Laziest Roadmap for Data Science
This article is incomplete, will be updated soon.
I am a nerd who has used JS, TS, React.js, Redux ToolKit, Express.js, MongoDB, Node.js, PostgreSQL, Next.js, VITE, Postman, Docker, Linux, Bash, Python, and Langchain.
This is why the line "JACK OF ALL, MASTER OF NONE" fits perfectly. I am sharing my experience with these techs in blogs. starting with JS...
DS Parts:
Data Cleaning & Preprocessing || Pandas
Data Visualization || Matplotlib
Data Analysis
Modeling (Machine Learning) || ScikitLearn/SciPy
Deep Learning || PyTorch
Python
Lists
Sets
Tuple
Functions
Python’s built-in Data Structures and methods are not optimized for working with huge data, and also performance issues there, so we need some data structures that are performant and optimized for huge Data Operations. And Numpy simply does that.
Python One Shot, Learn only the listed Topic, other things, you can learn on the go =>
Numpy
1D, 2D Arrays and Methods
Matrices
Tensors
Numpy Quick Video Guide =>
Pandas || Data Loading, Cleaning, Preprocessing
Matplotlib || Data Visualization Toolkit
ScikitLearn/Scipy || ML
Linear regression
SVM
Trees, Random Forest
k-Means, PCA, etc.
Pipelines, preprocessing
PyTorch/TensorFlow || Deep Learning
Neural networks
Backprop
GPU acceleration
CNNs, RNNs, Transformers