Python Security Training Course

This intensive, hands-on course covers advanced Python techniques, engineering best practices, and commonly used design patterns to build maintainable, testable, and high-performance Python applications. It emphasizes modern tooling, typing, concurrency models, architecture patterns, and deployment-ready workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level Python developers who wish to adopt professional practices and patterns for production-grade Python systems.

By the end of this training, participants will be able to:

• Apply Python typing, dataclasses, and type-checking to increase code reliability.
• Use design patterns and architecture principles to structure robust applications.
• Implement concurrency and parallelism correctly using asyncio and multiprocessing.
• Build well-tested code with pytest, property-based testing, and CI pipelines.
• Profile, optimize, and harden Python applications for production.
• Package, distribute, and deploy Python projects using modern tools and containers.

Format of the Course

• Interactive lectures and short demos.
• Hands-on labs and coding exercises each day.
• Capstone mini-project integrating patterns, testing, and deployment.

Course Customization Options

• To request a customized training or focus area (data, web, or infra), please contact us to arrange.

This course teaches practical engineering techniques to design, build, test, and deploy agentic (autonomous) systems using Python. It covers the agent loop, tool integrations, memory and state management, orchestration patterns, safety controls, and production considerations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, AI developers, and software engineers who wish to build robust, production-ready autonomous agents using Python.

By the end of this training, participants will be able to:

• Design and implement the agent loop and decision-making workflows.
• Integrate external tools and APIs to extend agent capabilities.
• Implement short-term and long-term memory architectures for agents.
• Coordinate multi-step orchestrations and agent composability.
• Apply safety, access control, and observability best practices for deployed agents.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs building agents with Python and popular SDKs.
• Project-based exercises that produce deployable prototypes.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This is a 5 day introduction to Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python

Artificial Intelligence with Python is the development of intelligent systems using Python’s extensive ecosystem of AI and machine learning libraries.

This instructor-led, live training (online or onsite) is aimed at intermediate-level Python programmers who wish to design, implement, and deploy AI solutions using Python.

By the end of this training, participants will be able to:

• Implement AI algorithms using Python’s core AI libraries.
• Work with supervised, unsupervised, and reinforcement learning models.
• Integrate AI solutions into existing applications and workflows.
• Evaluate model performance and optimize for accuracy and efficiency.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Poland (online or onsite) is aimed at data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

By the end of this training, participants will be able to:

• Set up the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

This instructor-led, live training in Poland (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

By the end of this training, participants will be able to:

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

This instructor-led, live training in (online or onsite) is aimed at developers who wish to use the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

By the end of this training, participants will be able to:

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led, live training in Poland (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

By the end of this training, participants will be able to:

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

The training focuses on practical learning of creating REST APIs using the FastAPI framework. Participants will learn the complete process of building a web application—from understanding client-server architecture and the HTTP protocol, through implementing all CRUD operations, to integrating with a database and securing the application.

The program includes working on a simple, example project that participants build step by step. They will learn to define endpoints, validate input data using Pydantic, handle errors, and return appropriate HTTP status codes. They will also explore two approaches to working with databases: direct SQL queries through psycopg and ORM SQLAlchemy.

We place a strong emphasis on code organization—modularization, separation of logic, and good practices for project structuring. Participants will also learn to test their APIs using TestClient, work with automatically generated documentation, and implement authentication and password hashing mechanisms.

After the training, participants will be able to independently design and implement functional REST APIs connected to a database, secured, and ready for further development. They will gain practical knowledge that allows them to start working as backend developers in Python.

This instructor-led, live training in Poland (online or onsite) is aimed at data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

By the end of this training, participants will be able to:

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

The training covers key tools used in analytical work and data science:
NumPy (array operations), Pandas (tabular data analysis), and visualization libraries.
Modules guide participants from the basics of data processing to creating charts
and exploratory data analysis (EDA).

This training allows participants to gradually enter the world of programming in the Python language. The course covers the most important elements of the language—from basic syntax and working with the environment, through data operations and program flow control, up to functions, modules, and the first elements of object-oriented programming.

Participants will become familiar with key constructs used in daily programming work, learn algorithmic thinking, and organize code in a transparent and consistent manner according to best practices. During the training, significant emphasis is placed on writing high-quality code and error analysis.

Upon completion of the training, participants will be able to independently write simple scripts, create functions and classes, organize projects into files, as well as understand, analyze, and run Python code in practical applications.

The training focuses on building a Retrieval Augmented Generation (RAG) system, which combines the capabilities of large language models with information retrieval from local documents. Participants will learn how to create applications that answer questions based on their own data, eliminating the problem of hallucinations and knowledge limitations in LLMs.

The program guides participants through all key stages of creating a RAG system. They will become familiar with communication principles using the OpenAI API and the LangChain framework, which facilitates working with language models. Participants will learn how to process documents in various formats, divide them into optimal segments, and transform them into vector representations. They will also get to know the Qdrant database and mechanisms for semantic search based on embedding similarity.

We place special emphasis on the quality of system performance—participants will implement result reranking techniques and learn how to quantify retrieval effectiveness and generated responses using the DeepEval library. The program also covers practical aspects of prompt engineering and methods for avoiding typical pitfalls when working with LLMs.

The outcome of the training will be a functional web application built in Streamlit, which allows users to ask questions about their own document base. Upon completing the course, participants will be able to independently design and implement a RAG system tailored to their specific business needs.

In this instructor-led, live training in Poland (onsite or remote), participants will learn how to use Python skope-rules to automatically generate rules based on existing data sets.

By the end of this training, participants will be able to:

• Use skope-rules to extract rules from available data.
• Apply skope-rules to carry out classification, particularly useful in supervised anomaly detection, or imbalanced classification. 
• Generate rules for classifying new incoming data.
• Fit rules to address real-world problems in fraud detection, predictive maintenance, intrusion detection, insurance application approvals, etc.

This training focuses on practical use of the Streamlit library to create interactive web applications and analytical dashboards in Python. Participants will learn to build functional user interfaces without needing knowledge of HTML, CSS, or JavaScript.

The program covers all key components of Streamlit – from basic text elements and layout to interactive input widgets, advanced features such as forms, charts, and file handling. Participants will understand session state management, result caching, and database integration, enabling them to create efficient production applications.

We emphasize practical application – each theoretical module is supplemented with exercises, and the training concludes with the creation of a complete project or two, depending on time availability.

Upon completing the training, participants will be able to design Streamlit applications independently – from simple dashboards to advanced analytical tools. They will gain skills for rapid prototyping of data science solutions and creating interfaces for machine learning models.