Question 1

Who is Mikołaj Paweł Sapek?

Accepted Answer

Mikołaj Paweł Sapek is a data scientist based in Copenhagen, Denmark, specializing in AI implementation, workflow automation, and political data analysis. He holds an MSc in Business Administration & Data Science from Copenhagen Business School and a BSc in Management & Artificial Intelligence from Kozminski University (graduated with distinction).

Question 2

What does Mikołaj Sapek specialize in?

Accepted Answer

Mikołaj specializes in building AI-powered data pipelines and workflow automation using Python, R, n8n, Make.com, and OpenAI API. His work includes automating news analysis (reducing processing from 36 hours to 20 minutes), political election data analysis using statistical methods, and full-stack web development with Next.js and Supabase.

Question 3

What projects has Mikołaj Sapek built?

Accepted Answer

Key projects include: a bachelor thesis analyzing third-force candidates' impact on voter turnout in Polish presidential elections using R and PKW data; Apron, a B2B shift management platform for Denmark built with Next.js and Supabase; automated data aggregation workflows using n8n for a real estate data company; and a traffic sign recognition system using convolutional neural networks achieving 96.9% test accuracy.

Question 4

Where did Mikołaj Sapek study?

Accepted Answer

Mikołaj is currently pursuing an MSc in Business Administration & Data Science at Copenhagen Business School in Denmark. He completed his BSc in Management & Artificial Intelligence at Kozminski University in Warsaw, Poland (2022–2025, graduated with distinction). He also studied on exchange at Konkuk University in Seoul, South Korea, and attended a Global Summer Program at Singapore Management University.

Bachelor Thesis

Projects

Banana Ripeness Classification

Echo Chamber Linguistics

Forecasting Danish Pharmaceutical Exports

Apron

Klaudiush

Alex Portfolio

Traffic Sign Recognition Project

Public Data Aggregation Automation

Auto Estate Generator

Election Data Cleaning Project

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