Python 3: Fundamentals

This comprehensive Python course is designed for beginner Python developers eager to build a strong foundation and transition to intermediate Python skills. You'll learn to confidently tackle professional Python projects by understanding the core concepts of this versatile programming language. While no prior coding experience is strictly required, familiarity with basic programming concepts like looping and branching will be beneficial.

This isn't a passive "copy and paste" course. We prioritize understanding why code works, empowering you to apply these techniques to your own unique challenges. Expect a substantial time commitment – mastering Python takes dedication. This course is not a quick overview; it's a deep dive into the language.

Important: Before starting, ensure you have basic proficiency with your operating system's command line (DOS prompt on Windows, shell on Linux/Mac). Familiarity with navigating directories, creating files, and managing files is essential. Numerous online tutorials can quickly cover these fundamentals.

Course Overview: Theory & Practice

We blend Python theory with practical coding exercises. Each module features a lecture explaining a concept, followed by a hands-on coding session applying that knowledge. We highly recommend taking notes and coding along – the interactive nature of video learning is invaluable. Downloadable course slides (over 900 pages!) are available, but taking your own notes, especially after the coding videos, will enhance retention.

We utilize Jupyter notebooks for an optimal learning experience. These notebooks combine Python code with Markdown documentation, providing clear explanations and examples. Each coding video has a corresponding Jupyter notebook available for download, containing the code, detailed explanations, and often, even more insights! All notebooks and data files are accessible in the course downloads and on GitHub.

Exercises at the End of Each Section: Crucially, you'll find exercises with solutions at the end of each section. Master these exercises before moving on to ensure solid understanding. Each section builds directly upon the previous one.

Course Modules:

• Python Basics:

  • What is Python?
  • Python Installation: Learn how to install Python on your system.
  • Virtual Environments: Master creating and using virtual environments for project isolation.
  • Running Python and Jupyter Notebooks
  • Basic Data Types: Integers, floats, booleans.
  • Operators: Boolean, arithmetic, and comparison operators, including operator precedence.
  • Conditional Execution: if, elif, else statements.
  • Looping: for and while loops.
  • Sequence Types: Lists, tuples, and strings.
  • Sequence Manipulation: Iterating, slicing, manipulating, copying, and unpacking sequence types.
  • Strings & Unicode: Advanced string operations and handling Unicode characters.
  • Dictionaries & Sets: Understanding and using dictionaries and sets.
  • List, Dictionary, and Set Comprehensions: Concise ways to create and manipulate data structures.
  • Exceptions & Error Handling: Managing errors gracefully with try, except blocks.
  • Iterables & Iterators: Understanding iterables, iterators, and generators.
  • User-Defined Functions: Defining and calling functions, passing arguments.
  • Lambda Functions: Creating anonymous functions.
  • Built-in Functions: zip, sorted, min, max, round, and more.

• Intermediate Python:

  • Higher-Order Functions: Functions that take functions as arguments or return functions.
  • Maps (Dictionaries): Creating dictionaries using mapping functions.
  • Closures: Understanding and using closures.
  • Advanced Sorting & Filtering: Sorting and filtering data effectively.
  • Decorators: Understanding and creating decorators.
  • File I/O: Reading and writing text files.
  • Modules & Imports: Organizing code with modules and the import system.
  • Dates & Times: Working with dates and times.
  • CSV Files: Reading and writing CSV files.
  • Random Numbers & Sampling: Generating random numbers and sampling data.
  • Python Math & Stats Modules: Exploring mathematical and statistical functions.
  • Decimal Data Type: Handling precise decimal calculations.
  • Object-Oriented Programming (OOP): Creating custom classes.

• 3rd Party Libraries:

  • pytz: Timezone handling and daylight saving time.
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