Data Science
(Python Advanced)
Next start: February 16, 2026
This Data Science course is our “Python Advanced” format – ideal for anyone looking to deepen their existing Python skills. In just eight days, you will learn how to analyse complex datasets, create professional visualisations, and build your first machine learning models. From Pandas and statistical concepts to regression models and geospatial analysis with Folium, you will gain a structured, practice-oriented introduction to advanced Python workflows for data-driven decision-making.
Key facts
Duration:
8 days (in 2 weeks)
Times:
Monday to Thursday
9:00 to 16:00
Language:
English
Level:
Advanced
Location
online (with an option to be onsite)
Costs:
€ 1.250
online
(with onsite option)
*We are exempt from sales tax in accordance with Paragraph § 6 Para. 1 Z 11 lit a UStG. Please see in addition our payment & financing options.
Next course starts
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16.02.2026 - 25.02.2026
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22.06.2026 - 02.07.2026
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12.10.2026 - 22.10.2026
Course description
Our 8-day Python Advanced course in Data Science is designed for participants who want to systematically expand their existing Python skills. Throughout the course, you will learn how to analyse complex datasets, apply statistical methods in a hands-on manner, create meaningful visualisations, and build your first machine learning models. Structured into five clearly defined modules, the course provides the essential tools of modern data analysis – including Pandas, NumPy, Matplotlib, regression models, model optimisation, and working with geospatial data using Folium. By the end, participants will have a solid, practice-oriented foundation for supporting data-driven decision-making and using Python confidently in analytical contexts.
This course has a practical focus. You'll receive theoretical input and then work on examples where you can apply your newly acquired knowledge. Trainers will guide you throughout the day and are always available to offer advice and support.
Requirements & Completion
This course requires motivation, personal responsibility, and a willingness to learn. You need sufficient knowledge of English (a B1 level is recommended) to follow the content.
You will need basic Python knowledge for this course. For this, we recommend taking a closer look at our Python Fundamentals course.
Before the course begins, you should set aside some time to review the materials we provide through our learning platform. Since the course covers a large amount of content in a short period of time, we recommend investing about an hour at the end of each day to look through the material for the following day.
Although this page is checked multiple times, errors may occur. Subject to change.
Course content
Course Content
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Exploring Data with Pandas, NumPy, and Matplotlib
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Foundations of Data Analysis and Statistical Reasoning
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Introduction to Machine Learning for Data Analysis
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Supervised Learning, Model Training, and Optimisation
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Working with Geospatial Data
Learning objectives
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Work confidently with Pandas DataFrames and NumPy arrays
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Create clear and meaningful visualisations using Matplotlib
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Apply core statistical concepts to explore relationships and patterns in data
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Build and evaluate regression-based machine learning models
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Diagnose overfitting and underfitting, and improve models using techniques such as ridge regression and grid search
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Analyse and visualise geospatial data with Folium
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Select appropriate visualisations to communicate insights effectively
