-Validators, incentives, finality

-Ethereum, EVM, gas, events
-Solidity fundamentals
-First testnet deployment

-Token standards & OpenZeppelin
-Access control, testing, gas
-Upgradeability trade-offs

-End-to-end dapp architecture
-Indexing events & L2 basics
-Secure integrations

-Build a real EVM feature
-Client, indexer, CI setup
-Testnet deploy & demo

APPLY

Why
Data Analytics?

High Market Demand

Organizations across all industries rely on data analysts
to support decision-making & growth.

Data-Driven Decision Making

Data analytics transforms raw data into insights
that guide strategy and operations.

Cross-Industry Relevance

Data analytics skills are applicable in finance, tech, marketing,
healthcare, and many other fields.

Price: --

duration: 4 months

Format: live sessions

Course details

Meet
the leaders

Principal Product Manager @Noom Inc.

Narek Tamoyan

Lead Business Intelligence Analyst @EPAM Poland

Vardges Bokhyan

Hovhannes Ayvazyan

Senior Big Data Engineer @Grid Dynamics

Sebastia Isaghulyan

Head of Lean-Agile @SoftConstruct

Senior Product Manager @Delivery Hero

Kristian Martirosyan

Head of Product @ Affinidi

Vardan Grigoryan

Yulia Shahnazarova

Agile Coach @Ameriabank

esim

esim

Hayk Poghosyan

esim

esim

Foundations of Data Analytics for FinTech

A. What data analytics means in FinTech organizations
B. ETL vs ELT in modern FinTech stacks
C. Modern data stack overview

A. Data normalization principles
B. Fact and dimension tables in FinTech
C. Data quality, testing, and documentation

Analytics Engineering & Data Modeling

A. Relational databases and FinTech data models
B. SQL fundamentals: joins, aggregations, filtering
C. Advanced SQL techniques

Data Management, SQL & Pipelines

Business Intelligence & Visualization

A. BI principles for FinTech stakeholders
B. Power BI data modeling and optimization
C. DAX fundamentals and advanced calculations

A. Python fundamentals for analytics
B. Exploratory Data Analysis (EDA)
C. Optimization and data-driven decision-making

Advanced Analytics & Experimentation

Experience

At least 1 year of hands-on experience in the product management field.

Course will contain materials in English. For the better understanding at least B1 lvl is required.

You will need 9-10 hours free time weekly for lessons and doing homework.

Our specialist will contact you to explain the course format and enrollment process.

Apply for
the course

Partners

If you have any other questions, we’ll be happy to answer them on any platform

Contact Us