The goal of this course is to familiarize students with the fundamentals of computer programming, foster algorithmic thinking for problem-solving, develop the ability to implement common algorithms using computers, and introduce the basics of structured and engineering-oriented programming. The Python programming language will be used throughout this course to teach and practice these skills.

1. Basic Concepts (1 session)
   • Definitions: algorithm, program, problem-solving
   • Introduction to main computer components
   • Getting started with Python
   • Steps to build and run a program
2. Programming Fundamentals (1 session)
   • Values, variables, and data types
   • Operators and precedence
   • Data type conversion
   • Input and output statements
   • Readable code practices
3. Selection Structure (1 session)
   • Logical expressions
   • Comparison operators
   • `if-else` statement
   • Nested and multiple selections
4. Repetition Structures (1 session)
   • Conditional and counted loops
   • `while` and `for` statements
   • Loop termination with `break` and `continue`
   • Nested loops
5. Functions (1 session)
   • Control flow
   • Parameters and arguments
   • Variable scope
   • Functions with return values
   • Examples of numeric functions
6. Modules (1 session)
   • Modules and how to use them
   • Introduction to turtle graphics
   • Basic drawing commands
   • Creating a custom module
7. Strings (1 session)
   • String operators
   • String comparison
   • String traversal
   • String functions and methods
   • String formatting
8. Lists (1 session)
   • Indexing and slicing
   • Modifying lists
   • List functions and methods
   • Nested lists
9. Files (1 session)
   • Opening text files
   • Reading from files
   • Writing to files
   • Binary files
   • Reading web pages
10. Dictionaries (1 session)
    • How to define dictionaries
    • Applications of dictionaries
    • Counting characters and words
    • Memoization
11. Tuples (1 session)
    • Functions and methods
    • Applications of tuples
    • Dictionary display
    • Storing sparse matrices
12. Text Processing (1 session)
    • Introduction to the `re` module
    • Regular expressions
    • Pattern matching
    • Search and replace in texts
13. Recursive Algorithms (1 session)
    • Recursive functions
    • Flow of recursive calls
    • Solving problems recursively
    • Examples of recursive problems
14. Searching and Sorting (1 session)
    • Linear search
    • Binary search
    • Selection sort
    • Merge sort
15. Object-Oriented Programming (1 session)
    • Introduction to classes and objects
    • Methods and attributes
    • Constructors
    • Class examples
    • Inheritance
    • Operator overloading
16. Web Programming (1 session)
    • Structure of web applications
    • Introduction to the Flask framework
    • Setting up a web server
    • Processing user input
17. Scientific Computing (1 session)
    • Introduction to NumPy and SciPy libraries
    • Arrays and array operators
    • Root finding
    • Optimization
    • Matrices and linear algebra functions
18. Charting (1 session)
    • Introduction to the Matplotlib library
    • Plotting various types of charts
    • Drawing histograms
    • Multi-plot charts

• Programming exercises and project: 10 points
• Final exam: 10 points
• Programming challenges: 1 bonus point

1. C. R. Severance. Python for Everybody: Exploring Data in Python 3. CreateSpace Independent Publishing Platform, 2016.
2. P. Wentworth, J. Elkner, A. B. Downey, C. Meyers. How to Think Like a Computer Scientist: Learning with Python. 3rd Edition, Open Book Project, 2011.