Computer Programming

Course outline:

1. Computational Thinking.
2. Introduction to Computers and Programming
   1. What is a computer? , Programming Languages: High-level vs. low-level languages Introduction to popular programming languages (e.g., Python, C, Java)
3. How Computers Understand Programs:
   1. Compilation vs. interpretation, Introduction to an Integrated Development Environment (IDE), Basic Algorithms: Introduction to algorithms and their role in solving problems.
4. Basic Programming Concepts (Language of preferred choice)
   1. Syntax and Semantics: Basic structure of a program, Understanding comments and documentation, Variables and Data Types: Primitive data types (int, float, char, boolean, string), Declaring and initializing variables, Operators: Arithmetic, relational, logical, and assignment operators Input/output: Reading input from the user, Displaying production to the screen
5. Control Flow Statements
   1. Conditional Statements, Loops,
6. Functions and Modular Programming
   1. Defining Functions, Scope and Lifetime of Variables, Recursion: Introduction to recursive functions
7. Arrays and Data Types
8. Introduction to Data Science
   1. Overview of data science and its applications, Role of programming in data analysis
9. Data Types and Structures:
   1. Data Science Workflow: Data collection, cleaning, analysis, visualization, and interpretation
10. Working with Data using Python/preferred language
    1. Basic Libraries for Data Science: NumPy for numerical computations, Pandas for data manipulation,
    Data Loading and Preprocessing: Importing data from CSV or Excel files, Handling missing data, Basic data cleaning and transformations,
    1. Exploratory Data Analysis (EDA):
    Descriptive statistics (mean, median, mode, standard deviation), Data summarization using Pandas
    1. Data Visualization: Introduction to Matplotlib and Seaborn, Plotting basic graphs (line, bar, histogram, scatter)
11. Object-Oriented Programming (OOP) Basics (if applicable to the language)
    1. Introduction to OOP: Concepts of classes and objects, attributes, and methods: Defining and accessing attributes and methods
12. Error Handling and Debugging
    1. Types of Errors, Debugging Techniques, Exception Handling
13. Introduction to Algorithms and Problem Solving
    1. Basic Algorithmic Techniques, Time and Space Complexity (Basic introduction), Problem Solving Techniques

Programming Language: Python (preferred for data science beginners)

Libraries:

NumPy: For array operations and numerical computations

Pandas: For data manipulation and analysis

Matplotlib/Seaborn: For data visualization

Datasets:

Open datasets from Kaggle, UCI Machine Learning Repository, or other public sources.

IDEs:

Jupyter Notebook: Ideal for combining code and visualizations.

Google Colab: Cloud-based Jupyter Notebook.