Top 20 Statistical Analysis Tools

Here is a list of 20 popular statistical analysis tools:

  1. SAS
  3. Minitab
  4. Gretl
  5. RapidMiner
  6. Orange
  7. KNIME
  9. StatTools

1. SAS

SAS Visual Analytics is available on-prem or in the cloud. Visual Analytics allows users to visually explore data to automatically highlight key relationships, outliers, and clusters. Users can also take advantage of advanced visualizations and guided analysis through auto charting. SAS has made its name as a result of advanced analytics, as the tool can ingest data from diverse data sources and handle complex models. In addition to BI, SAS offers data management, IoT, personal data protection, and Hadoop tools.


MATLAB is one of the most well-reputed statistical analysis tools and statistical programming languages. It has a toolbox with several features that make programming languages simple. With MATLAB, you may perform the most complex statistical analysis, such as EEG data analysis. Add-ons for toolboxes can be used to increase the capability of MATLAB.

Moreover, MATLAB provides a multi-paradigm numerical computing environment, which means that the language may be used for both procedural and object-oriented programming. MATLAB is ideal for matrix manipulation, including data function plotting, algorithm implementation, and user interface design, among other things. Last but not least, MATLAB can also run programs written in other programming languages.


  • MATLAB toolboxes are meticulously developed and professionally executed. It is also put through its paces by the tester under various settings. Aside from that, MATLAB provides complete documents.
  • MATLAB is a production-oriented programming language. As a result, the MATLAB code is ready for production. All that is required is the integration of data sources and business systems with corporate systems.
  • It has the ability to convert MATLAB algorithms to C, C++, and CUDA cores.
  • For users, MATLAB is the best simulation platform.
  • It provides the optimum conditions for performing data analysis procedures.


Minitab is a data analysis program that includes basic and advanced statistical features. The GUI and written instructions can be used to execute commands, making it accessible to beginners and those wishing to perform more advanced analysis.


  • Minitab can be used to perform various sorts of analysis, such as measurement systems analysis, capability analysis, graphical analysis, hypothesis analysis, regression, non-regression, etcetera.
  • It enables you to create the most effective data visualizations, such as scatterplots, box plots, dot plots, histograms, time series plots, and so on.
  • Minitab also allows you to run a variety of statistical tests, including one-sample Z-tests, one-sample, two-sample t-tests, paired t-tests, and so on.

4. Gretl

Gretl is an open-source statistical package, mainly for econometrics. The name is an acronym for Gnu Regression, Econometrics, and Time-series Library. It has a graphical user interface (GUI) and a command-line interface. It is written in C, uses GTK+ as a widget toolkit for creating its GUI, and calls gnuplot for generating graphs. The native scripting language of gretl is known as Hansl (see below); it can also be used together with TRAMO/SEATS, R, Stata, Python, Octave, Ox, and Julia.


RapidMiner is a valuable platform for data preparation, machine learning, and the deployment of predictive models. RapidMiner makes it simple to develop a data model from the beginning to the end. It comes with a complete data science suite. Machine learning, deep learning, text mining, and predictive analytics are all possible with it.


  • It has outstanding security features.
  • It allows for seamless integration with a variety of third-party applications.
  • RapidMiner’s primary functionality can be extended with the help of plugins.
  • It provides an excellent platform for data processing and visualization of results.

6. Orange

Orange is an open-source data mining and machine learning tool that has existed for more than 20 years as a project from the University of Ljubljana. The tool offers a mix of data mining features, which can be used via visual programming or Python Scripting, as well as other data analytics functionalities for simple and complex analytical scenarios. It works under a “canvas interface” in which users place different widgets to create a data analysis workflow. These widgets offer different functionalities such as reading the data, inputting the data, filtering it, and visualizing it, as well as setting machine learning algorithms for classification and regression, among other things.


  • Visual programming interface to easily perform data mining tasks via drag and drop
  • Multiple widgets offering a set of data analytics and machine learning functionalities
  • Add-ons for text mining and natural language processing to extract insights from text data


Last on our list is KNIME (Konstanz Information Miner), an open-source, cloud-based, data integration platform. It was developed in 2004 by software engineers at Konstanz University in Germany. Although first created for the pharmaceutical industry, KNIME’s strength in accruing data from numerous sources into a single system has driven its application in other areas. These include customer analysis, business intelligence, and machine learning.

Its main draw (besides being free) is its usability. A drag-and-drop graphical user interface (GUI) makes it ideal for visual programming. This means users don’t need a lot of technical expertise to create data workflows. While it claims to support the full range of data analytics tasks, in reality, its strength lies in data mining. Though it offers in-depth statistical analysis too, users will benefit from some knowledge of Python and R. Being open-source, KNIME is very flexible and customizable to an organization’s needs—without high costs. This makes it popular with smaller businesses, that have limited budgets.


  • Type of tool: Data integration platform.
  • Availability: Open-source.
  • Mostly used for Data mining and machine learning.
  • Pros: Open-source platform that is great for visually-driven programming.
  • Cons: Lacks scalability, and technical expertise is needed for some functions.


The analytical capabilities of Excel can be enhanced by using a variety of tools provided by XLSTAT. This makes it ideal for statistics and data analysis requirements.


Apache Spark was originally developed by UC Berkeley in 2009 and since then, it has expanded across industries and companies such as Netflix, Yahoo, and eBay that have deployed Spark, processed petabytes of data, and proved that Apache is the go-to solution for big data management, earning it a positive 4.2-star rating in both Capterra and G2Crowd. Their ecosystem consists of Spark SQL, streaming, machine learning, graph computation, and core Java, Scala, and Python APIs to ease the development. Already in 2014, Spark officially set a record in large-scale sorting. Actually, the engine can be 100x faster than Hadoop and this is one of the features that is extremely crucial for massive volumes of data processing.


  • High performance: Spark owns the record in the large-scale data processing
  • A large ecosystem of data frames, streaming, machine learning, and graph computation
  • Perform Exploratory Analysis on petabyte-scale data without the need for downsampling


Apache Hadoop is an open-source software that is best known for its top-of-the-drawer scaling capabilities. It is capable of resolving the most challenging computational issues and excels at data-intensive activities as well, given its distributed architecture. The primary reason why it outperforms its contenders in terms of computational power and speed is that it does not directly transfer files to the node. It divides enormous files into smaller bits and transmits them to separate nodes with specific instructions using HDFS. More about it here.

So, if you have massive data on your hands and want something that doesn’t slow you down and works in a distributed way, Hadoop is the way to go.


  • It is cost-effective.
  • Apache Hadoop offers built-in tools that automatically schedule tasks and manage clusters.
  • It can effortlessly integrate with third-party applications and apps.
  • Apache Hadoop is also simple to use for beginners. It includes a framework for managing distributed computing with user intervention.
Rajesh Kumar
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