Introduction
Without a doubt, Python has quickly become the go-to language in the data science field, and it is one of the first things recruiters look for in a data scientist’s skill set. It consistently ranks first in global data science polls, and its vast popularity continues to grow!

What makes Python so special for data scientists?

The fundamental Python language provides us with an easy-to-code, object-oriented, high-level language, similar to how our human body comprises various organs for multiple jobs and a heart to keep them going (the heart). We have libraries for math, data mining, data exploration, and visualization, among other things.

It is of utmost importance that we master each and every library, these are the core libraries and these won’t be changed overnight.

NumPy
data science libraries – numpy

NumPy is one of the most essential Python Libraries for scientific computing and it is used heavily for the applications of Machine Learning and Deep Learning. NumPy stands for NUMerical PYthon. Machine learning algorithms are computationally complex and require multidimensional array operations. NumPy provides support for large multidimensional array objects and various tools to work with them.

Various other libraries which we are going to discuss further like Pandas, Matplotlib and Scikit-learn are built on top of this amazing library! I have just the right resource for you to get started with NumPy –

SciPy
data science libraries – Scipy

SciPy (Scientific Python) is the go-to library when it comes to scientific computing used heavily in the fields of mathematics, science, and engineering. It is equivalent to using Matlab which is a paid tool.

SciPy as the Documentation says is – “provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.” It is built upon the NumPy library.

Data Mining
BeautifulSoup
web scraping tools beautiful soup

BeautifulSoup is an amazing parsing library in Python that enables web scraping from HTML and XML documents.

BeautifulSoup automatically detects encodings and gracefully handles HTML documents even with special characters. We can navigate a parsed document and find what we need which makes it quick and painless to extract the data from the webpages. In this article, we will learn how to build web scrapers using Beautiful Soup in detail.

Scrapy
web scraping tools scrapy

Scrapy is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.

You can learn all about Web scraping and data mining in this article –

Data Exploration and Visualization
Pandas
data science libraries – pandas

From Data Exploration to visualization to analysis – Pandas is the almighty library you must master!

Pandas is an open-source package. It helps you to perform data analysis and data manipulation in Python language. Additionally, it provides us with fast and flexible data structures that make it easy to work with Relational and structured data.

Matplotlib is the most popular library for exploration and data visualization in the Python ecosystem. Every other library is built upon this library.

Matplotlib offers endless charts and customizations from histograms to scatterplots, matplotlib lays down an array of colors, themes, palettes, and other options to customize and personalize our plots. matplotlib is useful whether you’re performing data exploration for a machine learning project or building a report for stakeholders, it is surely the handiest library!

Data Science libraries – plotly
Plotly is a free and open-source data visualization library. I love this library because of its high quality, publication-ready and interactive charts. Boxplot, heatmaps, bubble charts are a few examples of the types of available charts.

It is one of the finest data visualization tools available built on top of visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework. So if you are looking to explore data or simply want to impress your stakeholders, plotly is the way to go!

Here’s a great hands-on resource to get started –

Seaborn
Seaborn is a free and open-source data visualization library based on Matplotlib. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphics.

Seaborn provides easy functions that help you focus on the plot and know how to draw it. Seaborn is an essential library you must master. Here’s a great resource to checkout –

Become a Data Visualization Whiz with this Comprehensive Guide to Seaborn in Python

Machine Learning
Scikit Learn

Sklearn is the Swiss Army Knife of data science libraries. It is an indispensable tool in your data science armory that will carve a path through seemingly unassailable hurdles. In simple words, it is used for making machine learning models.

Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

Sklearn is a compulsory Python library you need to master. Analytics Vidhya offers a free course on it. You can check out the resources here –

PyCaret

Tired of writing endless lines of code to build your machine learning model? PyCaret is the way to go!

PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It helps you save tons of time by being a low-code library.

It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments, whether that’s imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning, or building ensemble models. Here’s an excellent resource for you to learn PyCaret from scratch –

Running Low on Time? Use PyCaret to Build your Machine Learning Model in Seconds

Tensorflow
Over the years, TensorFlow, developed by the Google Brain team has gained traction and become the cutting edge library when it comes to machine learning and deep learning. TensorFlow had its first public release back in 2015. At the time, the evolving deep learning landscape for developers & researchers was occupied by Caffe and Theano. In a short time, TensorFlow emerged as the most popular library for deep learning.

TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications.

To be a future-ready data scientist here are a few resources to learn TensorFlow –

Keras
Keras is a deep learning API written in Python, which runs on top of the machine learning platform TensorFlow. It was developed with a focus on enabling fast experimentation. According to Keras – “Being able to go from idea to result as fast as possible is key to doing good research.”

Keras is preferred over TensorFlow by many, due to its much better “user experience”, Keras was developed in Python and hence the ease of understanding by Python developers. It is simple to use and yet a very powerful library.

Many data science enthusiasts hail Pytorch as the best deep learning framework (that’s a debate for later on). It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive.

PyTorch is a Python-based library that provides maximum flexibility and speed. Some of the features of Pytorch are as follows –

Production Ready
Distributed Training
Robust Ecosystem
Cloud support
Excited? You can learn more about PyTorch here –

Introduction to PyTorch for Deep Learning [FREE COURSE]
A Beginner-Friendly Guide to PyTorch and How it Works from Scratch
End Notes
Python is a powerful yet simple language for all of your machine learning tasks.

In this article, we discussed popular libraries that will help you achieve your data science goals like maths, data mining, data exploration, and visualization, machine learning.

Do you have any other favourite library that we should know of? Let me know in the comments!