Top 10 Python Libraries For Data Science

June 5, 2019
Data Science Course

 

With the advent of digitization, the business space has been critically revolutionized and with the introduction of data analytics, it has become easier to tap prospects and convert them by understanding their psychology by the insights derived from the same. In today’s scenario, Python language has proven to be the big boon for developers in order to create websites, applications as well as computer games. Also, with its 137000 libraries, it has helped greatly in the world of data analysis where the business platforms ardently require relevant information derived from big data that can prove conducive for critical decision making.

Let us discuss some important names of Python Libraries that can greatly benefit the data analytics space.

Theono

Theono is similar to Tensorflow that helps data scientists in performing multi-dimensional arrays relevant to computing operations. With Theono you can optimize, express and array enabled mathematical operations. It is popular amongst data scientists because of its C code generator that helps in faster evaluation.

NumPy

NumPy is undoubtedly one of the first choices amongst data scientists who are well informed about the technologies and work with data-oriented stuff. It comes with a registered BSD license and it is useful for performing scientific computations. It can also be used as a multi-dimensional container that can treat generic data. If you are at a nascent stage of data science, then it is key for you to have a good comprehension of NumPy in order to process real-world data sets.

Keras

One of the most powerful libraries on the list that allows high-level neural networks APIs for integration is Keras. It was primarily created to help with the growing challenges in complex research, thus helping to compute faster. Keras is one of the best options if you use deep learning libraries in your work. It creates a user-friendly environment to reduce efforts in cognitive load with facile API’s giving the results we want.

SciPy

A number of people get confused between SciPy stack and library. SciPy is widely preferred by data scientists, researchers, and developers as it provides statistics, integration, optimization and linear algebra packages for computation.

NLKT

NLKT is basically national language tool kit. And as its name suggests, it is very useful for accomplishing national language tasks. With its help, you can perform operations like text tagging, stemming, classifications, regression, tokenization, corpus tree creation, name entities recognition, semantic reasoning, and various other complex AI tasks.

Tensorflow

Tensorflow is an open source library designed by Google that helps in computing data low graphs with empowered machine learning algorithms. It was created to cater to the high demand for training neural networks work. It is known for its high performance and flexible architecture deployment for all GPUs, CPUs, and TPUs.

Bokeh

Bokeh is a visualization library for designing that helps in designing interactive plots. It is developed on Matplotib and supports interactive designs in the web browser. 

Plotly

Plotly is one of the most popular and talked about web-based frameworks for data scientists. If you want to employ Plotly in your web-based model is to be employed properly with setting up API keys. 

SciKit-Learn

SciKit learn is typically used for simple data related and mining work. Licensed under BSD, it is an open source. It is mostly used for classification, regression and clustering manage spam, image recognition, and a lot more.

PyBrain

PyBrain is one of the best in class ML libraries and it stands for Python Based Reinforcement Learning, Artificial Intelligence. If you are an entry-level data scientist, it will provide you with flexible modules and algorithms for advanced research.

Comprehensively, if you are a budding data analyst or an established data scientist, you can use the above-mentioned tools as per your requirement depending on the kind of work you’re doing. This is why it is very important to understand the various libraries available that can make your work much easier for you to accomplish your task much effectively and faster. Python has been traversing the data universe for a long time with its ever-evolving tools and it is key to know them if you want to make a mark in the data analytics field. 

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