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Focusing on the 3 areas programming, data management and machine learning, this course teaches the basics of Python in the context of data science applications.
From installation, the essential object types, and programming basics to the management of projects, the participants first get all the prerequisites to write applications in Python. After a general introduction data is read in and processed to be ready for analysis. Practical examples and the help of example data are making the topic easy to follow and hands on. Here, participants learn the most important techniques in the field of data management. The final program item of the course is the analysis and forecasting of data using machine learning techniques. First, the topic machine learning is theoretically introduced and then applied directly by the use of Python a classification scenario based on a sample data set.
The course focuses on the popular Python libraries numpy and pandas for data management as well as scikit-learn for analysing data using machine learning techniques.
The training addresses people who want to take their first steps in Python to use the language for data analysis. The course is an introductory event. No prior knowledge is required.
- The concept and philosophy of Python
- Python data structures and their properties
- Functions, loops, control structures and object orientation in Python
- Data management with pandas
- Introduction to machine learning
- Model and forecasting with Python and the Scikit-Learn library
- Python (Anaconda-Distribution) in current Version (the development environments 'Jupyter Notebooks' and 'Spyder' should be set up; Python should be entered in the environment variables of the operating system – this can already be done during the installation)
- PyCharm in current version (Community-Version)
ABOUT THE TRAINER
Andreas Wygrabek is a freelance data science expert and experienced trainer in programming and statistics with a career background in an IT consultancy. With his project data-science-architect
he is offering data science services for industry and academic institutions. In his projects he is revealing insights from data through the use of modern algorithms and visualization techniques. The toolset he uses covers the most popular programming languages in the field of data science – R and Python.