Data analytics involves finding useful information from a large amount of data and working to improve it. There are many big data tools for data analytics. This article summarizes 10 open source tools for data analytics.

1. Gephi

Gephi is an open source software package for network analysis and visualization, built in Java based on NetBeans. It is like a Photoshop which deals with data. The user interacts with the drawn graph and manipulates the structure, shape, color, etc. while extracting the hidden attribute. So you can make hypotheses, intuitively understand patterns, and isolate structural singularities and defects during data sourcing.

2. Knime

KNIME is an open source software that provides a workflow data analysis platform. A series of functions called nodes are connected by lines to realize various processes. It has over 1000 nodes, numerous workflow samples, comprehensive integration tools, various algorithms available. Naturally, you can use it to discover potential hidden data, gain new insights, and make predictions.

3. Node XL

NodeXL is an open source network analysis template that can be done in Excel. You can easily draw a network diagram by entering or copying an edge list in an Excel worksheet. You can also select an image for the shape of the node, and draw an image-based network diagram. Easily calculate graph metrics to create network visualizations quickly, and add social network analysis and visualization capabilities to familiar spreadsheets.

4. OpenRefine

OpenRefine (formerly Google Refine, and formerly Freebase Gridworks) is a stand-alone open source desktop application. It can clean up data, convert it to other formats, and do something called data rungling. It looks similar to a spreadsheet application (it also handles the spreadsheet file format), but behaves more like a database.

5. Orange

Orange is a unique tool with a wide variety of interfaces. It is suitable for novice data mining beginners as well as programmers who write scripts and implement data processing algorithms. It is also easy to learn and use since it is a visual programming language. As for the workflow, the user can create it by predefined or custom widgets through the visual interface. When it comes to data analysis, the ability to visualize the results is important. So, in addition to ordinary bars and line graphs, Orange can handle various output formats. These range from tree diagrams, network diagrams to heat maps.

6. Pentaho

Pentaho is a data integration and analysis platform for integrating and analyzing a wide variety of big data. It has a consistent environment for data integration as well as analysis. It can not only extract, prepare and blend data but also analyze and visualize integrated data. Pentaho is a BI suite that includes reporting, interactive analysis, dashboards, data integration, extract/transform/load (ETL), data mining, and many other features for BI.

7. R language

The R language is a programming language for statistical analysis It is different from other programming languages ​​that develop systems. In particular, it has statistical analysis functions and rich analysis processing. Furthermore, it has several data graphing and illustration functions. Its inbuilt features offer more flexibility for Data Analytics over other languages. The R language is expanding its use in practical fields. One of the reasons is that environmental changes don’t affect statisticians easily in this language. For example, changing from an educational institution such as a university to a for-profit company’s research institute.

8. RapidMiner

RapidMiner is an open source data analysis platform. Machine learning, data mining, text mining, feature selection, predictive analysis, business analysis, etc. One of its characteristics is that you can perform data analysis without programming. You can also link it with R or Python and do more advanced analysis.

The basic flow of data mining involves these three processes:

“data preparation” → “data analysis” → “result evaluation”.

RapidMiner can significantly reduce the cost of doing these three tasks. It has several visualization features such as scatter plots, histograms, box plots, and heat maps. By visualizing analysis results, you can gain new insights beyond aggregation from data.

9. Talend

Talend integrates data across cloud and on-premises environments in a single open platform. Businesses can utilize more relevant data more quickly. It is a data integration collaboration platform that demonstrates its power as an application development platform for single database systems. You can also integrate and link data for multiple systems. In addition, you can export all the generated processing by Talend as Java code.

10. Weka

Weka is a collection of visualization tools and algorithms for data analysis and predictive modeling. Supports standard data mining tasks such as data preprocessing, clustering, statistical classification, regression analysis, visualization, and feature selection. Fully implemented in Java, it runs on most platforms and is easy to use with a GUI. The machine learning and clustering algorithms implemented by Weka are used as libraries by many tools. They are also available through other tools and APIs and CLI.

Conclusion

In this article, I introduced you to some open source tools for data analytics. You can give them a try and see which one suits your workflow.

Have you used any of them? Are there any other tools you use? What are your opinions on these? Leave your opinions down in the comments!