Єлизавета

Profile
Motivated and detail-oriented aspiring Data Analyst with a strong academic
foundation in Mathematics, Statistics, and Information & Communication Studies.
Yelyzaveta Skilled in Python, SQL, and data visualization tools such as Power BI and Excel.
Experienced in handling, cleaning, and analyzing data to extract meaningful insights.
Deviataieva Demonstrates excellent analytical thinking, teamwork, and communication abilities.
Eager to apply data-driven solutions to real-world challenges and contribute to
impactful decision-making in a dynamic team environment.
Details
38 Glydeview
A91 P383, Tallanstown
Education
Ireland
Complete General Secondary Education, Goloprystan Lyceum 2 , Hola
[open contact info](look above in the "contact info" section)
Prystan
[open contact info](look above in the "contact info" section) S E P T E M B E R 2 0 1 2 — M AY 2 0 2 3
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Graduated with High Honours
N AT I O N A L I T Y

Ukrainian Secondary Education, Colaiste Chu Chulainn, Dundalk
AUGUST 2022 — JUNE 2024
D AT E / P L A C E O F B I R T H

10/01/2006
Bachelor , University College Dublin | UCD, Dublin
Ukraine O C TO B E R 2 0 2 4 — P R E S E N T

• Specialisation in Information & Communication Studies
• Specialisation in Mathematics
Links
• Specialisation in Statistics
Linkedln
• Web Design and Analytics
• German

Skills
Course, Dan.it education
Analytical & Business Skills O C T O B E R 2 0 2 4 — M AY 2 0 2 5

Mathematics & Statistics Technical Skills:

Digital Technology • Python (pandas, NumPy, matplotlib, seaborn, scikit-learn)
• SQL (PostgreSQL, MySQL, SQLite)
Teamwork and Communication
• R (if applicable)
Attention to Detail
Data Visualisation Tools:

• Power BI
Languages • Microsoft Excel (pivot tables, Power Query, advanced formulas, VBA – if used)
Ukrainian Data Handling & Processing:

• ETL (Extract, Transform, Load) processes
Russian
• Data collection via APIs
• Data cleaning and preprocessing
English
• Data wrangling and transformation

German Databases:

• Relational databases (PostgreSQL, MySQL, MS SQL Server)
• NoSQL databases (MongoDB – if applicable)

Machine Learning (for advanced analysts):

• Basic ML algorithms: regression, classification, clustering
• Libraries: scikit-learn, XGBoost
• Feature engineering and model evaluation