Practical Data Science with Python 3 eBOOK FULL 2022

 Practical Data Science with Python 3 


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.


Post a Comment

Previous Post Next Post