Practical Data Science with Python 3
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Python Data Science Educational Programs:
The term "data science" is as broad as it comes. It may be easier to describe what it is by listing its most specific components:
Data mining and analysis :
Include here: panda; NumPy SciPy Help hand from Python Standard Library.
Data visualization. A nice name explains itself. Take the data and turn it into something colorful.
Includes here: Matplotlib; Seaborn; Datashader others.
Classic machine learning. Conceptually, we can define this as any supervised or unsupervised educational task rather than deep education (see below). Scikit-learn is the remote tool for implementing classification, regression, assembly, and dimensional limiting, while StatsModels is less sophisticated 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 done with Keras, among other libraries. It has seen huge improvements in the last 5 years, such as AlexNet in 2012, which was the first design to include cascading layers.
Included here: Keras, TensorFlow and a host of others.
Data storage and megadata frameworks. Megadata is best defined as data that is too large to be stored on a single device or cannot be processed in the absence of a distributed environment. Python's associations with Apache technologies play strongly here.
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