Костянтин

Kostyantyn Beschetnov
Business Economics Student | Aspiring Data Analyst

Kyiv, Ukraine • [open contact info](look above in the "contact info" section) • [open contact info](look above in the "contact info" section) • LinkedIn • GitHub

SUMMARY

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Second year Business Economics student at KSE with a strong analytical foundation and hands-on
experience with building end-to-end data projects. Proficient in Excel/Google Sheets, SQL, Python, and
R, focus on translating raw data into actionable business insights. Built a full retention and delivery
analysis on a real 100k+ order e-commerce dataset.​
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EDUCATION

Kyiv School of Economics (KSE)
Business Economics Graduating 2028
●​ Key Courses: Big Data Analytics, Statistics and Data Visualization, Marketing,
Financial Accounting, Microeconomics & Macroeconomics, Econometrics

HARD SKILLS

●​ SQL: Advanced Joins, CTEs, Window Functions, Conditional Aggregations, Subqueries
●​ Python: Pandas, NumPy, Matplotlib, Seaborn
●​ R: Tidyverse — Advanced dplyr, ggplot2
●​ Tableau: Data Visualization, Interactive Dashboards, Data Filtering
●​ Excel / Google Sheets: Pivot Tables, Advanced Formulas
●​ Data Analysis: Exploratory Data Analysis (EDA), KPI Tracking, Cohort Retention & CLV
Modeling, Geospatial Analysis, Data Storytelling.
●​ Tools: BigQuery, GoogleColab, DataCamp, GA4
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PROJECTS

Olist E-Commerce: Delivery, Satisfaction & Retention Analysis (GitHub) | Python, SQL (SQLite3)​

SQL — Delivery & Customer Satisfaction Analysis
•​ Find out how delivery speed and on-time rate affect customer review scores (1–5 stars).
•​ Quantified the delivery impact: Satisfied (5-star reviewers) receive their orders in 10.6 days on
average, which is twice as fast as unsatisfied(1-star reviewers) where the delivery time is 21.3 days
on average.
•​ Missing the estimated delivery date drops the average score from 4.29 → 2.27.
•​ Found regional logistics bottlenecks across Brazil. Some cities had an overdue rate of over 20%.​

Python — Cohort Retention, CLV & Geospatial Analysis
•​ Built monthly cohort retention heatmaps using Pandas and Seaborn. The plots revealed a critically
low platform repeat-purchase rate.
•​ Calculated global CLV and per-cohort cumulative CLV growth curves; aggregated AOV, CLV, and
retention rate by Brazilian state to identify high and low value regions.
•​ Merged SQL findings with Python findings to showcase the full pipeline:
Slow Delivery → Poor Satisfaction → Low Retention.

CERTIFICATIONS

●​ DataCamp:
○​ SQL Associate certificate

LANGUAGES

•​ Ukrainian: Native
•​ English: Upper-Intermediate / Advanced (B2–C1)