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The course "Data Economy: Monetization of data using AI"

Turn data into money. Learn how to create and market commercial data products using artificial intelligence.

This course for:
For analysts, data scientists, and data engineers who want to reach a new level and create products, not just reports.
Education format:
  • Duration: 7 weeks
  • Training mode: 3-4 hours per week
  • Format: Mixed (online lectures + live practical sessions with experts)
  • Bottom line: A working prototype of your own data product and a ready-made business plan for its monetization.
This course about:

The world has moved from simple data collection to active monetization.

Data is the new oil, and AI is a refinery that turns raw materials into an expensive product.


This practical course will give you all the necessary tools and knowledge, from designing the right data architecture to bringing a ready—made data product to market.

You will learn not just how to analyze, but how to create commercial solutions that bring real profits.

This course is for you if you:
  • A Data Scientist/Analyst who wants to understand how your models turn into products.
  • A data engineer who strives to build not just an infrastructure, but platforms for creating data products.
  • A product manager in the field of data who wants to understand more deeply the technical aspects and monetization.
  • A specialist who sees the potential of data in his company, but does not know how to make commercial use of it.
You will learn:
  • Design a scalable data architecture (Data Mesh, Data Fabric) for efficient data management.
  • Apply LLM (large language models) to automate analytics and generate insights.
  • Create working prototypes of data products using MLOps practices.
  • Develop semantic search systems and personalized recommendations.
  • Evaluate the economics of a data product: consider unit economics, LTV, CAC, and ROI.
  • Ensure that your solutions comply with the requirements of 152-FZ, GDPR and ethical standards of AI.
Course program
The course is based on the principle of "from theory to practice": each module is anchored by a real task that leads to the creation of your final project.

Module 1: Modern Data Architecture
Building the foundation for scalable data products.

  • Data Mesh and Data Fabric.
  • Vector databases for AI.
  • The principles of DataOps and MLOps.

Practice: Designing a Data Mesh for a company.


Module 2: Advanced Analytics using LLM
We use AI as an analyst's assistant.

  • AI assistants for analysis.
  • Generation of hypotheses and insights.
  • Automation of reporting and visualization.

Practice: Setting up an AI assistant for analyzing the dataset.


Module 3: Creating AI-based data products
From prototype to production.

  • MLOps for production.
  • Monetization models (SaaS, Pay-per-Use).
  • UX for data products.

Practice: Creating a working prototype of a service with an AI API.


Module 4: Semantic Search and recommendation systems
We make search smart and recommendations personalized.

  • Vector embeddings.
  • Building a semantic search.
  • A/B testing of recommendations.

Practice: Implementing semantic search for a directory.


Module 5: Blockchain for Data Management
We provide trust, audit and control.

  • Decentralized Storage (IPFS).
  • Self-Sovereign Identity (SSI).
  • Access control via smart contracts.

Practice: Development of an access control system scheme.


Module 6: AI ethics and regulation
We create responsible and legitimate solutions.

  • Principles of Responsible AI.
  • Requirements of 152-FZ and GDPR.
  • Data security and privacy.

Practice: Creating a compliance checklist for the project.


Module 7: Scaling of data products
We bring the product to the market and grow.

  • Growth Strategies (Product-Led Growth).
  • Unit-economics of data products.
  • Partnerships and international expansion.

Practice: Developing a monetization business plan.

The final project

You will create and protect your own data product from an idea to a monetization plan.


What is included in the project:


  • Technical prototype: A working prototype (for example, a customer churn prediction service or Ravil Akhtyamov: a semantic search system).
  • Financial model: Calculation of cost, revenue, LTV/CAC and ROI.
  • Investor Presentation: A ready-made solution that you can show to management or investors.
How the training is structured

80% of the training consists of practice and real cases.

  • Practical tasks: After each module, you put your knowledge into practice, creating your product step by step.
  • Live sessions with experts: Regular online meetings with case studies, answers to questions and feedback on projects.
  • Closed community: Communicate with fellow students, graduates, and faculty in a shared chat.
  • Personal progress tracker: You always see how you are moving towards the goal, and you receive the support of a curator.
  • Templates and guides: Ready-made documents and code that you can use in your work.

At the end of the course, you will receive:

  • Certificate of successful completion.
  • A working prototype and business plan for your portfolio.
  • Access to the alumni community and career opportunities.
Ready for the course?
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Contacts:
ravilakhtyamov@yandex.ru
@digitaleconomylab
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