Summer Data School

Summer Data School is an intensive 5-day course focused on working with data, statistics and artificial intelligence using the Python language. Participants will learn to clean, visualise and analyse real-world data in an interactive workshop environment. The course is perfect for anyone transitioning from Excel to Python.

Prague | 22 - 26 June 2026

Bratislava | TBA July 2026

Duration: 5 full-time days (45 hours), 8:30 AM - 5:30 PM

Prerequisites: basic knowledge of Excel is required. Programming knowledge is not required.

The bootcamp is conducted in Czech / Slovak language.
Students are eligible to apply for a Scholarship (more details in the Pricing section).

TARGET AUDIENCE

1

PhD students who need to handle data, use statistics or build models in their research.

2

Analysts who want to swing away from Excel and start working with big data, applying machine learning and create eloquent visualisations.

3

Professionals wanting to learn more about machine learning and statistics.

COURSE STRUCTURE

Day 1: Introduction to Python and Data Processing

  • Fundamentals of programming in Python
  • Working with data in Pandas
  • Data cleaning, filtering and transforming
  • Manipulating dates and strings
  • Groupby and aggregations, multi-index
  • Long and wide data formats
  • Data import and export, linking with Excel

Day 2: Data Visualisation

  • Fundamentals of data visualisation in Python
  • Matplotlib, Seaborn, and Plotly libraries
  • Groupby + aggregations and their visualisation
  • Interactive charts and dashboard elements
  • Geographical and multivariate charts
  • Multivariate plots
  • Principles of data communication

Day 3: Statistics and Regression Modelling

  • Fundamentals of statistics and probability
  • Statistical hypothesis testing
  • Interpretation of stat results and p-values
  • Linear and logistic regression
  • Metrics of predictive power
  • Correlation, causality and randomisation
  • Natural experiments
  • Statistics vs. machine learning

Day 4: Machine Learning

  • AI from the ground up: concepts, types and uses
  • Training machine learning models
  • Classification and regression models
  • Sensitivity, specificity, ROC curve
  • Interpretation of ML model decision-making
  • Neural networks and deep learning
  • Unsupervised learning, t-SNE
  • Integration of LLMs into Python projects
  • Extraction of structured data from text

Day 5: Data Hackathon!

In cooperation with our partners, we have prepared challenging data tasks using data from the healthcare and education sectors. The goal of the hackathon is for every participant to utilise their new data and programming skills directly in practice and at the same time, learn something new about important social topics.
Several teams already came up with interesting findings in both tasks, which provided new insights for stakeholders.

MORE INFORMATION

Required knowledge

Basic quantitative skills
No programming skills needed

Familiarity with computer programming or database structures is a benefit, but not a requirement. Summer Data School is set up in a way that beginners will learn the basics and do some hands-on experimentation with guidance, while participants with some experience will be able to see best practices, utilise their knowledge and do some additional magic on really cool datasets while consulting with experienced mentors!

WHY PYTHON?

Python is considered the most suitable language for data science and machine learning today. It is clear, intuitive, and ideal even for beginners. Already on the first day, you will learn its fundamentals and see how to solve real data tasks with just a few lines of code.

GRADUATE PROFILE

After completing the Summer Data School you will be able to:

Work with real-world data

  • Clean, transform and explore datasets using Python
  • Move beyond Excel and work efficiently with larger, messy data
  • Use pandas to filter, join, reshape and summarise information
  • Document your work in clear, reproducible notebooks

Create high quality visualisations

  • Build clear charts using Matplotlib, Seaborn and Plotly
  • Choose the right type of plot for the story you want to show
  • Design clean, readable figures for presentations and reports
  • Communicate insights with confidence

Apply statistical models in practice

  • Run and interpret common statistical tests (parametric and non-parametric)
  • Understand distributions, variance and sampling
  • Fit regression models and understand their output
  • Explain results in a way that makes sense for non-experts

Build and evaluate machine-learning models

  • Prepare data for ML (splits, encoding, scaling, feature selection)
  • Train, evaluate and explain models
  • Understand accuracy, precision, recall, ROC curves and overfitting
  • Choose the right model for classification or regression tasks

Use AI and LLM tools in data workflows

  • Integrate AI tools into your daily work to speed up coding, cleaning and documentation
  • Use LLMs to explain code, detect errors and improve clarity
  • Extract information and structured data from unstructured text documents
  • Integrate LLMs into your Python project pipeline

JOIN OUR NEW CROSS-DISCIPLINARY DATA SCIENCE COMMUNITY

By attending Summer Data School, you will gain lifelong access to our data science community. We support our members online and in person by organising monthly events such as discussions, short lectures and workshops, and data challenge evenings. Participants of our previous Data Schools found work opportunities and research collaborations within our community. To always improve your learning and “people” experience, we listen closely to your feedback and try to accommodate what the members want in our Data Schools and events. Join us and gain access to a unique resource for learning data science!

PRIVATE SECTOR

Price: 1100 EUR | 26 500 CZK

PUBLIC SECTOR

Price: 950 EUR | 23 000 CZK

ACADEMIA

Price: 800 EUR | 19 500 CZK

Scholarships available for students!

Thanks to our partners, we are able to provide scholarships to students and PhDs. This scholarship covers from 50 % up to 100% of the costs of Summer Data School. If you wish to apply for the scholarship, please fill out the sign-up form accordingly and write a short (500 words maximum) motivation statement - the scholarship will be awarded based on this statement and registration date (first come first serve).

Our scholarships are intended to support those who (or their research groups in the case of PhDs) cannot otherwise afford the entry to Summer Data School. If a participant applies and is not granted the scholarship, they will be offered to buy the entry, however this is subject to availability of places, which may be exhausted.

  • 5 days of lectures and workshops by industry leaders
  • Exercises and mentoring
  • Carefully prepared curriculum
  • Presentations from external speakers
  • Networking opportunities
  • Knowledge exchange
  • Membership in the new data science community with monthly events

TEAM

Imrich Berta

Applied mathematics graduate from University of Cambridge, experienced in machine learning models for disease prediction. Currently works as a consultant for government on cancer epidemiology and public health. Actively mentors analysts and organizes coding workshops for students.

LinkedIn →

Laura Johanesová

Laura is a bioinformatician and biomedical scientist currently at the University of Vienna. The skills she has in R, Linux and Python are crucial for her research in regeneration and she also developed interest in biotechnology and medicine, which helped her team win the first place in a biotech incubator.

LinkedIn →

Ján Dudek

Magna cum laude economics and econometrics graduate from Rice and Oxford. Improved the risk-equalization model at the Ministry of Health and implemented ML fraud detection algorithms in Slovak healthcare. Currently a senior data scientist specializing in the health insurance industry.

LinkedIn →

PARTNERS

COMMUNITY PARTNERS