Week 1/ Intro to Python

 Python is super popular in data science because it’s easy to use, simple to read, and can do a lot of different things. It’s more than just a coding language—it helps both beginners and experts work on big tasks like AI, machine learning, and analyzing large amounts of data. One big reason people love Python is because it comes with lots of helpful tools, like NumPy for math, pandas for organizing data, and scikit-learn for building smart programs. Plus, there’s a big community to help out, and the code looks clean and easy to understand. That’s why so many people start with Python when learning data science.

I personally had zero experience with python and the tools it offers before I joined this class. After reading chapter one, and trying one of the exercises on google collab, I realized it was much easier than I thought it would be using python coding. For the first exercise, I put print ("Hello World") into the code cell, and that was the first exercise I completed using Google Collab. Though, it was a simpler exercise for beginners, it helped my confidence but also made me realize that using Google Collab is similar to cooking. Just as you put whatever ingredients into the dish you are preparing, the same goes for python on Google Collab. What things you put into the system decides what is going to come out just like when you are preparing a dish. 

Overall, the first week taught me that coding, while complex, is easier when you understand and use the tools that are there for you.

# This prints a message

print("Hello, world!")

# Simple function

def greet(name):

    return f"Hello, {name}!"

print(greet("Aidan"))