Free Open Source Data Analysis Tools
Dr Mithileysh Sathiyanarayanan, Founder & CEO, MIT Square
Data Analysis and Data Visualization are crucial components of any data-driven project. The ability to quickly and efficiently extract insights from data is essential for making informed decisions. In recent years, open-source tools have become increasingly popular for data analysis, as it offers a cost-effective and flexible alternative to proprietary tools. In this article, we will take a look at some of the best open-source tools options for students, researchers, professors, data analysts, and other professionals.
Rapid Miner is one of the best predictive analysis system developed by the company with the same name as the Rapid Miner. It is written in JAVA programming language. It provides an integrated environment for deep learning, text mining, machine learning & predictive analysis.
The tool can be used for a vast range of applications including business applications, commercial applications, training, education, research, application development, and machine learning.
Rapid Miner offers the server as both on-premise & in public/private cloud infrastructures. It has a client/server model as its base. Rapid Miner comes with template-based frameworks that enable speedy delivery with a reduced number of errors (which are quite commonly expected in the manual code-writing process).
Also known as Waikato Environment is a machine learning tool that is best suited for data analysis and predictive modeling. It contains algorithms and visualization tools that support machine learning.
Weka has a GUI that facilitates easy access to all its features. It is written in JAVA programming language.
Weka supports major data mining tasks including data mining, processing, visualization, regression etc. It works on the assumption that data is available in the form of a flat file.
Weka can provide access to SQL Databases through database connectivity and can further process the data/results returned by the query.
KNIME is the best integration platform for data analytics and reporting. It operates on the concept of the modular data pipeline. KNIME constitutes of various machine learning and data mining components embedded together.
KNIME has been used widely for pharmaceutical research. In addition, it performs excellently in customer data analysis, financial data analysis, and business intelligence.
KNIME has some brilliant features like quick deployment and scaling efficiency. Users get familiar with KNIME in quite lesser time and it has made predictive analysis accessible to even naive users. KNIME utilizes the assembly of nodes to pre-process the data for analytics and visualization.
Orange is a perfect software suite for machine learning & data mining. It best aids data visualization and is a component-based software. It has been written in Python computing language.
As it is a component-based software, the components of orange are called ‘widgets’. These widgets range from data visualization & pre-processing to an evaluation of algorithms and predictive modeling.
Apache Mahout serves the primary purpose of creating machine learning algorithms. It focuses mainly on data clustering, classification, and collaborative filtering.
Mahout is written in JAVA and includes JAVA libraries to perform mathematical operations like linear algebra and statistics. Mahout is growing continuously as the algorithms implemented inside Apache Mahout are continuously growing. The algorithms of Mahout have implemented a level above Hadoop through mapping/reducing templates.
Apache Superset is a free and open-source data visualization and exploration platform that provides a user-friendly interface and supports a wide range of data sources. With Apache Superset, you can easily create interactive visualizations, ad hoc reporting, and dashboarding. Apache Superset offers a variety of visualization types, including heatmaps, scatterplots, pie charts, and more. Its versatility and ease of use make it a great alternative to Tableau for those who are looking for a user-friendly data visualization tool. It does not support machine learning algorithms.
RATH is an open-source Automated data exploratory analysis and visualization tool. It goes beyond an open-source alternative to Data Analysis and Visualization tools such as Tableau. It automates your Exploratory Data Analysis workflow with an Augmented Analytic engine by discovering patterns, insights, and causals and presents those insights with powerful auto-generated multi-dimensional data visualization. It does not support machine learning algorithms.
Octave is a high-level programming language and numerical computation software that is often used as a free alternative to MATLAB. It offers a wide range of mathematical and statistical functions, as well as visualization capabilities. It does not support machine learning algorithms.
Grafanais an open-source data visualization and monitoring platform. It allows users to create and share interactive dashboards and visualizations that can be used to monitor and analyze data from various sources. It can be used to monitor metrics, traces, and logs from different systems and applications, making it a powerful tool for monitoring and troubleshooting in data-driven organizations. It does not support machine learning algorithms.
Metabase is an open-source data visualization and business intelligence tool. It allows users to easily create and share interactive dashboards, charts, and reports. It supports a wide range of data sources and provides a simple user interface that allows non-technical users to easily explore data and gain insights. It also offers a SQL interface, making it easy to perform complex data analysis tasks. It does not support machine learning algorithms.
Other open-source data analysis tools are