# Popular scientific computing libraries
- pandas : works with dataframe structures (tabular calculations)
- numpy : works with arrays and matrices (vector calculations)
- scipy : works with decision making (calculations of statistics, optimization)
# Popular visualization libraries
- Matplotlib : Common graph and plots (Allows most customization)
- Seaborn : Based on matplotlib, with better look and feel (Allows less customization)
- Plotly : Interactive plot (Used to make dashboards)
# Algorithmic libraries for Machine Learning Tasks for modeling
- Scikit-learn : For regression, classification, clustering etc models (based on numpy, scipy, matplotlib)
- statsmodels : Statistical models (Intuitive, follows mathematical representations)
- keras and tensorflow : advanced modeling techniques involving neural networks.
- Pytorch : tensorflow alternative (Intuitive, follows algebraic equations)
- opencv : for computer vision
- nltk : for natural language processing