Data scientists are experts at understanding data. Given any amount of data, they will develop tools to read the data, find patterns in the data and then present the useful information in simplified easy-to-understand format. While a number of proprietary softwares exist which can do this task, open source tools have been gaining popularity recently. The open source community has been developing tools specifically geared for data science and machine learning and these tools have led to major advancements in the field. This has encouraged some big enterprises to get involved with the open source solutions as well. As a result, the present toolkit for data scientists has more and more open source tools.
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Here we list
some of the most popular tools used by data scientists:
- R – An open source language developed mostly for statistics and data visualization, R is considered to be a fairly easy language to master. Numerous packages are available online to help new users learn it.
- Python – The reason for the popularity of Python is its simplicity and legibility. Python has an extensive online support base. Numerous python packages exist which can easily be imported and used by new users for their specific tasks.
- KNIME – KNIME offers an open source platform developed in Java for data analysis, mining and predictive analysis. The company also offers a whole range of commercial extensions which can be used used to advance the base platform.
- Gawk – Gawk is open source and specially designed for working with files. It is an extension of the awk program which is rooted in the Unix operating system. Gawk makes it easy to make changes to existing text files and extract the useful information from given data files.
- Weka – Weka is based on Java and focuses on machine learning. It is most specifically used for data mining.
- Scala – Scala is based on Java and is great for large datasets. It is noted for its speed and is slowly gaining popularity in data scientists.
- SQL – Structured query language, or SQL, is in use by data scientists for decades now. It is used for basic data analysis and supposed to be one the best for filtering and searching through databases.
- RapidMiner – RapidMiner is a predictive analysis tool based on a free open-source platform. The company offers add-ons which can be bought to supplement the base platform.
- Scikit-learn – Scikit-learn is an open source machine learning library written in Python and built on the Python SciPy library.
- Apache Hadoop – Apache Hadoop has been written in Java and is used for processing large and complex datasets.
- Apache Mahout – Apache Mahout is based on Apache Hadoop. It can be used for building scalable machine learning algorithms.
- Apache Spark – Apache spark is specifically focused on cluster-computing and is preferred for big data analysis due to its speed.
- Scipi – Scipi is a computing framework developed on Python and can be used for scientific analysis, numerical computations and data visualization.
- Orange – Orange does not require coding and makes the whole process of data analysis fun and interactive.
- Axiis – Axiis is a data visualization kit used for building charts and exploring data.
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