Python Foundations

Python Foundations

Duration: 3 Days

Description

Nearly every AI course in this catalog assumes working Python, and this is the course that gets someone there without wasting time on generic programming tutorials aimed at web developers or hobbyists. It moves through syntax, data structures, functions, and file and library handling with a consistent eye toward how that material actually gets used in AI and data work — list and dict comprehensions before working with datasets, reading structured files before wiring up an API. Participants finish able to read and write practical Python and ready to move directly into the AI- and data-focused courses that assume it.
Class time is divided between instructor-led explanation (35 percent) and hands-on coding exercises (65 percent).

Audience

This course is for anyone heading toward AI, machine learning, or data engineering work who doesn’t yet have working Python — aspiring developers, analysts, and technical professionals making a deliberate move into the field. No prior Python experience is required, though prior experience with any programming language will make the pace feel comfortable rather than brisk.

Objectives

  • Write and run Python programs using core syntax and control flow
  • Work effectively with Python data structures such as lists, dictionaries, and sets
  • Organize code using functions, modules, and clear structure
  • Read from and write to files and handle common data formats
  • Use external libraries and manage packages and environments
  • Apply Pythonic patterns commonly used in data and AI work

Prerequisites

No programming experience is required. The following will help participants get the most from the course:

  • Comfort using a computer and installing software
  • Basic familiarity with working in a text editor

Related AI Courses

See the full AI training roadmap and course directory, or the dedicated Python training roadmap, for how this fits into a broader learning path.

Once you have working Python, natural next steps include AI for Developers for building AI-powered applications, or Machine Learning Foundations for the classical ML and data-science track. Want to work directly with a model’s API next? See Python OpenAI API for Developers.

Course Outline

  • Module 1 – Getting Started with Python
    • Welcome and objectives
    • Installing Python and setting up an environment
    • Running code: scripts, the REPL, and notebooks
    • Variables, types, and basic operations
  • Module 2 – Control Flow
    • Conditionals and boolean logic
    • Loops: for and while
    • Comprehensions
    • Hands-on exercise: solving problems with control flow
  • Module 3 – Core Data Structures
    • Lists and tuples
    • Dictionaries and sets
    • Strings and text processing
    • Choosing the right structure
  • Module 4 – Functions and Organization
    • Defining and calling functions
    • Arguments, return values, and scope
    • Modules and imports
    • Writing readable, reusable code
  • Module 5 – Working with Files and Data
    • Reading and writing files
    • Working with CSV and JSON
    • Error handling and exceptions
    • Hands-on exercise: process a data file
  • Module 6 – Libraries and Environments
    • The standard library
    • Installing packages with pip
    • Virtual environments and dependency management
    • An introduction to key data libraries
  • Module 7 – Pythonic Patterns for Data and AI
    • Iterables, generators, and lazy evaluation
    • Working with structured data
    • Common idioms and best practices
    • Hands-on exercise: a small end-to-end program
  • Module 8 – Course Wrap-Up
    • Key takeaways
    • Resources for continued learning
    • Next steps in the AI and data path