Data Science Tools 2020

A Data Scientist is responsible for extracting, manipulating, pre-processing and generating predictions out of data. In order to do so, he requires various statistical tools and programming languages. In this article, we will share some of the Data Science Tools used by Data Scientists to carry out their data operations. We will understand the key features of the tools, benefits they provide and comparison of various data science tools.

Altair

Altair Knowledge Works (some time ago Datawatch) offers an advanced data mining and predictive analytics workbench called Knowledge Studio. The product includes licensed Decision Trees, Strategy Trees, and a work process and wizard-driven graphical UI. It additionally incorporates capacities for data preparation tasks, visual data profiling, advanced predictive modeling, and in-database analytics. Users can import and export using common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS.

 Mozenda

Mozenda is an enterprise cloud-based web-scraping platform. It assists organizations with gathering and sorting out web information most productively and cost-effectively possible. The tools have a point-to-click interface with an easy to understand UI. The tools have two sections: an application to construct the data extraction project and Web Console to run agents, organize results, and export data. It is anything but difficult to incorporate and permits users to distribute results in CSV, TSV, XML, or JSON format. The tools additionally give API access to get information and have inbuilt storage integrations like FTP, Amazon S3, Dropbox, and much more.

SAS

It is one of those data science tools which are specifically designed for statistical operations. SAS is a closed source proprietary software that is used by large organizations to analyze data. SAS uses base SAS programming language which for performing statistical modeling. It is widely used by professionals and companies working on reliable commercial software. SAS offers numerous statistical libraries and tools that you as a Data Scientist can use for modeling and organizing their data.

Apache Spark

Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing. It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce. Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.

D3.js

Javascript is mainly used as a client-side scripting language. D3.js, a Javascript library allows you to make interactive visualizations on your web-browser. With several APIs of D3.js, you can use several functions to create dynamic visualization and analysis of data in your browser. Another powerful feature of D3.js is the usage of animated transitions. D3.js makes documents dynamic by allowing updates on the client side and actively using the change in data to reflect visualizations on the browser.

Tableau

Tableau is a Data Visualization software that is packed with powerful graphics to make interactive visualizations. It is focused on industries working in the field of business intelligence. The most important aspect of Tableau is its ability to interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes, etc. Along with these features, Tableau has the ability to visualize geographical data and for plotting longitudes and latitudes in maps.

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