OpenLearn Cohort 1.0 | W0D1 : Python Setup/Kaggle basics and Working

ACHINTYA SHARMA

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Jun 15, 2025

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Welcome back learner, to OpenLearn Cohort Week 0 — Day 1. Here we’ll go through the brief steps to be followed to install VSCode, setup python and jupyter notebooks. Along with that we will also look into Kaggle Basics and workflow options.

Step 1: Installing Visual Studio Code (VSCode)

VSCode is like a super-powered text editor that makes coding much easier. Think of it as Microsoft Word, but designed specifically for writing code.

Installation Steps:

1. Go to code.visualstudio.com
2. Click the big download button (it automatically detects your operating system)
3. Run the installer and follow the prompts
4. Once installed, open VSCode

Essential Extensions to Install:

Using the extensions tab in VSCode ( Ctrl + Shift + X)

• Python Extension: Adds Python support to VSCode
• Jupyter: Lets you work with Jupyter notebooks directly in VSCode
• Python Debugger: Helps you find and fix errors in your code

To install extensions: Click the Extensions icon in the left sidebar (looks like building blocks), search for the extension name, and click “Install.”

Step 2: Setting Up Python(windows)

1. Go to python.org
2. Download the latest Python version (3.11+ recommended)
3. Important : Check “Add Python to PATH” during installation
4. Run the installer

For Mac(Rich kids)

• Go to python.org and download Python
• Or use Homebrew: brew install python3

Verify Installation:

Open your terminal (Command Prompt on Windows, Terminal on Mac/Linux) and type:

python --version

You should see something like “Python 3.13.x” , thus python has been successfully setup on your device.

Installing Essential Packages:

Python packages are like apps for your phone — they add extra functionality. Install these essential ones by typing the following command in your Command Prompt on Windows, Terminal on Mac:

pip install pandas numpy matplotlib seaborn jupyter scikit-learn

If this shows an error, try using :

python -m pip install pandas numpy matplotlib seaborn jupyter scikit-learn

Step 3: Setting Up Jupyter Notebooks

Jupyter notebooks are interactive documents where you can write code, see results immediately, and add explanations. Think of them as a mix between a Word document and a calculator.

Using Jupyter in VSCode:

VSCode can also run Jupyter notebooks directly (which otherswise are run with the help of Command Prompt):

1. Create a new file with .ipynb extension
2. VSCode will automatically recognize it as a Jupyter notebook
3. Start coding in cells

Basic Jupyter Tips:

• Shift + Enter: Run current cell and move to next
• Ctrl + Enter: Run current cell and stay in same cell
• A: Add cell above
• B: Add cell below
• DD: Delete cell (press D twice)

Troubleshooting Common Issues

Python Not Found:

• Make sure you checked “Add to PATH” during installation of python
• Restart your terminal after installing
• Try python3 instead of python

Jupyter Won’t Start:

• Make sure you installed it : pip install jupyter
• Or try if it shows an error : python -m pip install jupyter

Step 4: Kaggle Basics and Setup

1. Go to kaggle.com
2. Sign up with your email or Google account
3. Complete your profile (add a photo, bio, etc.)
4. Verify your phone number (required for competitions)

Why Kaggle?

1. Active Discussion Community — Kaggle’s forums are filled with experienced professionals and researchers who actively engage in discussions, answer questions, and share insights. It’s a great place to learn from real-world experts.
2. Diverse Datasets — From beginner-friendly datasets to complex real-world data, Kaggle’s dataset repository is extensive, covering everything from finance to healthcare.
3. Competitions & Real-World Challenges — Kaggle regularly hosts competitions where participants solve practical machine learning problems and even get chances to win prizes. It’s a fantastic way to test and improve your skills.
4. Kernels & Notebooks — Kaggle allows users to write, run, and share Jupyter notebooks directly on the platform with pre-installed libraries, making collaboration seamless.

The sweet spot? Free GPUs & TPUs!! — Kaggle provides free access to high-performance GPUs and TPUs, making it an incredible resource, you could literally play around with the available hardware to perform complex calculations, or even if you simply want to run a really long For loop.

Wrapping Up⚡

And with that, ladies and gentlemen, you’ve successfully set up everything you need to embark on this exciting journey of continuous learning. The tools are in place, the possibilities are endless, and the adventure awaits! Keep exploring, keep growing, and embrace the ever-evolving world of knowledge!

Got stuck somewhere? Don’t worry! Drop a comment below or reach out to the OpenLearn community. We’re all here to help each other succeed.

Previous & Next

1. previous : The Purpose of Machine Learning
2. next : Data Analysis Refresher: NumPy, Pandas, EDA