Data Science in Python

“Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components:

Data exploration & analysis.

• ncluded here: Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library.

Data visualization

• A pretty self-explanatory name. Taking data and turning it into something colorful.

• Included here: Matplotlib; Seaborn; Datashader; others.

Classical machine learning.

Conceptually, we could define this as any supervised or unsupervised learning task that is not deep learning (see below). Scikit-learn is far-and-away the go-to tool for implementing classification, regression, clustering, and dimensionality reduction, while StatsModels is less actively developed but still has a number of useful features

• Included here: Scikit-Learn, StatsModels.

Deep learning.

This is a subset of machine learning that is seeing a renaissance, and is commonly implemented with Keras, among other libraries. It has seen monumental improvements over the last ~5 years, such as AlexNet in 2012, which was the first design to incorporate consecutive convolutional layers.

• Included here: Keras, TensorFlow, and a whole host of others.