True-African/Getting-started-with-Data-Science

In this course we'll learn basics of Python, transition to defining Data science, Statistics and how it is important in preprocessing, Machine learning, Regression # Getting Started with Data Science An introductory Python course covering the foundational programming skills needed for data science. ## Overview This repository contains practice questions and solutions from a pre-lecture notebook, working through core Python concepts step by step. Topics include: - **Basic I/O** — printing strings, string concatenation - **Arithmetic Operators** — `+`, `-`, `*`, `/`, `%`, `**`, `//` - **Data Structures** — lists, tuples, dictionaries - **Control Flow** — `if`/`else` statements, `for` loops - **Functions** — defining and calling custom functions, working with arguments and return values ## Files | File | Description | |------|-------------| | `prelecture_notebook_answer.ipynb` | Jupyter Notebook with practice questions and solutions | | `prelecture_notebook_answer.py` | Python script version of the notebook | ## Getting Started ### Prerequisites - Python 3.x - Google Colab (recommended) or a local Jupyter Notebook environment ### Running Open `prelecture_notebook_answer.ipynb` in Google Colab or Jupyter Notebook and run the cells, or execute the script directly: ```bash python prelecture_notebook_answer.py ``` ## Practice Topics 1. **Strings & Output** — printing and concatenating strings 2. **Arithmetic** — basic math operations, even/odd checks, BMI calculation 3. **Lists, Tuples & Dictionaries** — creating and using Python data structures 4. **Loops & Conditionals** — `for` loops, `if`/`else` branching, finding max values 5. **Functions** — extracting characters, counting keys, reversing lists, string formatting