Top 7 Tools for Data Analysis in 2021!

With the evolution of the field of data analytics, the range of data analysis tools is ever increasing.

Below listed is a number of data analysis tools ranging from open source to commercial software, that is used in small and large organizations.

Microsoft Excel

Microsoft Excel is one of the best commercially available spreadsheet software. The software is widely used for data wrangling and reporting.

The features supporting calculations and graphing functions make the software perfect for data analysis. The most valuable built-in features, which have become a staple in this particular field, include pivot tables and form creation tools.

Pivot tools are used for sorting or totaling the data. The software also offers other vital functions such as streamline data manipulation.

The other important features of Microsoft Excel are CONCATENATE function and SUMIF. However, there are a few disadvantages of this particular software.

For example, Excel is not well-functioning with big datasets and causes inaccurate calculations.


Python is a programming language with a plethora of options. It is openly available and is offered by thousands of libraries.

It has a variety of functions including data scraping, analysis as well as reporting. Due to its readability, Python is highly popular in the field of technology.

Another major advantage of Python over other complex programming languages is the fact that it can be easily learned by aspiring programmers. Python’s huge resource libraries offer a variety of data analytics tools.

For example, NumPy and panda libraries function efficiently in streamlining highly computational tasks and supporting general data manipulation. The only disadvantage of Python is its low speed, in comparison to other programming languages.

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R, similar to Python, is a programming language. The availability of R is open source. The syntax of R although more complex than Python, is nevertheless, a popular programming language used widely for data mining and statistical analysis.

As R is specifically designed for dealing with heavy statistical computing tasks, it is a widely popular tool in data visualization. R has a network of freely available codes, namely Comprehensive R Archive Network or CRAN which provides more than 10,000 package options to choose from.

R is platform-independent and highly compatible with other languages and systems, which is inclusive of big data software. However, it is slow and less secure.

Statistical Analysis System (SAS)

SAS is a widely known commercial suite of business intelligence and data analytics tools. It is mostly used for profiling customers, reporting, data mining as well as predictive modeling.

This tool is more favorable for larger organizations as they tend to have more versatile levels of in-house programming expertise.

The way you use assignment help Australia service for assistance in your home-work, data analysts use this set of tools for business intelligence, multivariate and predictive analysis.

SAS is a great suite of tools as it is easily accessible and supports the user efficiently. It is also popular because of its business-focused interface.

However, the cons of this particular set of tools are a highly expensive and poor graphical representation.

Apache Spark

Apache Spark is an open-source data processing framework that is widely popular with data analysts and data scientists to process huge chunks of data sets.

It is specially designed to analyze unstructured big data and computes heavy analytics tasks across a number of computers.

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The great advantages of Apache Spark are exceptionally fast working, which it achieves by using RAM instead of local memory.

Apache Spark has a library of machine learning algorithms, MLlib that functions in a lot of ways. It is, however, computationally expensive and lacks a file management system.

Microsoft Power BI

It is a commercially available business analytics suite. It is multifunctioning and allows the user to create interactive visual reports and a dashboard with a minimal learning curve.

The other advantages are great data connectivity, regular updates, and proper visualization. It works hassle-free with different software.

Although there is a free version of this suite, there are problems with data limits. Other problems are rigid formulae and complicated user interface.


It is a commercial data visualization tool that is mostly used for creating data dashboards and worksheets.

Tableau is capable of handling huge amounts of data and has a user-friendly interface. It basically uses the visual drag and drop interface.

The advantages of Tableau are perfect visualizations, speed, and the aspect of being more interactive. This tool, however, does not allow data pre-processing and has poor version control.