Hello!

My name is Iliyan Staykov, and I am an enthusiastic Computing and IT graduate with a solid foundation in data analysis, machine learning, software and web development. My academic and dissertation work has honed my skills in coding, analytics, and problem-solving. I enjoy solving complex challenges, transforming data into actionable insights, and developing impactful software solutions that drive meaningful outcomes, with a proven track record of enhancing operational efficiency and profitability in real-world applications.

This repository is a reflection of my journey in technology, showcasing academic projects, technical expertise, and a commitment to continuous learning with focus on data analysis. I'm excited to connect with others who share a passion for innovation and problem-solving.

• Programming: Python, Java, SQL
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, SciPy
• Data Analysis: Data cleaning, Transformation, EDA, Visualisation
• Databases: MySQL, PostgreSQL, MongoDB
• Machine Learning: Neural networks (RNN, CNN, DNN), Clustering, Decision tree
• Web Development: HTML, CSS, JavaScript
• Algorithms: Strong foundation in data structures and computational theory

This project involved a comprehensive analysis of sales and product data from a multi-channel retail operation. The goal was to leverage underutilized data to drive improvements in sales performance, inventory management, and customer satisfaction by focusing on key metrics such as revenue, profit margins, and order volume.

• Tools & Techniques: Used Python (Pandas, Matplotlib, Seaborn), SQL, and advanced EDA techniques.
• Data Scope: Analyzed over 18,636 transaction records from 2017 to 2024, capturing intricate details across physical and e-commerce platforms. Included metrics on product categories, pricing, revenue, cost, and profit margins, along with customer purchasing behaviors and sales channel performance.

• Business Model Insights - Uncovered a volume-driven business model with an average transaction value of £88.15, where revenue is driven by mid-priced items (£60–£70). The analysis confirmed a consistent pricing strategy with minimal discounts and a markup of 1.25x, achieving steady profit margins averaging 49.4%.

• Top Performers - Identified women's footwear (sandals, sneakers, boots) as the leading category, contributing significantly to revenue and profitability, with some items achieving margins exceeding 72%.

• Challenges & Opportunities - Addressed challenges with Women’s Boots and Shoes, which faced negative margins from excessive discounting and cost inefficiencies. Highlighted over 6,600 transactions with average discounts of -£34.59 impacting profitability.

• Seasonality and Sales Dynamics - Examined the impact of seasonality on sales, noting significant demand peaks during Autumn/Winter, with sharp declines in off-seasons. The analysis also highlighted the dominance of offline sales and the underutilization of online sales channels, presenting growth opportunities.

• Customer Preferences - Detailed the pricing preferences with products between £60 and £150 dominating sales, while niche premium items contribute disproportionately to revenue, serving a smaller, specific audience.

The insights provided enabled strategic realignment of inventory with market demands, optimization of pricing strategies, and leveraging of seasonal trends to enhance profitability and customer satisfaction.

Explore the full analysis here.

This project provides an in-depth analysis of fixed broadband coverage across 374 UK local authorities using Ofcom datasets. The analysis tracks significant improvements and regional disparities in broadband acess from 2019 to 2023. It includes a detailed examination of various broadband types such as Superfast (SFBB), Ultrafast (UFBB), Full Fibre, and Gigabit broadband.

• Data Handling - Python, Pandas for data cleaning and analysis.
• Database - MongoDB for data storage
• Visualisation - Matplotlib and Folium for creating insightful maps and charts
• Data Scope - Aggregated 1,870 records from five years of data, leveraging multi-dimensional metrics like Full Fibre and Superfast Broadband.

• Increased Coverage - SFBB coverage increased from 93.62% in 2019 to 96.07% in 2023. UFBB saw a rise from 47% to 70%, and Gigabit broadband jumped from 8.5% to nearly 70%.

• Regional Disparities - Notable coverage gaps in rural areas compared to urban centers like Birmingham and Leeds. The Orkney and Shetland Islands remain significantly underserved.

• Infrastructure Investments - Analysis of areas with rapid infrastructure developments and those still facing digital divides.

• Actionable Insights - Recommendations for targeted infrastructure investments in underserved areas.
• Policy Recommendations - Strategies to enhance broadband coverage, focusing on public-private partnerships and investments in rural connectivity.

Explore the full analysis here.

BSc (Hons) Computing and IT

Open University, UK | 2021-2024 | Grade: 2:1

• Learning techniques: Studied supervised and unsupervised learning techniques, including deep neural networks, CNNs, and RNNs.
• Model Development: Developed and evaluated machine learning models using Python libraries like TensorFlow, Keras, and Scikit-learn.
• Practical Applications:Worked on applications such as image recognition, autoencoders for data compression, and anomaly detection.
• Data Challenges: Addressed challenges like data preprocessing, bias mitigation, and imbalanced datasets.
• Ethics in AI: Explored societal and ethical considerations of machine learning.

• Database Systems: Worked with relational and NoSQL databases, focusing on data integrity and transaction management.
• Data Analysis: Performed exploratory data analysis (EDA) and data visualization using Python libraries like Pandas, NumPy, and Matplotlib.
• Data Cleaning: Cleaned and transformed datasets using tools like SQL and OpenRefine.
• Large-Scale Data: Gained experience handling large-scale datasets and addressing legal and ethical data considerations.

• Algorithm Design and Analysis: Developed skills in creating efficient algorithms and evaluating their performance.
• Data Structures: Gained proficiency in implementing and utilizing structures such as lists, stacks, queues, trees, and graphs to manage and organize data effectively.
• Computability Theory: Explored the theoretical foundations of what problems can be solved computationally, enhancing problem-solving capabilities.
• Python Programming: Applied concepts through practical programming assignments using Python, reinforcing both theoretical and coding skills.

• Object-Oriented Principles: Mastered core concepts including classes, objects, inheritance, polymorphism, and encapsulation, essential for modular and maintainable code development.
• Java Programming: Acquired practical experience in Java, focusing on writing robust and efficient object-oriented applications.
• Software Development Practices: Emphasized good programming practices, including code readability, debugging, and testing, to ensure high-quality software development.
• Use of Development Tools: Utilized integrated development environments (IDEs) like BlueJ to design, implement, and test Java applications.

• Software Development Lifecycle: Gained a comprehensive understanding of the processes involved in designing, building, and testing software systems to meet specified requirements.
• Software Engineering Concepts: Explored fundamental principles and practical approaches to software development, emphasizing disciplined methodologies.
• Collaborative Online Exercises: Participated in collaborative online exercises as part of tutor-marked assignments, enhancing teamwork and practical application of software engineering principles.

• Foundations of Web Technology: Studied the basic technologies on which the Web is founded, including protocols, standards, and content handling.
• Web Application Architectures: Explored different approaches to web application architecture, focusing on components of the client-server architecture and dynamic content delivery.
• Mobile Content and Applications: Examined the trend toward more portable content and content customization, including the development of simple mobile applications.
• Application Development Lifecycle: Learned how applications are planned, designed, developed, deployed, and maintained by IT professionals.