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Introduction to Python

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Course description

This course provides a practical introduction to the Python programming language, designed for researchers who are new to Python but may have some prior programming experience. The focus is on mastering the fundamental skills needed to handle, analyze, and visualize data. You will learn the core syntax and logic of Python, with an emphasis on powerful data science libraries like Pandas and NumPy.

 

Target Audience

Researchers who want to learn Python for data analysis. This course is ideal for those with little to no Python experience who wish to build a strong foundation for more advanced computational work. Mastering these fundamentals opens the door to more advanced topics in data science and machine learning.

 

Topics

The course covers the foundational building blocks for data analysis in Python:

  • Python Fundamentals: Core concepts like variables, data types, and operators.
  • Data Structures: Working with essential structures like lists, dictionaries, and NumPy arrays.
  • Control Flow: Using loops (for) and writing custom functions to automate tasks.
  • Data Analysis with Pandas: Importing, filtering, and performing descriptive statistics on data frames.
  • Data Visualization with Seaborn: Creating essential plots like scatterplots and histograms to explore data.

 

Methods

The course is highly interactive and follows a "learning-by-doing" approach. Each new programming concept is immediately applied in hands-on coding exercises, with best-practice solutions provided to guide your learning.

 

Learning Goals

At the end of this course, you will be able to:

  1. Apply Python's fundamental syntax, including variables, data types, and functions.
  2. Use core data structures like lists and NumPy arrays to store and manage data.
  3. Write loops and functions to automate repetitive analytical tasks.
  4. Use the Pandas library to import, filter, and perform basic analysis on tabular data.
  5. Create simple data visualizations to explore and summarize datasets using Seaborn.

 

Prerequisites

No prior Python knowledge is required. However, basic experience with another programming language (e.g., R, MATLAB) is helpful.

 

Format

  • Duration: 3 days
  • Language: English
  • This course will be offered either on campus (in person), or online.
  • For online courses we use the software Zoom.

 

Dates and Application

  • Courses provided for Helmholtz Munich:
    • You can check the current dates and whether the courses are already fully booked here*.
    • Please read the corresponding FAQ* before applying via the forms of the HR Development department*.

 * Links marked with * are only available for Helmholtz Munich staff.