Олександра

OLEKSANDRA YAKOVENKO
Data Analyst

CONTACT PROFILE
[открыть контакты](см. выше в блоке «контактная информация») Data Analyst skilled in SQL, Python, statistics, and BI
[открыть контакты](см. выше в блоке «контактная информация») dashboards, with a strong analytical background backed by

Telegram IELTS 8.0, 200/200 in Mathematics on the Ukrainian NMT,

LinkedIn and regional Mathematics Olympiad awards. Focused on
turning raw data into clear insights and communicating them
Warsaw, Poland
effectively to business and technical audiences.

EDUCATION
National Technical University of Ukraine WORK EXPERIENCE
‘Dnipro Polytechnic Institute’
DATA SCIENCE INTERN
2023 - 2027
MODUS X
BSc in Computer Science
Aug 2025 - Jan 2026

AWARDS & Built and improved Copilot for Microsoft 365 AI agents,
CERTIFICATIONS analyzed performance and key metrics, working with
large-scale energy datasets.
Data Analyst course at Mate academy
Delivered data storytelling insights and
IBM Data Analyst Professional Certificate
recommendations to stakeholders.
at Coursera
Led internal webinars on practical AI use cases and
Data Scientist: Machine Learning Career
adoption in business.
Path at Codeacademy

SKILLS PORTFOLIO
Spreadsheets: Pivot Tables, Data
Cleaning, Visualization E-COMMERCE SALES PERFORMANCE ANALYSIS
SQL: Advanced Queries, Window This project analyzes e-commerce sales performance using
Functions, Query Optimization, Common SQL, Python, and Tableau.
Table Expressions (CTE) The goal was to understand sales performance across
Python: Pandas, NumPy, Matplotlib, geography, product categories, traffic channels, devices,
Seaborn, Exploratory Data Analysis (EDA) and user types.
Tableau: Interactive Dashboards, Data Link to review
Storytelling, Publishing
Statistics: Sampling, Hypothesis A/B TESTING SIGNIFICANCE ANALYSIS TOOL
Testing, Correlation, Statistical This project is an A/B testing analysis tool built with BigQuery
Significance SQL, Python, and Tableau.
A/B Testing: Planning, Analysis, The goal is to evaluate whether the differences between
Reporting control and test groups are statistically significant for key
Other Tools: Jupyter Notebooks, Google funnel metrics. The analysis is performed both at the overall
Analytics, Excel, Git test level and across separate user segments.
Languages: English (Advanced),
Link to review
Ukrainian (Native)