Петро

PETRO YANIV
Phone: [відкрити контакти](див. вище в блоці «контактна інформація») | Email: [відкрити контакти](див. вище в блоці «контактна інформація»)
LinkedIn: [відкрити контакти](див. вище в блоці «контактна інформація») | GitHub: github.com/PetroYaniv

PROFESSIONAL SUMMARY

A motivated and analytically oriented graduate of the Department of Applied Mathematics of the Lviv
Polytechnic National University, with a strong foundation in mathematical modeling, statistical
analysis, and software engineering. Has practical experience in developing algorithmic solutions,
complex data structures, and predictive machine learning models. Possesses solid hands-on
experience in building scalable Data Warehouse (DWH) architectures and optimization algorithms.
Specialized in leveraging Python, C++, Java and Machine Learning frameworks (Scikit-Learn,
TensorFlow) to solve complex algorithmic challenges, including neural-network-based startup
success forecasting and multivariate regression analysis.

EDUCATION

Lviv Polytechnic National University
Bachelor of Science in Applied Mathematics | Lviv, Ukraine (September 2022 – June 2026)
• Relevant Coursework: Mathematical Analysis, Linear Algebra & Analytic Geometry, Probability
Theory & Mathematical Statistics, Differential Equations, Numerical Methods, Discrete
Mathematics, Object-Oriented Programming (C++/Java), Data Structures and Algorithms,
Databases & Information Systems, Machine Learning Foundations.
• Bachelor's Topic: «Intelligent system for predicting the success of HealthTech startups and
forming a venture portfolio based on neural networks»

TECHNICAL SKILLS

• Programming Languages: C++, Python, Java, SQL, MATLAB, R
• Mathematical Modeling: Statistical Analysis, Econometric Modeling, Numerical Optimization,
Linear Programming, Predictive Modeling
• Tools & Frameworks: Git, Docker, NumPy, Pandas, Scikit-Learn, TensorFlow, LaTeX
• Software Engineering: OOP, Data Structures, Algorithm Design, Database Management
(PostgreSQL, MySQL, DWH)
• Languages: Ukrainian (Native), English

ACADEMIC & PROGRAMMING PROJECTS

Course Project: ETL Process & Data Warehouse for Ultra-Large Data | Python, SQL, Git, DWH
• Designed and deployed a scalable Data Warehouse (DWH) architecture and engineered robust
ETL pipelines to process and clean ultra-large datasets.
• Implemented data aggregation and advanced analytical queries to derive structured, actionable
insights from raw data fabrics.
• Repository: https://github.com/PetroYaniv/kursova_NVBD_Variant_02_Yaniv.git
Cross-Platform Game Development: Battleship | Java, OOP, JavaFX/Swing, Git
• Developed a cross-platform Battleship game utilizing Java, strictly adhering to clean code
standards and core Object-Oriented Programming principles.
• Designed an intuitive Graphical User Interface (GUI) and engineered complex game-state logic
for matrix-based grid placements, turn-based mechanics, and real-time validation.
• Repository: https://github.com/PetroYaniv/CrossPlatformProgramming.git
Calculation Work: Econometric Regression Analysis & Predictive Modeling | Python, EXCEL,
Statistical Analysis
• Constructed linear and multivariate econometric regression models via Ordinary Least Squares
(OLS) to analyze corporate profit dynamics and technology pricing factors.
• Evaluated model reliability using Student's t-test, Fisher's F-test, and adjusted R² coefficients.
• Performed advanced statistical diagnostics: validated the model against multicollinearity using
the Chi-Square (χ²) test, verified the absence of heteroscedasticity via the Goldfeld-Quandt test
and μ-test, and analyzed residual autocorrelation with the Durbin-Watson statistic.