Critical tools in Data Science Domain

SAS – It is specifically designed for operations and is a closed source proprietary software used majorly by large organizations to analyze data. It uses the base SAS programming language which is generally used for performing statistical modelling. It also offers various statistical libraries and tools that are used by data scientists for data modelling and organising. 

Apache Spark – This tool is an improved alternative of Hadoop and functions 100 times faster than MapReduce. Spark is designed specifically to manage batch processing and stream processing. Several Machine Learning APIs in Spark help data scientists to make accurate and powerful predictions with given data. It is a highly superior tool than other big-data platforms as it can process real-time data, unlike other analytical tools which are only able to process batches of historical data.

BigML – BigML provides a standardized software using cloud computing, and a fully interactable GUI environment that could be used for processing ML algorithms across various departments of the organization. It is easy to use and allows interactive data visualizations. It also facilitates the export of visual charts to mobile or IoT devices. BigML also comes with various automation methods that aid the tuning of hyperparameter models and help in automating the workflow of reusable scripts. 

D3.js – D3.js is a javascript library that makes it possible for the user to create interactive visualizations and data analysis on their web browser with the help of its several APIs. It can make documents dynamic by allowing updates on the client-side, it actively uses the change in data to reflect visualization on the browser. 

MATLAB – It is a numerical computing environment that can process complex mathematical operations. It has a powerful graphics library to create great visualizations that help aid image and signal processing applications. It is a popular tool among data scientists as it can help with multiple problems ranging from data cleaning and analysis to much advanced deep learning problems. It can be easily integrated with enterprise applications and other embedded systems. 

Tableau – It is a Data Visualization software that helps in creating interactive visualizations with its powerful graphics. It is suited best for the industries working on business intelligence projects. Tableau can easily interface with spreadsheets, databases, and OLAP (Online Analytical Processing) cubes. It sees a great application in visualizing geographical data. 

Matplotlib – Matplotlib is developed for Python and is a plotting and visualization library used for generating graphs with the analyzed data. It is a powerful tool to plot complex graphs by putting together some simple lines of code. The most widely used module of the many matplotlib modules is the Pyplot. It is an open-source module that has a MATLAB-like interface and is a good alternative to MATLAB’s graphics modules. NASA’s data visualizations of Phoenix Spacecraft’s landing were illustrated using Matplotlib.

NLTK – It is a collection of libraries in Python called Natural Language Processing Toolkit. It helps in building the statistical models that along with several algorithms can help machines understand human language. 

Scikit-learn – It is a tool that makes complex ML algorithm simpler to use. A variety of Machine Learning features such as data pre-processing, regression, classification, clustering, etc. are supported by Scikit-learn making it easy to use complex ML algorithms. 

TensorFlow – TensorFlow is again used for Machine Learning, but more advanced algorithms such as deep learning. Due to the high processing ability of TensorFlow, it finds a variety of applications in image classification, speech recognition, drug discovery, etc.