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Data analytics is used for transforming raw data into useful insights. It provides statistics and explanations to help businesses produce improved data-driven decisions.

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According to a variety of data analytics courses: modern business operations revolve around data science.

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Data Science uses several different tools and technologies to provide better results, and that’s what makes it difficult to choose just one particular data analytics tool for improving a business.

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Every tool has a different role to play, and not all of them are suitable for doing the same job. Therefore, let us learn what data analytics tools are, and explore some tools that are available in the market.

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## **What is a Data Analytics Tool?**

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A data analytics tool is used to make classifications about software and applications which are then used by data analysts in order to produce different analytical processes that help businesses make improved, better informed, and strategic business decisions.

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Data analytics tools also help organizations and industries minimize costs and increase profit.

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## **How to Choose the Right Data Analytics Tool?**

There are many data analytics tools in the market and it is quite difficult for businesses to choose the right one for themselves.

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When a company takes their business requirements into consideration, they have to get some extensive research done beforehand, where they would gather knowledge about data analytics tools and how to use them in the correct manner.

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In business, experienced data analysts and data scientists are the ones who take care of all the data-related work.

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When it comes to code creation, especially with SQL, there are some data analytics tools that provide an enticing experience, compared to other tools that are more connected to the click-and-point review best suited for interns.

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A company should always work with data analytics software that offers the best support for visualizations that are relevant specifically to their business targets. For instance, think about the ability of data analytics software for data modeling.

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Now, there are some data analytics tools that support a more semantic and syntactic layer or can model data themselves.

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If a company decides not to use one of these data analytics tools, they will have to use SQL or data analytics tools, such as the Data Build Tool or DBT, in order to model the data before the analysis.

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The requirement for data analytics tools is unmatched, and there will never be too much data to process. In order to do the processing efficiently and successfully, there will always be an abundance of data analytics tools.

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Here, we are going to take a look at some of the most popular data analytics tools to help you obtain better knowledge on how to choose the right tool for your business. Let’s begin!

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## **Top Eight Data Analytics Tools**

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**1. [Python](https://www.python.org/)**

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Python has been and still is one of the most used programming languages since it was first introduced to the market. The prime reason for Python’s unmatched popularity is that it is a language that can be learned quickly yet in a very efficient manner.

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Later, Python was transformed into one of the most powerful data analytics tools with the help of analytical and statistical libraries, such as SciPy, NumPy, and others. Today, it provides holistic coverage of both mathematical and statistical functions.

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Because of the multifaceted prevalence of Python, we are getting to see a huge number of programmers and technology aspirants inclining towards analytics. Data science aspirants are familiar with Python, and therefore, it has become probably the most popular data analytics tool for many data scientists.

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**2. [R](https://www.r-project.org/)**

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R, like Python, is also one of the popular data analytics tools used in the industry. R has outperformed SAS in terms of usage and is now used extensively by companies that can easily afford SAS.

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Over time, R has grown to be a lot more efficient. It now works with data sets more effectively than it used to a decade ago. R has also become more versatile. Between April 2015 and April 2016, R was introduced to 1,800 new packages, which makes the total number of R packages more than 8,000.

However, there are concerns about the growing number of R packages, but this has added a lot more to R’s competencies. R also incorporates quite well with different big data platforms which have contributed a lot to its success.

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**3. [SAS](https://www.sas.com/en_us/home.html)**

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SAS is another data analytics tool that continues to be widely used by various companies and industries. SAS is a vigorous, versatile, and learner-friendly tool. It has added a large number of new modules to the market. Some of the expert modules that have recently been added are SAS Anti-Money Laundering, SAS for IoT, SAS Analytics Pro for smaller businesses.

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**4. [Tableau](https://www.tableau.com/)**

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According to several [data analytics courses](https://www.analytixlabs.co.in/), Tableau remains one of the most easy-to-learn data analytics software in the world for the fact that it performs very efficiently. Tableau works with data and produces brilliant visualizations as well as great dashboards.

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Tableau can make visualizations even better than Excel, and can also handle more data than the latter. If a business wishes to implement more dynamicity to their business plots, Tableau is the tool they should go for.

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**5. [Excel](https://office.live.com/start/excel.aspx)**

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> Excel needs no introduction because we all know that it is the most popular data analytics tool in the world. Whether or not a person is an expert in R or Tableau, they will have to come back to Excel for the grunt work.

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Professionals who do not work with or in analytics, usually do not have access to data analytics tools, such as Python or SAS on their systems. But everyone has access to Excel. The usage of Excel becomes extremely crucial for a business when the analytics teams and business teams come to work together.

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**6. [Apache Spark](https://spark.apache.org/)**

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Apache Spark is an open-source data processing engine that was created to work with a specific focus on data analytics. It primarily focuses on unstructured data or large volumes of raw data. Apache spark has made its way to be included in the list of the most popular data analytics tools over the period of the last couple of years.

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The spark came to see this growth due to varied reasons. The chief one is the easy and simple assimilation with the ecosystem created by Hadoop. Apache spark is one of the very few data analytics tools that have its own libraries.

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**7. [Apache Storm](https://storm.apache.org/)**

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Apache Storm is a big data tool that is used to move data when it comes in an extremely continuous manner. Apache spark works on data that is static, and, Apache storm, on the other hand, is considered ideal for real-time data analytics and stream processing.

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**8. [Microsoft Power BI](https://powerbi.microsoft.com/en-us/)**

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Microsoft Power BI is considered one of the top business intelligence platforms that offers support to numerous data sources. It allows its users to produce reports, dashboards, displays, and post them.

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The MS Power BI also helps users create and implement automated models by applying machine learning with Azure ML. Users can amalgamate a group of dashboards and reports into a Power BI application.

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