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Critical tools used in the Data Science Domain

Data Scientists help find insights about the market and help make products better. They are responsible for analyzing and handling a massive amount of structured and unstructured data and require various tools to do so. Some of the tools used by Data Scientists to carry out their data operations are mentioned below.

1. SAS-
Designed for statistical operations, SAS is an open source proprietary software that is used to analyze data. Base SAS programming language, which is generally used for statistical modeling is used by SAS. It offers a number of statistical libraries and tools that can be used for modeling and organizing data. SAS is highly reliable, it is also quite expensive and thus is used mainly by larger industries.

2. Apache Spark-
Spark has been specifically designed to handle batch processing and stream processing. It is one of the most widely used Data Science tools and comes with various APIs that facilitate data scientists to make powerful predictions with the data given to them. It is highly superior to other big-data platforms as it is able to process real-time data, unlike other analytics tools which process batches of historical data. It can perform operations 100 times faster than MapReduce.

3. BigML-
BigML provides a fully interactable, cloud based GUI environment that can be used for processing various ML Algorithms. Through BigML, companies are able to use Machine Learning Algorithms across various parts of their company, for example, this software can be used in sales forecasting, product innovation etc. It also specializes in predictive modeling.

4. D3.js-
It is a JavaScript library that allows you to make interactive visualization and analysis of data on your web-browser. JavaScript is used mainly as a client-side scripting language. One of the powerful features of D3.js is the usage of animated transitions. It also makes documents dynamic by allowing updates on the client side and actively using this change in data to reflect visualizations on the browser. It can be very beneficial for Data Scientists who are working on IOT based devices.

5. MATLAB-
It is a closed-source software which facilitates matrix functions, algorithmic implementation and statistical modeling of data and is a multi-paradigm numerical computing environment which is used for processing mathematical information.
MATLAB is used to stimulate neural networks and fuzzy logic in Data Science. We are able to create powerful visualization by using the MATLAB graphics library. It is also used for image and signal processing.

6. Tableau-
It is a Data Visualization software packed with powerful graphics which are used to make interactive visualization. One of the important aspects of Tableau is its ability to interface with databases, spreadsheets, Online Analytical Processing cubes, etc. It can also be used for plotting longitudes and latitudes in a map. It is an enterprise software, but comes with a free version called Tableau Public.

7. Matplotlib-
This tool is one of the most popular tools for generating graphs with data that has been analyzed. It is a plotting and visualization library that has been developed for Python and is mainly used to plot complex graphs using a simple line of code. We are able to generate bar plots, histograms, etc. This tool is used over other contemporary tools as it is the more preferred tool and is ideal for beginners in learning data visualization with Python. In fact, NASA, during the landing of Phoenix Spacecraft used Matplotlib for illustrating data visualization.

8. NLTK-
One of the most popular fields in Data Science is Natural Language Processing or NLP and deals with the development of statistical models which help computers understand human language. Natural Language Toolkit or NLTK is a collection of libraries that comes under Python language and has been developed for this particular purpose. Word Segmentation, Speech Recognition, Machine Translation etc., are some of the applications.

9. Scikit-learn-
A library based in Python, Scikit-learn is used for implementing ML Algorithms. It is widely used for analysis and data science because it is a tool that is easy to implement. It makes it easy to use complex ML Algorithms and is therefore used in situations that require rapid prototyping. It is also an ideal platform to perform research which requiring basic ML. Several underlying Python libraries such as Numpy, Matplotlib etc., are used by Scikit-learn.

10. TensorFlow-

A standard tool for Machine Learning, it is widely used for advanced ML algorithms like Deep Learning. It was named TensorFlow after Tensors, which are multidimensional arrays. It is an open-source toolkit and is known for its performance and high computational abilities. It can run on CPUs as well as GPUs and has emerged on other powerful TPU platforms, this gives TensorFlow an unprecedented edge in terms of the processing power of advanced ML algorithms. It has a variety of applications such as image classification, drug discovery, speech recognition etc., due to its high processing ability. 

This is a brief explanation about the various Data Science tools that are available today. Read more here.

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