Python Essential Training: Fundamentals for Software Engineering

Python Essential Training: Fundamentals for Software Engineering

8h 29mBeginner2026-08-25

Authors

Ryan Mitchell

Ryan Mitchell

Senior Software Engineer at GLG

Course details

Explore the fundamentals of Python programming with a focus on real-world application and problem-solving. This practical, hands-on course is designed to help you write, run, and debug Python programs, ensuring you have a solid grasp of the core language constructs and essential topics like using variables, data types, conditionals, loops, functions, and handling data structures such as lists and dictionaries. Learn how to read, understand, and modify Python code effortlessly, and master the art of debugging to resolve common errors in your scripts. Gain the skills to apply Python to simple automation tasks, preparing yourself for more advanced programming challenges.

This course is perfect for beginners, career switchers, and professionals who need a working knowledge of Python for their roles. It's also beneficial for individuals like data analysts, DevOps practitioners, or QA engineers looking to strengthen their Python foundation. By the end of this course, you'll be more equipped to collaborate with engineering teams, automate tasks, and confidently transition to intermediate Python topics.

Learning objectives
Write and run Python programs using core language constructs.
Read, understand, and modify existing Python code.
Use variables, data types, conditionals, loops, and functions effectively.
Debug and resolve common Python errors.
Work with core data structures such as lists and dictionaries.
Apply Python to simple real‑world tasks like scripting and basic automation.

Concepts

Introduction

  • Python - Your modern career advantage

Jumping into Python

  • Running Python in Codespaces
  • Lab - Variables and f-strings
  • Lab - Adding conditional logic with if elif else
  • Lab - Lists, dictionaries, and for loops
  • Lab - Using functions to reuse code
  • Lab - Objects, methods, and imports
  • How to use CoderPad

Data Types

  • Ints, floats, and arithmetic
  • Lab - Cleaning and parsing city names from addresses
  • Lab - Dates and timedeltas in weather records
  • Booleans, truthiness, and filtering

Data Structures

  • Lab - Lists slicing the weather
  • Dictionaries
  • Tuples and unpacking
  • Lab - List comprehensions for data transforms
  • Lab - Dictionary comprehensions
  • Lab - Organizing weather with nested data structures

Control Flow

  • Lab - Iterating in parallel
  • While loops
  • Lab - Break, continue, for else
  • Iterators and generators

Functions

  • Defining and calling functions
  • Lab - Default and keyword arguments
  • args and kwargs
  • Variables and scope
  • Lab - Composing a data pipeline with functions

Classes and Libraries

  • Lab - Using classes
  • Lab - Inheritance and extending classes
  • Lab - Dunder methods
  • Importing and using classes from packages
  • Anaconda, Python, and Pip
  • Virtual environments

Exceptions and Debugging

  • Exceptions and try except
  • Lab - Reading and debugging tracebacks
  • Raising exceptions
  • Finally and else clauses

Reading and Writing Files

  • Reading and writing files with the with statement
  • Files and directories with pathlib
  • Lab - Loading the NOAA dataset from JSON
  • Lab - Writing a CSV weather report

Automating the Web

  • Lab - Making HTTP requests
  • Lab - Handling HTTP errors
  • Calling APIs with GET and POST
  • Lab - Using the OpenAI API

Conclusion

  • Where Python can take you
150,000 Toman