Python is a preferred language for data science and development, particularly due to its extensive libraries like NumPy, SciPy, and Pandas, which facilitate complex data analysis and mathematical functions. Libraries such as Matplotlib and Seaborn enhance data visualization capabilities, while Scikit, Theano, and Tensor offer machine learning support. The document also mentions various training opportunities in data science across different locations.
Introduction to Python as a leading choice for data analysis, emphasizing its large libraries for efficient coding.
Overview of essential Python libraries for data science: Numpy for arrays, Scipy for complex algorithms, Pandas for data manipulation, Ipython for enhanced functionality, and Matplotlib for 2D graphics.
Introduction to key libraries for machine learning, specifically Scikit, Theano, and Tensor.
Key libraries for data mining and natural language processing, including Scrapping, Nltk, and Collaboration.
Overview of libraries for visualization such as Seaborn, Bokeh, Basemap, and Networkx.
Website link for further training information on data science.
WHAT IS PYTHON?
Python is often a choice for development that needs to be applied
for census and data analysis to work, or data scientists whose
work should be integrated into web applications or the
production environment.
In particular, python actually looks at the learning point of the
machine.
The combination of python's teaching and library libraries makes
it particularly suited to develop modern lenses and predecessors
forecasts directly connected to the production process.
3.
CONT..
One ofthe biggest assets in python is the large library.
Libraries are the types and timetables of a particular
language.
Hard libraries can make it easier for developers to
perform complex tasks and not rewrite many code
lines.
NUMPY
It isa basic library of computer science labs, and many of
the libraries in this list using the array numpy as the key and
their basic production.
In short, it presents the arrangement arrows and array array
elements and the power of development to perform
mathematical functions and the matrices of the matrices with
the least number of possible codes.
6.
SCIPY
It isbased on numpy to add a collection of highly
algorithms to judge and provide the information
presented.
This package includes complex multi-tasking operations
to solve different equations, simplification and more.
7.
PANDAS
It integratesdata structures and tools for effective financial
analysis, statistics, social studies and engineering.
pandas works perfectly with incomplete information, unified
and unlabeled (ie, what type of data is most likely to meet in
the real world) and provides reserved, deleted, distributed,
distributed and stereo formatting.
8.
IPYTHON
Increases thefunctionality of python interpreter with a shell that
combines a well-developed image, rich media, sconces,
complete tabs and repetition of the order history.
It also works as a reliable translator for your programs that can
actually be useful for errors. if you have ever used mathematical
or matlab, you should feel comfortable with ipython.
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9.
MATPLOTLIB
It isthe standard python library to create 2d graphics and
graphs. it is a very low level, which means more require
additional instructions to some of the libraries with more
advanced photographs and better numbers.
However, the loose left side. With sufficient commands,
you can make any type of graph you want a mattotlib.
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