Top Python Data Science Libraries

“Python has been an important part of Google since the beginning and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we are looking for more people with skills in this language.” Peter Norvig

The quote above gives you a reason influential enough to go with a Data Science with Python course and get certified in order to get placed in companies like Google.

Amazing fact:

A huge 1581% growth is anticipated in demand for Data Science professionals by 2021 across the globe, stating Python to be the most crucial skill for the same.

Data Science is regarded as the ‘sexiest job of the 21st century’ and is the ever-evolving domain. Almost 46% of jobs in Data Science include Python in the ‘Skills Required’ list. 

Data Scientists are difficult to hire and retain because there is a huge disparity between the demand for Data Scientists and the availability of trained and skilled professionals. If an organization wants to profit from Big Data and succeed in its sector, it needs trained Data Scientists. 

Do you wish to have this sexiest job? The key to land into this ever-evolving domain is Data Science with Python course, which makes you earn the certification, eventually making you the preferred candidate for employers.

Today, Python is the most widely used programming language used by Data Scientists. It is an open-source, object-oriented, easy-to-learn, easy-to-debug, high-performance language. It is built with extraordinary libraries meant for performing specific functions.

Learning Python requires you to have a brief knowledge of some of the commonly used libraries. Let us explore top Python libraries that are popular among Data Scientists.

Top Python Libraries for Data Science

  • TensorFlow

It is the library meant for performing numerical computations and different scientific fields. TensorFlow is typically a framework used for defining and executing computations that include tensors that are partially defined computational objects producing a value.

The top features of TensorFlow include better computational graph visualizations, error reduction by 50 to 60%, seamless library management, quicker updates to offer the latest features.

TensorFlow finds its applications in Text-based applications, Image and Speech Recognition systems, video detection, and time-series analysis. 

  • NumPy

NumPy or Numerical Python is a basic package for numerical calculations. It is a general-purpose library for array processing that offers high-performance multi-dimensional arrays and tools for working with those arrays.

The top features of NumPy include quick, pre-compiled functions for numerical routines, compact and quick calculations with vectorizations, and offers better efficiency by array-oriented computations.

The applications of NumPy are found in Data Analysis, Machine Learning, and Deep Learning.

  • SciPy

SciPy or Scientific Python, an open-source Python library, is widely used for high-level computations. It is extensively used for technical and scientific computations as it extends NumPy. It also offers user-friendly and systematic routines for scientific computations.

The top features of SciPy include built-in functions for solving differential equations, data manipulation and visualization, and multidimensional image processing. 

It finds its applications in optimizing algorithms, linear algebra, Fourier transformation, and more. 

  • Matplotlib

Matplotlib is a powerful library that produces graphs and plots. Hence it is extensively used for data visualizations. It also provides an object-oriented API that can be utilized for embedding those plots into applications.

The top features of Matplotlib include low memory consumption, open-source, free to use, and better runtime behavior. It supports many backends and output types, so you need not worry about what operating system you are using or the output format.

Matplotlib is applied in correlation analysis of variables, outlier detection, visualization of data to gain meaningful insights. 

  • Pandas

Pandas or Python data analysis is the most popular library used by Data Scientists. Pandas offer fast and flexible data structures like data frames CD that are designed to work with structured data with ease.

The special features of Pandas include expressive syntaxes and rich functionalities, which allow you to create your own functions, abstraction at high-level, high-level data structures, and manipulation tools.

Pandas find its applications in general data wrangling and cleaning, ETL tasks for data transformation and storage, academic and commercial areas, and time-series-specific functionalities such as linear regression, data range generation, date shifting, and more. 

  • Scikit Learn

Scikit-learn is a machine learning library that provides nearly all the machine learning algorithms you might require for data science. It is designed such that it can be interpolated into NumPy and SciPy.

Scikit-learn finds its applications in clustering, classification, regression, model selection, and dimensionality reduction.

  • Keras

Keras, like TensorFlow, is used for neural networks and deep learning modules. It supports the backends of both the TensorFlow and Theano, making it a good move to understand TensorFlow clearly.

The special features of Keras are that it provides huge prelabeled datasets that enable direct importing and loading. It contains parameters used for construction, training, configuration, training, and evaluation.

One of the most crucial applications of Keras is the deep learning models that are present with pre-trained weights. These models can be used to make predictions or extract their features without the need for training your own new model.

  • PyTorch

PyTorch is a Python-based scientific computing package that utilizes the power of graphics processing units. It is the most commonly preferred library for deep learning research platforms so as to provide the greatest flexibility and speed. 

PyTorch finds its applications in tensor computations with strong GPU application support, developing deep neural networks on a tape-based autograd system.

  • Plotly

The free and open-source library Plotly is popular because of its high-quality production-ready and convertible charts. It provides one of the finest data visualization tools. With Plotly, you can easily build, test, and deploy beautiful and interactive web applications. 

It finds applications in a wide range of statistical, scientific, financial, geographic, and 3-dimensional use-cases. 

Apart from the popular Python libraries in Python mentioned above, there are many more useful libraries such as Seaborn, BeautifulSoup, PyCaret, Theano, OpenCV, Requests, Ramp, NuPIC, and many many more.


Clearly, Python is the most crucial skill required for a career in Data Science. To make a career in such an evolving domain, go with an online training course.

There are several benefits of taking an online course.

  • You can learn at your own pace.
  • You can choose to learn via online training, instructor-led, or blended learning.
  • The training goes according to the level of your knowledge.
  • You get free access to the study material for a lifetime.
  • There are doubt sessions conducted by industry experts.
  • You don’t need to worry about the study material.

So, Enroll Now!

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