There are 100s of tools available to support Data Scientists to do their day to day job. But due the usefulness, features and ease to use, few are widely popular compared to rest.
This document provides a brief overview of 15 top data science tools, it covers:
1. The origin country,
2. Creator(s),
3. Year of creation, and
4. Small Overview
It covers a wide range of tools used in various aspects of data science, from programming languages like Python and R to software suites like SAS and platforms like Hadoop and Spark.
Additionally, it includes tools for data visualization (Tableau, D3.js), version control (Git), and specialized tools for machine learning (H2O.ai) and business intelligence (Domo, QlikView). The document also mentions tools like Julia for technical computing, KNIME for data analytics, and MATLAB for numerical computing.
I hope you find this insightful.
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Hmm … it will come in handy …