Project Background
The course combines mathematical fundamentals, hands-on Python programming, and real-world problem solving, enabling students to develop practical skills for starting a career in data science.
Delivered through a fully automated Slack-based learning environment and becoming an obligatory part of the curriculum, the program integrates AI tools and methodologies directly into the educational process.
Students work on applied lab cases such as determining friction coefficients using linear and polynomial regression, estimating thin film thickness from spectral data, and solving two-dimensional stationary Navier–Stokes equations using Physics-Informed Neural Networks (PINNs) with DeepXDE, alongside participating in competitions and completing a full-cycle machine learning project.
Overall, the program equips the next generation of Ukrainian scientists and engineers with a combination of classical technical knowledge and hands-on AI capabilities.
A 14-week Data Science Fundamentals and Python program launched for Physics and Mathematics faculty bachelor students at Kyiv Polytechnic Institute (KPI), designed to build a strong foundation in data science, machine learning, and applied AI.
Objectives
Establish fundamental data science literacy: refresh and strengthen key mathematical concepts essential for machine learning.
Build practical coding and tool skills: use Python and data science libraries to implement standard workflows.
Apply machine learning in practice: solve real problems through hands‑on activities and competitions.
Deliver a full‑cycle project experience: guide students through problem scoping, model development, evaluation, and deployment.
Key Features Delivered
Comprehensive 8‑module curriculum covering math review, Python, ML techniques, and applied workflows.
Blended learning activities including MOOCs, labs, programming tasks, and competitions.
Real‑world benchmarks via participation in Kaggle competitions to test and compare models.
Final capstone project where students build an end‑to‑end machine learning solution.
Career alignment with job preparation and application pathways for junior data science roles.
Our Approach
Curriculum Customization: Tailored the Data Science Fundamentals syllabus to align with the academic goals of Kyiv Polytechnic Institute and student readiness levels.
Blended Delivery: Combined online resources (top MOOCs), hands‑on coding labs, and structured assessments to ensure balanced theory and practice.
Active Learning: Encouraged student engagement through Kaggle competitions and peer learning.
Capstone Project: Guided students to apply the full machine learning lifecycle – from data acquisition to model deployment – on a real‑life data problem.
Mentorship & Support: Delivered ongoing instructor feedback to solidify understanding and accelerate skill acquisition.
Results
Reinforced Foundations: Students gained a stronger footing in data science, mathematics, and essential programming skills.
Applied Competency: Participants completed machine learning tasks and competed in Kaggle challenges.
Full‑Cycle Experience: Learners delivered end‑to‑end machine learning projects demonstrating real-world problem-solving ability.
Career Preparedness: Graduates of the program were prepared to apply for Junior Data Scientist positions and build foundational portfolios.
Next Project
