Data Science with Python (Certified)
7 April, 2026 – Amsterdam, The Netherlands
Do you know how to perform basic data analysis in Python but want to learn more about machine learning? During this three-day data science deep dive, you will learn how to unlock Python’s potential for data analysis and machine learning. The course covers training models with scikit-learn and best practices for transforming your data with pandas, with a perfect combination of theory and practice. After passing the exam at the end of the course, you’ll be able to call yourself a certified data scientist!
Looking to upskill your team(s) or organization?
Rozaliia will gladly help you further with custom training solutions.
Rozaliia Khafizova
Data and AI Training Advisor
Duration
3 days
Time
09:00 – 17:00 (GMT +2:00)
Language
English
Lunch
Included
Certification
Yes
Level
Foundation
What will you learn?
After the training, you will be able to:
Perform exploratory data analysis on your datasets with pandas.
Train and evaluate machine learning models with scikit-learn.
Identify a suitable machine learning algorithm and metric for your data problem.
Prepare complex data for machine learning with techniques such as scaling, encoding, and imputing.
Apply best practices for data wrangling and model building.
Program
Master exploratory data analysis with Pandas
- Fetch descriptive summary statistics of your data with simple operations
- Effectively select and filter parts of your data with loc
- Retrieve advanced statistics with group by aggregations
- Extend your dataset by creating new columns with assign
- Structure your code neatly by chaining methods
Introduction to machine learning
- Identify the type of machine learning task (classification or regression, supervised or unsupervised, and others)
- Use scikit-learn to train a classification model
- Evaluate your model’s effectiveness with various metrics (such as precision & recall, F1, root mean squared error, r2)
This training is for you if:
You have some basic experience with Python (or any other programming language)
You want to get certified in data science
You would like to apply best practices to your data science projects
You hope to better communicate and collaborate with your data science colleagues
This training is not for you if:
You’ve never worked with Python or any other programming language before (check out our Python for Data Analysis training instead)
You are already an experienced data scientist and you want to develop your skills further (check out our Advanced Data Science training and other specialized topics instead)
You think data science is all hype and can’t add value (check out our Analytics Translation training instead).
You are not interested in practical applications, only the math behind the algorithms
Why should I
follow this training?
Dive into the world of Data Science and Machine Learning using Python.
The course combines theory and practice perfectly, making it a great introduction to the field.
Get best practices for data wrangling and model building.
What else
should I know?
After registering for this training, you will receive a confirmation email with practical information. A week before the training, we will ask you about any dietary requirements and share literature if you need to prepare.
See you soon!
Course information
For this training, you need a laptop on which you can install programs.
All literature and course materials are included in the price.
Meet the trainers
Lucy Sheppard
Meet Lucy Sheppard, trainer at Xebia Data. Lucy is a data science trainer and teaches many Python courses as well as dbt Learn.
Lysanne van Beek
Lysanne is a Data Science Educator at Xebia Data and loves making courses as accessible as possible. With roughly 2,000 hours of training experience and almost 1,500 students taught, she’ll make sure you get the most out of your training.
Juan Venegas
I help companies tell their stories with data. I am rigorous with the data and engaging with my stories. I believe that a good technical analysis can also find a good way to communicate it.
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