# Laziest Roadmap for Data Science

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 =>  

%[https://youtu.be/v9bOWjwdTlg] 

  

### Numpy

* 1D, 2D Arrays and Methods
    
* Matrices
    
* Tensors
    

Numpy Quick Video Guide =>

%[https://youtu.be/x7ULDYs4X84] 

  

### 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
    

### Gymnasium (OpenAI Gym) => RL Env

### Stable Baselines3 => RL Algorithms