Page 1 of 3

Prague Data School

Prague 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.

Location: Prague, DXC Technology, Pikrtova 1737/1a Date: 9. - 13. February 2026 Duration: 5 full-time days (45 hours), 8:30 AM - 5:30 PM

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

The bootcamp is conducted in Slovak/Czech language.

Students are eligible to apply for a Scholarship, more details in the Pricing section.

Who is the course best for?

Analysts who want to transition from Excel to larger datasets, learn to model data and create visualisations.

PhD students and Researchers who work with data in their research and need to use statistics and regression models.

Professionals from various fields who want to gain practical knowledge in the area of data science, AI and statistics.

Students keen to learn the basics of data processing and AI.

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.

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.

Why Codebridge College? We understand that learning data science skills isn’t easy. It requires a new way of thinking, patience and the right support. At Codebridge College, we know how overwhelming it can feel to switch from Excel to Python, or from raw tables to statistical models and AI. That’s why our bootcamps are designed to be practical, clear and guided by mentors who walk you through every step. Discussion is encouraged and no question is left unanswered.

Participants consistently describe our courses as the most practical training they’ve attended and with new skills they can apply immediately. -> Feedback on our bootcamps

Graduate Profile

After completing the Prague 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 LLM into your python project pipeline

Lecturers

Imrich Berta A graduate of Applied Mathematics at the University of Cambridge, with experience in machine learning models for disease prediction and clinical data analysis. He currently works as a consultant for government institutions and start-ups. He actively mentors analysts and organises programming and data workshops for students. Imrich enjoys helping people who do not consider themselves "math types" understand and apply mathematical and statistical principles in practice.

Laura Johanesova A bioinformatician studying at the University of Vienna. Skills in shell scripting, R and Python are crucial for her research in regeneration. She designs and leads intensive, practically oriented training sessions that help analysts transition from Excel to Python or R and applied machine learning. Laura's work includes the creation of educational programmes, the development of data skills in science and various projects in the field of healthcare and biomedical data.

Price

Private sector 28 000 CZK | Public sector 24 000 CZK | Academia 20 000 CZK

Prices are without VAT.

Contact

Laura Johanesova [email protected]