Master the essential tools and techniques of data science with Python. This hands-on, four-day course gives you the practical skills needed to analyze data, build machine learning models, and create powerful visualizations using Python’s leading libraries—Pandas, NumPy, Matplotlib, and Scikit-learn. Whether you're just starting out or looking to sharpen your expertise, this course gives you a strong technical foundation and the experience to confidently work with real-world data.
Python and Data Science
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Python and Data Science
Data science is a fast-evolving field that combines statistics, programming, and domain knowledge to uncover insights from large and often complex datasets. It helps us recognize patterns, make predictions, and support smarter decision-making. With more companies relying on digital tools to collect and process data, professionals who can make sense of that data are in high demand across many industries.
Python has become the top language for data science—and for good reason. Its clear syntax and wide range of open-source libraries make it accessible, yet powerful enough for complex tasks like data manipulation, statistical analysis, and machine learning. It’s ideal for automating repetitive processes, building predictive models, and working with everything from simple spreadsheets to vast datasets.
This course is designed to help you gain both confidence and competence with Python and its most useful tools for data science. Through a mix of hands-on exercises and instructor-led sessions, you’ll learn to write clean code, explore and visualize data, and apply machine learning techniques to real challenges. From writing your first script to building models that can make predictions, this course prepares you to use Python effectively in a wide range of professional settings.
What will you learn
The Python and Data Science Course takes you from the basics of Python programming to applying it in real data science workflows. You’ll start by learning Python syntax, how to write functions, use conditional logic, and structure code using classes and modules. Along the way, you’ll use Jupyter Notebooks—an interactive coding environment that makes it easy to test ideas and document your work—and set up your workspace using Anaconda to keep everything organized and consistent.
Once the foundation is in place, you’ll dive into NumPy to handle numerical data and perform high-speed calculations. You’ll work with arrays, apply mathematical operations, and manipulate data in ways that are essential for efficient analysis. Then, using Matplotlib, you’ll learn to create visualizations like line plots, bar charts, and scatter plots to present your data clearly and effectively.
Next, you’ll work with Pandas—one of the most powerful data analysis libraries available. You’ll learn how to load and clean messy data, filter and group values, and perform summary statistics and exploratory data analysis. With Pandas, you’ll be able to quickly make sense of complex datasets and prepare them for further analysis or modeling.
On the final day, you’ll be introduced to machine learning with Scikit-learn. You’ll practice loading datasets, choosing and training models, making predictions, and evaluating results. Topics like classification, regression, and clustering will be covered through real-world examples. You’ll also learn about model selection and performance tuning so you can make informed decisions about how to improve your models.
Why choose this course
The Python and Data Science Course at Geo-ICT offers a structured, practice-focused learning experience that helps you build in-demand skills while working on real data.
- Balanced approach: Combines essential theory with practical exercises, so you can understand concepts and immediately apply them.
- Modern tools: Learn with today’s most widely used libraries—NumPy, Pandas, Matplotlib, and Scikit-learn.
- Expert instruction: Taught by professionals with experience in Python programming, data analysis, and geo-information.
- Job-ready skills: Everything you learn is directly applicable to common tasks in analysis, automation, and reporting.
Course structure
Day 1
You’ll begin by learning the core elements of Python, including variables, data types, functions, control flow, comprehensions, classes, and modules. You’ll also explore Jupyter Notebooks and the IPython shell, and learn how to manage libraries and environments using Anaconda.
Day 2
You’ll focus on NumPy and learn how to work with multi-dimensional arrays. You’ll explore array manipulation techniques and apply sorting, searching, and comparison operations. You’ll also begin working with Matplotlib to visualize data and create plots that support your analysis.
Day 3
This day is dedicated to mastering data analysis with Pandas. You’ll use DataFrames and Series to load, clean, filter, and transform data. Through practical exercises, you’ll prepare and analyze real datasets, giving you the tools to handle a wide range of analysis tasks with confidence.
Day 4
The final day introduces SciPy and Scikit-learn. You’ll practice building machine learning models using built-in datasets, and learn how to classify, predict, and group data. You’ll also explore techniques to evaluate and improve model performance. The course wraps up with tips on best practices and using these tools in real-world workflows.
Learning Outcomes
- Understand the fundamentals of Python and how to use it for data analysis
- Collect, clean, and prepare datasets for analysis
- Apply statistical techniques and workflows using NumPy, Pandas, and SciPy
- Build, train, and evaluate machine learning models with Scikit-learn
- Create clear, insightful visualizations with Matplotlib and Seaborn
- Use Jupyter Notebooks and Anaconda to manage your development environment
- Apply your skills to real-world challenges in sectors such as business, research, and technology
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
Frequently Asked Questions about Python and Data Science
You will learn to work with Python packages such as NumPy, SciPy and Pandas, which are essential for data science applications.
The course lasts 4 days.
The course costs €1999 excluding VAT.
Yes, the course can be taken online via Google Meet.
The course is suitable for both beginners and advanced students in Python and data science.
Yes, you can ask questions to the teacher via email up to two weeks after the course.
Yes, there is a discount of 10% for 3 students and 15% from 4 students.
On the first day, Python syntax aspects, important for data science projects, are discussed.
Yes, the course includes practical exercises with Python and data analysis tools.
Yes, you will receive a certificate of participation upon completion of the course.