Professional Python: Code Quality and Reliability

Description

In the training "Advanced Python for Data Engineers and Data Scientists," you will learn how to harness the true power of Python. You will acquire skills in writing efficient and readable Python code, and learn how to enhance the quality and reliability of your code to apply it across various data analysis, science, and engineering tasks.

Who Should Attend "Advanced Python for Data Engineers and Data Scientists"

The training "Advanced Python for Data Engineers and Data Scientists" is designed for anyone who wants to elevate their Python coding skills to the next level. A key audience includes Data Engineers who wish to support their processes with Python.

A second key group who will greatly benefit are data scientists working in Python who aim to increase the quality and reliability of their code and models. All participants should enjoy hands-on work and be looking for ways to implement high-quality solutions in Python: the Pythonic way of solving problems with a focus on quality and reliability.

Objectives

Over two days, we engage in numerous hands-on Python tasks and cover the following:

• Advanced language constructs in Python, including:
  • Efficient and readable structures
  • Elegant, built-in packages
  • Comprehensions (for lists, sets, and dictionaries)
  • Context managers
  • Decorators
  • Dunder and reflection
• Enhancing quality and reliability with:
  • Documentation using docstrings
  • Type hinting
  • Debugging
  • Linters
  • Unit testing
  • Mocking, patching, fixtures

By the end of the course, you'll be able to implement your Python scripts, modules, and programs more efficiently for all sorts of applications. You’ll also gain knowledge on how to document and test your Python code, significantly enhancing its quality and reliability.

Prerequisites to Attend "Advanced Python for Data Engineers and Data Scientists"

Basic knowledge and some experience with Python are required for this training. If you've regularly written some Python code, you’ll be able to integrate this knowledge powerfully and more quickly grasp case studies.

The training can be taken following Python Fundamentals or Intermediate Python, but this is not mandatory: this course focuses more on writing Python code (programming skills), and thus there is no strict dependency on these prior trainings.

Topics

• Efficient language constructs
• Readable code
• Elegant, built-in packages
• List comprehensions
• Set comprehensions
• Dictionary comprehensions
• Using and implementing context managers
• Using and implementing decorators
• Dunder variables
• Dunder functions
• Reflection
• Docstrings
• Type hinting
• Debugging with an IDE
• Linters
• Unit testing
• Advanced unit testing with mocking, patching, and fixtures

Course Materials

In the training Advanced Python for Data Engineers and Data Scientists, we use materials that we have developed ourselves at Wortell Smart Learning. We will ensure that you receive all the necessary materials in time.