Let's Decode The Cosmos Together 💜

  Image 1: setup of vs code  When people post their Data Science journey online, they usually show beautiful graphs, machine learning models, and polished GitHub repositories. Very few people show the reality. The first week is mostly confusion. You don't struggle with Artificial Intelligence. You struggle with Git. You struggle with Python environments. You struggle with why a library refuses to install after following five different YouTube tutorials. This blog is my complete Week 1 documentation. Not only what I learned, but every command I used, every mistake I made, why those mistakes happened, and how I fixed them. If you're starting Data Science on Ubuntu, especially using VS Code, Jupyter Notebook, GitHub, and virtual environments, this article may save you several hours. My Goal I am documenting my entire journey publicly. Not because I already know Data Science. Because I don't. I want every future researcher or student to see what le...

If you're a physics student interested in cosmology or astrophysics, installing Python is only the beginning. A proper research environment should be clean, organized, and easy to maintain as your projects grow more advanced — and getting this right early will save you hours of frustration later. When I started preparing my Ubuntu laptop for scientific computing, I quickly realized that simply installing Python wasn't enough. I needed tools for numerical computation, symbolic mathematics, data analysis, visualization, and interactive notebooks — all while keeping project dependencies isolated. T his guide documents the exact setup I used to build my Python research environment for cosmology and astrophysics work. If you're just starting out, you can follow along step by step. Why Ubuntu for Scientific Computing? Ubuntu is one of the most widely used operating systems in research and scientific computing. It offers a stable environment, powerful terminal tools, e...