Reviews 4,2
Reviews 4,5
Python for Data Science
Professionals working with data need a language that effectively combines analysis, visualization, and machine learning. Python has established itself as the standard in this field – versatile, well-documented, and ready for real-world use.
Key topics
- Applying data structures and libraries effectively.
- Performing statistical evaluations and presenting results visually.
- Understanding machine learning concepts and developing first AI models.
- Processing and cleaning large datasets efficiently.
- Writing structured, reusable code for analysis projects.
Prerequisite
Those with basic IT knowledge and an interest in working with data can get started right away – prior experience with Python is not required.
Target group
Suitable for anyone working in data-intensive environments who wants to expand their analytical skills with Python – for example in research, IT, or business intelligence.
The skills learned here make it possible to approach real data projects in a structured way – from data preparation to interpretable visualization.
Course content
Course structure at a glance
- Python fundamentals
- Jupyter Notebooks
- NumPy
- Pandas
- Data import and export
- Excel integration
- CSV processing
- Database connectivity with SQL
- Converting datasets into DataFrames
- Custom functions for data adjustment
- Handling and cleaning missing values
- Aggregating and grouping data
- Creating charts with Matplotlib
- Customizing axes and scales
- Statistical charts with Seaborn
- Creating scatter plots
- Visualizing distributions
- Box plots and outlier analysis
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What does the term Python for Data Science stand for?
- Python for Data Science refers to the targeted use of Python for evaluating, preparing, and presenting data – with the goal of gaining data-driven insights.
What strengths make Python so sought-after in data science?
- Python’s well-structured syntax, wide selection of libraries, and strong community make it one of the most widely used languages in data projects.
What can I actually do with Python in the field of data science?
- From data cleaning and statistical analysis to predictive models and compelling visualizations – Python covers the full spectrum of data-driven work.
Which libraries should I know for data science with Python?
- The most important include Pandas for data manipulation, NumPy for numerical calculations, Matplotlib and Seaborn for visualizations, and Scikit-learn for machine learning models.
Do I need to know Python already to take the course?
- No – prior knowledge of Python is not required. Basic IT skills make it easier to get started, but are not mandatory.
Which industries use Python for data science?
- The course is relevant for professionals in fields such as research, finance, healthcare, IT, marketing, and anywhere data-driven decisions are made.
How does the Python for Data Science course benefit me professionally?
- Participants gain competence in data-driven decision-making, can automate processes, and build a foundation for AI and machine learning projects.
