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Data Science School 2025

When

July 21—25, 2025

Where

Lviv

Format

Offline

Cost

Free upon selection

 pc

Objective

Data Science School 2025 is all about building a strong foundation in data science, gaining hands-on skills in working with data and machine learning — and growing a community. Together with the National University Lviv Polytechnic, we’re creating an environment where students from different universities learn, sharpen their analytical thinking, and collaborate on real-world challenges.

For whom:

  • 1st–2nd year students interested in analytics, programming, and machine learning
  • 3rd–4th year IT students looking to grow in the field of Data Science
  • Junior specialists and interns in the IT industry who want to systematize or deepen their DS knowledge
  • Participants with no ML experience but with basic Python skills and analytical thinking

Participant requirements

  • Python: basic level (variables, loops, lists, file handling)
  • Libraries: preferably familiar with Pandas, NumPy, and Matplotlib at a tutorial level
  • Data: basics of working with tables and CSV files, fundamental analytics concepts

In program

  • Introduction to Data Science. Basics of Python and Pandas
  • Data Analysis and Visualization: Matplotlib, Seaborn, Plotly
  • Fundamentals of Machine Learning: KNN, Decision Trees
  • Clustering and Modeling: k-means, DBSCAN, Dimensionality Reduction
  • Analysis of a new dataset and model building

Organizers

Organizers

Partners

Program

Opening & First Session
09:00 – 09:30

Participant check-in & welcome coffee

09:30 – 10:00

Official opening of the school. Welcome remarks from university and partner representatives

10:00 – 12:00

Workshop: Introduction to Data Science – Basics of Python and Pandas

12:00 – 13:00

Lunch break

13:00 – 15:00

Hands-on session: Working with real-world datasets – data cleaning and analysis exercises

Wrap-up

Solution review, task evaluation, and participant feedback delivery

Data Analysis & Visualization
10:00 – 12:00

Workshop: Data Visualization Techniques – Matplotlib, Seaborn, Plotly

12:00 – 13:00

Lunch break

13:00 – 15:00

Hands-on session: Creating charts and analytical dashboards

Wrap-up

Result evaluation, feedback, and distribution of sample solutions

Machine Learning Basics
10:00 – 12:00

Workshop: Classification & Regression – Algorithms: KNN, Decision Trees

12:00 – 13:00

Lunch break

13:00 – 15:00

Hands-on session: Building classification models and evaluating their performance

End of day

Result review, feedback, and grading

Clustering & Modeling
10:00 – 12:00

Workshop: Data Clustering (k-means, DBSCAN), Dimensionality Reduction

12:00 – 13:00

Lunch break

13:00 – 15:00

Hands-on session: User/product segmentation, working with high-dimensional data

End of day

Evaluation and preparation for the final day

Final Day: Assessment & Closing
10:00 – 12:00

Final task: Analyze a new dataset and build a predictive model

12:00 – 13:00

Jury evaluation of results

13:00 – 14:00

Lunch break

14:00 – 14:30

Partner remarks, feedback, and recommendations

14:30 – 15:00

Award ceremony and certificate presentation