Tools for Data Science

In the previous article, we talked about the different languages used by data scientists and the different open-source, commercial, and cloud-based software for doing different data science tasks.

In this article, we would be learning about:

• The important libraries used by the different programming languages.
• what is an API, and what is it used for?
• The different forms of data structures used to store data.

And in the end, we would be providing some websites in which you can find different types of data.

Learn about the providers of online masters in data science by clicking here

Libraries for Data Science

Libraries are a collection of functions and methods for doing some particular tasks. They are written by someone else and for using them we just have to import it to our program and call the functions we want.

Scientific Computing Libraries in python

Pandas

Pandas is Developed by Wes McKinney. Pandas is a software library for Python. It is used for data manipulation and analysis. Pandas provide tools to work with different types of data. The main tool that pandas provide is a way to represent the data in the form of a 2D table called a Data Frame. This allows the user to give an index to data and can use these indices to work with the data easily.

NumPy

Created by Travis Oliphant NumPy is a library for Python programming language based on arrays. This enables the user to perform different mathematical operations using these arrays which represent our data.

Visualization Libraries in python

Data Visualization is like telling a story where the different insights from data play different roles. It is one of the best ways to get insights about the data and also to communicate with others about the results you get from your analysis of that data.

Some of the most widely used visualization libraries in python are:

• Matplotlib – Most popular and easy to use
• Seaborn – Similar to matplotlib but more modern
• Plotly – provides functions to make interactive plots
• Altair – Used to generate maps

Machine Learning & Deep Learning Libraries in python

Scikit-learn

Developed by David Cournapeau scikit-learn contains tools for statistical modelling, including regression, classification, clustering, and many more. It is built using libraries like Scipy (similar to NumPy), Numpy, and matplotlib.

Keras

Keras is an open-source library with a high-level interface (functions that require very lengthy coding can be done using simple function call) used for building deep learning models easily with few codes. It can run on GPUs which makes training the models faster.

TensorFlow

Developed by the Google Brain team TensorFlow offers tools for large scale production and deployment of deep learning models. It uses a low-level framework which means we have to code a bit more than what we do in Keras. With the arrival of the newer version of TensorFlow, TensorFlow 2.0 Keras is now integrated with TensorFlow to make it a more higher-level framework.

PyTorch

PyTorch is developed by the fakebook’s AI Research(FAIR) lab . It is used for building deep learning models for different applications and it is open source.

Apache Spark