Basics of Data Science using Python After going through the topic above, what are the questions that come to your mind? It must be, why python? How can we use python to implement data science? And, what are the advantages and disadvantages? We will be answering all these questions and we are also going to talk about the libraries that we can use to implement data science. 1. NumPy NumPy arrays are similar to Python’s built-in list type in some ways, but NumPy arrays provide much more efficient storage and data operations as the arrays grow in size. 2. SciPy It is built on top of the Numpy library, which provides more extensions for finding scientific mathematical formulae such as Matrix Rank, Inverse, polynomial equations, LU Decomposition, and so on. 3. Pandas Pandas is a pillar library in any data science workflow because it allows you to perform data processing, wrangling, and munging. 4. Matplotlib This library is built on NumPy arrays and includes several plots such as line charts, bar charts, histograms, and so on. 5. Scikit Learn Scikit-learn is by far one of the most important Python libraries for machine learning, as it allows you to create machine learning models while also providing utility functions for data preparation, post-model analysis, and evaluation.. 6. TensorFlow TensorFlow is a software library that uses data flow graphs to perform numerical computations. 7. Keras It was created with the goal of allowing for quick experimentation .Keras is a Python library that is widely used for deep learning model training. 8. PyTorch PyTorch takes these tensors and makes it simple to move them to GPUs for faster processing during neural network training.