Statistics tools are playing a crucial role in running our life. We performed various tests on data to get valuable results from the data. We can get more information from that data if we do the proper experiment on the data. Every analysis depends on appropriate planning and execution. To win the data experiment’s battle, we need to make sure that we are analyzing the data in the right ways that will help us find critical insights of data.
Analyzing the data is crucial in many aspects of our life. There are plenty of statistics tools in the world that are designed for the statistical analysis and make this process super smooth and easy for the users. Here we are going to discuss the topmost statistics tools useful in many different aspects of our lives.
We have categories these tools as per their uses in the industry. All these tools are the statistical analysis tools, but we have divided them into two groups.
Statistics tools for data analysis
SPSS is the number 1 statistics tool for human behavior research. As the name suggests, it stands for Statistical Package for the Social Sciences. That is why the statisticians used it widely for data analysis purposes. Apart from that, you can also use the GUI for the graphical depictions of results. You can also create a customized script for automatic analysis. It is one of the most potent statistics tools for advanced statistical processing. SPSS Inc introduced it, but later on, it was acquired by IBM in the year 2009. Therefore the SPSS is known as IBM SPSS.
Key feature of SPSS Statistics
- It provides better ways to analyze, understand your data. You can also solve complex business and research problems quickly with the outstanding interface of SPSS.
- It helps you to ensure high accuracy and quality decision-making.
- You can easily integrate Python and R programming language code with SPSS.
- It also offers flexible deployment options to manage your software.
- You can also create stunning visualizations and reports with the help of point and click interface. You need not have the coding experience to get started with SPSS.
- It offers the best viewing patterns of missing data and summarizing variable distributions.
- You can store the data locally on your computer with SPSS.
R is one of the best open source statistics tools for data analytics. It is use by statisticians for research purposes. It offers excellent toolboxes that are available for a vast range of applications. R is an open-source programming language, and it has a severe learning curve. It is not for beginners, and you need to have good command over coding to get started with R. It was developed at Bell Laboratory by John Chambers and colleagues. R offers a wide range of statistics and graphical techniques i.e., linear, nonlinear modeling, classical statistics test, time series analysis, and many more.
Key Features of R Programming
- R programming offers effective data handling and storage facility,
- There is the most powerful suite of operators in R. That is used for the calculations on arrays, in particular matrices,
- It also offers the best tools for data analysis.
- R is also best for data visualization with the help of its graphical facilities for data analysis.
- It is a complete high-level programming language that includes the decision statements, conditional loops as well as many types of functions.
MS Excel is one of the best statistical tools for data analysis. It offers cutting edge solutions to data analytics professionals. You can use it for data visualization as well as simple statistics. It is the best statistical tool for those who want to perform basic data analysis techniques on their data.
Key Features of MS Excel
- It offers the best graphical user interface for best data visualization solutions, and You can create many types of graphs with the help of excel.
- MS Excel offers almost every feature to perform every single data analysis operation.
- It offers you to perform necessary calculations to an advanced one.
- There are lots of inbuilt formulas in excel that makes it also a reliable option to perform big data tasks.
- Excel allows you to manage your work. Most of the SMEs are using excel daily to run the smooth operation of their enterprises.
Tableau is one of the most powerful data visualization software. The data visualization technique is widely used in data analytics. Now it is the part of Salesforce that is known for its high-end CRMs in the world. You can create the best data representation of the massive amount of data in Tableau within a few minutes. Therefore it helps the data analyst to make quicker decision making. It has an enormous amount of online analytical processing cubes, cloud databases, spreadsheets, and many more. It also offers the drag and drop interface to the users. Thus the user needs to drag and drop the data set sheet in Tableau and set the filters as per their requirements.
Key Features of Tableau
- Best end to end analytics
- Most powerful data calculations
- Effective content discoveries
- High-end security within the system
- Work with almost every screen resolution.
It is not the most popular data analysis statistics tool. But you can do the basic as well as some advanced statistical techniques using Minitab. You can also execute commands in MATLAB with the help of GUI as well as scripted commands. It was developed in the year 1972 at Pennsylvania State University. The researcher’s Barbara F. Ryan, Thomas A. Ryan, Jr., and Brian L. Joiners are the creators of this tool. In earlier days, it was known as OMNITAB. In this way, it will enable you to find a solid answer to the most challenging questions.
Key Features of Minitab
- You can do different types of analysis using Minitab i.e., Measurement systems analysis, capability analysis, Graphical analysis, Hypothesis analysis, regression, and so on.
- It allows you to do best data visualization i.e., Scatterplots, matrix plots, box plots, dot plots, histograms, charts, time series plots, probability distribution plots etc.
- Minitab offers you to perform various statistics tests i.e., One-sample Z-test, one- and two-sample t-tests, paired t-test, etc.
- You can also perform regression and non-regression in Minitab
- It is best for analysis of variance. You can perform the ANOVA, General linear models, Mixed models, Multiple comparisons, plots: residual, factorial, contour, surface, etc.
Statistics Tools For Data Science
When it comes to data science, then Apache Hadoop is considered as the best and most reliable statistics tool. It is open-source software that is used for scalable computing. Apache Hadoop is part of the Apache software foundation under Apache license 2.0 and can solve the most complex computational problems. It is also best at data-intensive tasks. Hadoop has the best functionality that it doesn’t send the larger files directly to the node. It splits the large files into small chunks and then sends it over to the node with different instructions.
Key Features of Hadoop
- Hadoop is open-source software. It makes it a cost-effective tool for data science.
- Apache Hadoop has the built in features that schedules the jobs and manages the clusters automatically.
- It can easily integrate with external software and apps without any hassle.
- Apache Hadoop is quite easy to use for beginners as well. It has built in a framework that manages the distributed computing with user interference.
- It has the ability to process unstructured data. Besides, it makes it an available software that allows you to perform data science operations on any type and size of data.
SAS is one best statistics tool for data science. It is also playing a crucial role in the data science industry. You can use it either as the GUI or create your script for the advanced level statistics analysis in data science. It can produce the best graphs and charts. You can also extend the functionality of SAS using the coding feature.
Key features offered by SAS are
- It is one of the most accessible statistics tools and provides a variety of tutorials to the users.
- Variety of statistics tools available in its suite.
- best technical support when you need help
- High quality and Visually appealing reports
- It has the best support for spelling errors and grammar mistakes identification. That results in a more accurate analysis
RapidMiner is yet another best tool for data science. It offers a platform that is quite helpful in data preparation, machine learning, and predictive model deployment. You can create the data model from the initial stage to the last step easily with RapidMiner. It offers a complete data science package. It is best for machine learning, deep learning, text mining, as well as predictive analytics.
Features of RapidMiner
- It offers you the best platform for data preparation as well as for result visualization.
- You can expand the core functionality with the help of plugins in RapidMiner.
- It has the ability of real-time data tracking and analytics.
- It also supports various third-party integration without any hassle.
- Excellent security features.
Python is one of the best programming languages in the world. I have mentioned it in this blog because it can work seamlessly with the statistics. It is the most straightforward programming language and offers lots of packages and models for statistics and data science. Python is a high-level, general-purpose programming language. It was created by Guido Van Rossum and released in 1991. It is the best statistics tool for data science. You can fulfill all your statistics requirements by using Python for data science.
Key features of Python
- Some of its packages like Pandas are used for data manipulation and exploration in data science
- Matplotlib; Seaborn; Datashader are used for the stunning data visualization in Python.
- Python has built in data storage and big data frameworks.
- SciPy is the best packages for data science in Python.
- Numpy is a python library that is used to perform basic to the advanced level mathematics equations.
MATLAB (The Mathworks)
MATLAB is the best statistics analysis tool and the best statistics programming language in the world. It offers a variety of tools in its toolbox that makes it quite easy to use programming languages. You can do the most sophisticated statistical analysis with MATLAB i.e., EEG data analysis. You can extend MATLAB functionality with the help of add ons for toolboxes. It offers a multi-paradigm numerical computing environment. It means that you can do the procedural as well as object-oriented programming with MATLAB. Math Works developed it. It is best for matrix manipulation: data function plotting, algorithms implementations, user interface creations, and many more. You can also run the program of other programming languages with MATLAB all because of its interfacing function.
Key Feature of MATLAB
- MATLAB toolboxes are designed with perfection and professionalism. It is also rigorously tested by the tester in different conditions. Apart from that, you also get full documents with MATLAB.
- MATLAB is specially designed for the production. Therefore the MATLAB code is production-ready. All you need to do is integrate the data sources and business systems to the enterprise systems.
- It has the functionality to change the MATLAB algorithms into C, C++, and CUDA cores.
- MATLAB provides the best simulation platform for users.
- It has the best environment to perform data analysis techniques.
We have seen that there are plenty of statistics tools for data analysis, as well as data science. Even there are some more statics tools available in the world that can fulfill your requirements for data analysis and data science. Even some of the statistics tools online are the alternative to these statistics tools that I have mentioned above. But all these tools are best in their class. You can use any one of these tools without having the second opinion.
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