Python for Data Analytics Path
From Python basics to advanced machine learning, your complete guide to becoming a data analyst.
1. Intro to Python for Data Analytics
Python's simple syntax and vast ecosystem of powerful libraries (like NumPy, Pandas, and Matplotlib) have made it the go-to language for data analysis, machine learning, and data science.
2. Setup & Jupyter Notebooks
The best way to start is by installing the Anaconda distribution, which bundles Python and all essential data science libraries. You will primarily work in Jupyter Notebooks, an interactive environment perfect for exploring data and documenting your analysis.
3. Variables & Basic Data Types
Learn the fundamentals of Python variables and the core data types you'll use constantly: integers (`int`), floating-point numbers (`float`), strings (`str`), and booleans (`bool`).
4. Python Data Structures
Master Python's built-in collection types. `Lists` for ordered, mutable data. `Tuples` for ordered, immutable data. And `Dictionaries` for unordered key-value pairs, which are essential for working with structured data like JSON.
5. Control Flow
Guide your program's logic using conditional statements (`if`, `elif`, `else`) to make decisions and loops (`for`, `while`) to iterate over data, a fundamental task in data analysis.
6. Functions
Write reusable blocks of code with functions (`def`). This is key to creating clean, modular, and maintainable analysis scripts. Learn to define functions that accept arguments and return results.
7. Modules & Pip
Extend Python's capabilities by importing modules from its extensive standard library and third-party packages. Learn to manage these packages using `pip`, the Python Package Installer.
8. Reading CSV Files
Data often comes in text files like CSV (Comma-Separated Values). Learn how to use Python's built-in `csv` module to read data from these files line by line, setting the stage for using Pandas.
9. Exception Handling
Real-world data is messy. Use `try...except` blocks to handle potential errors gracefully, such as when a file is not found or data is in an unexpected format, ensuring your scripts don't crash.
10. Lambda Functions
Lambda functions are small, anonymous functions defined with the `lambda` keyword. They are particularly useful in data analysis for creating quick, one-off functions to use with libraries like Pandas.