This repository contains exercises and projects for learning functional programming in Clojure, focusing on solving mathematical and algorithmic tasks using a functional approach. The goal of this project is to practice functional programming concepts such as:
- Pure functions – functions without side effects
- Immutability – avoiding mutable state
- Recursion – replacing loops with recursive calls
- Higher-order functions – functions that take other functions as arguments or return functions
- Collections and sequences – leveraging lists, vectors, maps, and sets for mathematical computations
- ⚡ Pure Functions – Functions without side effects, producing predictable results every time.
- 🔒 Immutability – Avoiding mutable state to write safer, more reliable code.
- 🔄 Recursion – Replacing loops with elegant recursive calls for clean, efficient solutions.
- 🎯 Higher-Order Functions – Functions that accept other functions as arguments or return them as results, enabling modular and composable logic.
- 📚 Collections and Sequences – Mastering lists, vectors, maps, and sets for mathematical computations and functional data manipulation.
Through these exercises, learners gain hands-on experience applying functional programming concepts to real-world mathematical and algorithmic challenges. This process strengthens the ability to:
- Write clean, maintainable, and robust code
- Approach problems with a functional mindset
- Build solutions that are scalable and composable
- Connect theoretical knowledge with practical implementation
By completing this journey, learners not only improve their Clojure skills but also gain an appreciation for functional programming paradigms that are widely used in backend development, data pipelines, and algorithmic systems.
This repository is ideal for anyone looking to bridge the gap between theoretical knowledge and real-world application, making it a strong showcase for internship or junior developer opportunities.
All tasks are implemented in Clojure and focus on applying these principles to mathematical problems, such as:
- Arithmetic and algebraic computations
- Factorials, Fibonacci numbers, and sequences
- Prime number generation and testing
- List and sequence manipulations
- Recursion-based mathematical problem solving
As part of the onboarding program, I practiced designing APIs, rapid API description, and functional integration to handle data transfer between systems.
Key objectives:
- Write clear and concise API specifications for sending, receiving, and transforming data.
- Implement modular, composable functions to interact with APIs using functional principles.
- Ensure data integrity and immutability during processing and transfer.
- Build functional pipelines for reliable data flow across systems.
- Apply error handling and validation in a functional style for robust endpoints.
This experience demonstrates how FP concepts can improve system integration, maintainability, and rapid development of backend services.
This approach allows you to connect multiple systems reliably while learning how FP concepts like pure functions, composition, and immutability improve API design and maintainability. During my onboarding program, I was part of a team working with core algorithms in a product-oriented company, gaining real-world experience in developing backend solutions.
Key highlights of this experience:
- Implemented and optimized algorithms for data processing and functional pipelines in a production-like environment.
- Contributed to the backend logic powering real products, ensuring scalability, efficiency, and maintainability.
- Collaborated with the team to design, test, and deploy algorithmic solutions following best practices.
- Practiced functional programming and composable pipelines to improve clarity and reliability of code handling real data.
This experience strengthened my understanding of how AI and algorithms are applied in real products, bridging theoretical knowledge with practical, production-ready implementations.
The codes for APIs are confedential and cannot be post on my github account.
Mihaela Koseva (Михаела Косева) • Sofia University (Софийски университет) • AI Engineer • Software Engineer • Backend Engineer • Data Systems & APIs • Applied Machine Learning • Deep Learning • Neural Networks • Model Training • Data Pipelines • Data Science • LLMs • Python • C++ • Java • Clojure • SQL • PyTorch • TensorFlow • Scikit-learn • Pandas • NumPy • ETL • Data Modeling • MLOps
© 2026 Mihaela Koseva (Михаела Косева) • Софийски университет • Original portfolio design.
🔗 Explore on GitHub: Mihaela Koseva (Михаела Косева) • Software Engineer • AI • ML • Dreamerol
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