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Top 10 Packages to learn Data Science in Python
Since last few years, data science is a boom in IT Industry. In the top-notch companies data science plays a very important role in business decisions. Data scientists works with so many languages and they found that the most handy and easy to learn languages are R and Python. If we compare both the languages then Python is more easy and understandable.
The positive points of python language are easy to learn, scalable, quick and has so many inbuilt libraries. As, Python has so many in built libraries which helps so much in data science. So here is the list of top 10 best libraries in python that is used to learn data science:
NumPy:
It is an open source library in Python so it makes the work easy with multi dimensional arrays and matrices. The other advantages of NumPy are that it applies standard mathematical operations on an entire data, easily export data to external libraries which is written in low-level languages i.e. C or C++.
SciPy:
SciPy provides an easy and fastest N-Dimensional array. It provides many routines such as numerical integration and optimization which are very user-friendly and efficient.
Theano:
Theano is one of the highly-rated library in Pyhton for Data Science. this library gives you the permission to define, optimize and evaluate the mathematical expressions efficiently that involves multi-dimensional arrays. For Python users, Theano has a steep learning curve for the framework for declaring variables and building functions.
Pandas:
Pandas is also an open source tool which provides various tools for Python programming that are high-performance, data analysis and easy-to-use data structures. In multiple streams like finance, social sciences, engineering and statistics this can be used to add data structures and tools for practical data analysis. The best thing in Pandas is that it is very easy to adapt and that's why it is one amongst the top Python libraries for Data Science. It works really well with incomplete, messy, unstructured and uncategorized data.
PyBrain:
PyBrain or in elaborated form it is "Python-Based Reinforcement Learning for Artificial Intelligence and Neural Network". PyBrain is an open source package which is used to learn neural networks, reinforcement and unsupervised learning.
Pydot:
To generate complex oriented and non-oriented graphs, Pydot library is used. Pydot is an interface to Graphviz and it is written in pure python. By using Pydot library, you can easily show the structure of graphs which is very much required in building neural networks and decision trees based algorithms.
Statsmodels:
Statsmodels is used to explore data, estimation of statistical models and perform statistical tests in Python. In this library, the list for different types of data such as descriptive statistics, plotting functions, statistical tests and result statistics are available.
SciKit-Learn:
The SciKit-Learn library put main emphasis on using a general purpose high-level language to bring machine learning to non-specialists. But the primary focus of SciKit-Learn is on easy to use, documentation, performance and API consistency. this library is widely used in academic and commercial settings as it comes up with minimal dependencies and easy distribution.
Matplotlib:
This library in Python allows you to make graphs, charts, histogram and other professional grade figures quickly. Matplotlib is the best library for data visualization. In this library, you can export graphics to common vector & graphic formats as it supports various GUI backend on all operating systems.
Gensim:
It is a Python library for topic modelling which is built on NumPy and SciPy.
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