Best Software for Statistics and Data Analysis

Data analysis is crucial in almost all academic fields. With the advent of modern technology, data analysis has been really easy. But just like anything, it depends on the tools that you have at your disposal. There are thousands of software for statistics and data analysis that could meet your needs. Amidst all these options, making the right choice is key to the success of your project. So, here are the best software for statistics and data analysis:

Best Software for Statistics and Data Analysis

Here are some Best Software for Statistics and Data Analysis:

1. Excel

Excel may not be the most powerful software on this list, but it is the fundamental one. It’s your base for all other higher-level software. Almost 99% of all companies use it in some capacity, and it is also widely used in academics. 

If you have no experience with programming and don’t want to learn coding, just use Excel. It’s super flexible and easy to use. It gives you loads of options for visually representing your data.

Platforms: Windows, macOS, Android, and iOS

Pricing: Paid (Comes with MS Office)

2. R

R is the lingua franca of statistics. It’s an extremely versatile programming language designed for statistical analysis. It has a wide variety of libraries that can do almost any task you throw at them. R was written by statisticians and is solely intended to be used for statistical purposes.

You should start with Excel or Google Sheets and move to R once you have some more experience. R has a pretty steep learning curve though since it is a programming language. R is pretty powerful. And for any advanced level of statistical works, learning to code is a must.

A beginner should start using R with the Tidyverse package. I recommend using R Studio as well which is free and makes using R many times easier. R along with Tidyverse and some good libraries is way better than anything the proprietary software companies want to sell you.

Once you know R, other data analysis software will be easy to pick up. If you have no prior experience of programming, R, like python, will be difficult. It isn't as versatile as Python but it is much more flexible for analysis and custom statistical needs. 

I believe a beginner should not think about other options and just jump in with R. Basic skills required for simple statistical analysis can be gained within a weekend. A large number of packages available makes things a whole lot easier.

Platforms: Multiple

Pricing: Free

3. Tableau

Tableau is another great alternative if you are just starting and don't want to learn a lot of coding. It was designed for non-programmers. There’s a free version (Tableau Public) and lots of videos online to get started. It is a simple drag and drop software with zero coding. It has an easy to use UI too.

It gives you loads of options for visually representing your data. It can help make your data visually appealing, intuitive, and graphical.

Platforms: Windows, macOS, Linux

Pricing: Free, Paid


For someone with basic statistical knowledge and no programming knowledge, SPSS will be the easiest. It is essentially Excel with some extra statistical menu bars. It isn't a programming language, has an extremely easy point-and-click interface, and can be learned in a matter of hours.

I believe SPSS should be learned along with your statistical course. And soon afterward, you should migrate to a better alternative. 

Platforms: Windows, macOS, Linux

Pricing: Paid

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5. Stata

Stata is a simple language-based software designed for basic statistical procedures. It is very easy to learn and use. It is very intuitive and has a smaller learning curve compared to R. It is very powerful compared to SPSS, but is more difficult to learn. It’s an absolute winner and a go-to data- analysis tool for 99% of the users.

Stata is great for basic data manipulation, regression analysis, and summary statistics. Stata has its limitations but is robust enough for most users.

Platforms: Windows, macOS, Linux

Pricing: Paid

6. Python

Python is a general-purpose high-level language. You can do almost anything with Python, including data analysis. Python skills are mandatory in fields like machine learning, neural networks, and data science. Without prior knowledge of programming, learning Python will be difficult.

In Python, data analysis can be done using the pandas, pandas-profiling, Numpy, Sci-kit learn (or Statsmodels), and Matplotlib packages in a Jupyter notebook. Python can also be very robust but again requires coding experience and has a relatively steep learning curve. 

Platforms: Windows, macOS, Linux

Pricing: Free


It is a free alternative to SPSS. It is a point-and-click software that is free and user friendly. It is more user friendly than R for a beginner, and able to do pretty much anything you need to do.

Platform: Windows, macOS, Linux

Pricing: Free

8. SAS

It is an old data analysis software. It has a cumbersome UI and is arduous to use. However, it is largely used in government organizations. It is not very popular in academia.

Platform: Windows, IBM mainframe, Unix/Linux, OpenVMS Alpha

Pricing: Free

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Bonus: Other Software

9. MS Power BI

It is a Business Intelligence and reporting tool that lets a user create visualizations of data from multiple data sources (excel, databases, web services, etc). It is an alternative to Tableau and has a low learning curve. It integrates well into other office tools.

10. JASP

For non-technical people that are just looking at simple testing and simple regression, JASP is probably a better choice than R. It is really easy to use for t-tests, correlations, regressions, etc.

11. RapidMiner

It's a pretty powerful data analysis tool. You don’t need to learn to code as it is a point and click software.

12. Jamovi

It is a simple software with an easy UI.

13. Epi Info

It is a point-and-click software that is free and user friendly

14. Octave

It is the free version of Matlab. It can do numerical and analytical things.

These were some of the best software for Statistics and Data Analysis. Hope you liked the posts.

Read also: Best Software for Academic Research and Thesis