Communicating with AI: Programming in Python 1 2600-ABdz1KAIPIkf

Module 1 - Introduction: Python's work environment and basics
Information about classes, rules for crediting
Introduction to Python and his applications in business
• Why is it worth learning Python? Why learn to program if you don't want to become a programmer?
• Examples: automation, data analysis, decision support
Work environment: Anaconda, Google Colab, Visual Studio Code
• Installation and configuration of Anaconda for local work
• Starting Python in the console
• JUPYTER Notebook - the best invention since the cut bread
• Introduction to Google Colab as an alternative in the cloud, group work, the possibility of using Google server resources
• Visual Studio Code, i.e. you program like a professional;)
• Differences, advantages and limitations of different environments
Python as a smarter calculator
• Basic arithmetic operations
• writing out results
Numbers, text and logical operations
• variables, numbers, simple operations, comments, int, float, string, boolean
• arithmetic and logical operators
Text inscriptions and processing
• Customer names, product categories, opinions
• Formatting strings (F-Strings)

Module 2 - flow control and loops, testing
Conditional instructions and business logic
• IF/ELSE instructions and decision -making rules
• Exercises: Raid Calculator, Rules for granting loans
Functions and multiple use logic
• Function creating
• Division of code into reusable parts - good practices - philosophy do one thing, but good
Installation of Python external libraries
• Using the PIP library manager
• Virtual work environment (Venv)
Testing
• Pytesest Library
• Good practices
Module 3 - work with data collections, loops
Data structures - boards, letters
• Letters - creation, modifying, basic operations
• Append (), Remove (), Len (), Indexing
• iteration through the letters
• Examples
Loops
• Loop for and while
• RANGE function, ITERING through lists
• Examples
Dictionaries and shorts
• Basics of dictionaries
• Creating, access to values, Keys (), Values (), Items () methods
• Short

Module 4 - classes and objects
Object -oriented programming - concept
• Simple introduction (employees, products as objects)
• Classes and objects
• Adding behavior to objects - inheritance and polymorphism
Application
• Use of existing classes
• Creating your own classes and objects, initialization
Code organization, creating your own modules
• Importing built -in and own modules
• Exercises: division of business logic into files
• good code organization practices in files/catalogs
Useful built -in modules
• Datetime, Math, Random, OS, Pathlib, Statistics
Code version (additional topic)

Module 5 - input/output support
Loading data
• Loading data interactive
Working with files
• Reading and saving files
• Error service, exceptions
Reading data from the Internet
• loading the contents of websites
• data processing
• Web Services
Read and save file (CSV, Excel) (2h)
• Import/export of data from spreadsheets
• Exercises: sales report analysis, employee presence list

Module 6 - use of artificial intelligence
Introduction to the SI - large language models (LLM)
• SI - old friend (neural networks, machine learning)
• LLM - (R) Evolution of SI systems
Use to generate code
• Popular models
• Examples of use, how to write effective "prompts", context, tokens, how much it will cost me
The use of SI libraries in your own programs
• Libraries to work with SI, configuration, API keys
• AGDENTS
• Do Androids dream about electric sheep? - model Context Protocol

Module 7 - repetition and final project
Repetition of the material
• Repetition of the most important information, discussion of the completion project
Completion project
• execution of a completion project - all tricks allowed