Data Analytics

Disclosure: is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.

7 Best Data Analytics Tools to Use in 2022

Posted in Data Analytics

Ever wonder why, after searching for holiday destinations, you log into your favourite social media account only to see advertisements of the very destination you were searching for? That’s data analysis tools working behind the scenes to target you and your interests.

Big Data and Data Analytics tools and techniques help deliver precise advertisements, among other things, to users, and it’s no surprise that the world’s largest companies are very keen on them. It’s estimated that over 2.5 quintillion bytes of data is created every day, with over 44 zettabytes making up the internet by the end of 2020.

There are many tools to assist this Data-Driven Decision-making process, and choosing the right tool is a challenge for data scientists or data analysts. Common questions that could run in your mind are: How do you use data analysis tools? How easy is it to learn data analysis? And if you are a business owner, you might what data are the relevant tools for data analysis and how much do they cost?

Here we cover some of the most popular data analytics tools, with their features, pros and cons.

Top Data Analytics Tools

Some of the best data analytics tools available today are listed below. This does not cover all of the tools out there, but they are some of the most popular.

  1. Python
  2. R
  3. SAS
  4. Excel
  5. Power BI
  6. Tableau
  7. Apache Spark

Let us walk through each of these tools.


1. Python



  • Libraries, such as Scipy, Scikit-learn, StatsModels, are used for statistical modeling, mathematical algorithms, machine learning, and data mining
  • Matplotlib, seaborn, and vispy are packages for data visualization and graphical analysis
  • Python has an extensive developer community and is the most widely used language
  • Top Companies that use Python for data analysis are Spotify, Netflix, NASA, Google and CERN, among others

Python was initially designed as an Object-Oriented Programming language for software and web development and later enhanced for data science. It is a powerful data analysis tool that is very popular, and Python itself is one of the fastest-growing programming languages today. It is also free and open source.

Python’s data analysis library Pandas was built over NumPy, which is one of the earliest libraries in Python for data science. With Pandas, you can just do anything! You can perform advanced data manipulations and numeric analysis using data frames. Pandas support multiple file-formats; for example, you can import data from Excel spreadsheets to processing sets for time-series analysis.


  • Great set of friendly libraries for any aspect of scientific computing
  • Good for data visualization, data masking, merging, indexing and grouping data, data cleaning, and many more
  • Extensive development community
  • Free, open-source software, and it is easy to learn
  • Among the easiest programming languages to learn


  • High memory usage
  • Dynamically typed, which may lead to more user-generated bugs

To know more about Pandas, click below. 

Checkout Python Pandas Tutorials.



2. R

R Programming



  • Used by statisticians for statistical analysis, Big Data and machine learning
  • Good for data for statistical modeling, visualization, and data analysis
  • Often used for Exploratory Data Analysis(EDA)
  • Used by Facebook, for behavior analysis related to status updates and profile pictures; Google for advertising effectiveness and economic forecasting; Twitter for data visualization and semantic clustering; and Uber for statistical analysis.

R is a leading programming language for statistical modeling, visualization, and data analysis. It is majorly used by statisticians for statistical analysis, Big Data and machine learning. As a free, open-source programming language with enhancements through user written packages, it has a lot to offer to the data analyst.

R is a winner when it comes to Exploratory Data Analysis(EDA), an approach to analyzing data sets to summarize their main characteristics, often with visual methods.


  • Excellent when it comes to data visualization and analysis with packages such as ggplot, lattice, ggvis, etc.
  • Open source and has a developer community
  • Data manipulation through packages such as plyr, dplyr, and tidy


  • Steep learning curve and needs some prior coding knowledge
  • Slower than Python
  • Slightly more difficult to implement it into web applications


Learn more about R here


3. SAS



  • Used in business intelligence
  • Widely used in the pharmaceutical industry, BI, and weather forecasting
  • Google, Facebook, Netflix, Twitter use SAS
  • SAS is used for clinical research reporting in Novartis and Covance, Citibank, Apple and Deloitte for predictive analysis

SAS is a statistical software suite widely used for BI (Business Intelligence), data management, and predictive analysis. As a proprietary software, companies need to pay to use it. A free university edition has been introduced for students to learn and use SAS.

SAS has a simple GUI which is easy to learn; however, a good knowledge of the SAS programming knowledge is required to make the most of the tool. SAS’s DATA step (The data step is where data is created, imported, modified, merged, or calculated) helps inefficient data handling and manipulation.

SAS’s Visual Analytics software is a powerful tool for interactive dashboards, reports, BI, self-service analytics, Text analytics, and smart visualizations. SAS is widely used in the pharmaceutical industry, BI, and weather forecasting.

SAS’s data analytics process is as shown:

SAS’s data analytics process


  • Simple user interface and easy to learn
  • Free university edition for students does exist
  • 24x7 customer support


  • Proprietary software that requires payment

You can learn more about SAS here.



4. Excel




  • Widely popular piece of software available on most office systems
  • Easy to pick up and use for basic analysis
  • Good for performing statistical analysis
  • Used by more than 750 million users across the world

Excel is a spreadsheet and a simple yet powerful tool for data collection and analysis. Excel is not free, as it comes as a part of the Microsoft Office “suite” of programs. It is also readily available, widely used and easy to learn and start data analysis with.

The Data Analysis Toolpak in Excel offers a variety of options to perform statistical analysis of your data. The charts and graphs in Excel give a clear interpretation and visualization of data. The Analysis Toolpak feature needs to be enabled and configured in Excel, as seen here:

Configured in Excel

Once the Toolpak has been set up, you will see the list of tools. You can choose the tool based on your goals and the information that you want to analyze.

Data Analysis Toolpak


  • Easy to organize data
  • Built-in formulae and calculation makes it easy to get started right away


  • Human error is very possible with the way that excel works
  • Not good for large-scale analysis as a business scales


5. Power BI

Power BI


  • Powerful business analytics tool
  • Three tiers, including one free one
  • Integrates with other tools
  • Companies that use Power BI include Nestle, Tenneco and Ecolab

Power BI is yet another powerful business analytics solution by Microsoft. It comes in three versions – Desktop, Pro, and Premium. The desktop version is free for users; however, Pro and Premium are priced versions.

With Power BI, you can bring your data to life with live dashboards and reports. You can visualize your data connect to many data sources and share the outcomes across your organization.

It also integrates well with other tools, including Microsoft Excel, so you can get up to speed quickly and work seamlessly with your existing solutions. Gartner has said that Microsoft is a Magic Quadrant Leader among analytics and business intelligence platforms.


  • Free version exists
  • Offers live dashboards and reports
  • Integrates well with other tools, including Microsoft Excel


  • Can be difficult for new users of such tools
  • Premium tier is expensive
  • Needs better connections to data sources not associated with Microsoft

To know more about Power BI, click here.


6. Tableau



  • Used for Business Intelligence analytics
  • Has drag and drop features
  • Fast analytics and mobile-friendly
  • Companies that use Tableau include Amazon, Barclays and Citibank

Tableau is a BI (Business Intelligence) tool developed for data analysts where one can visualize, analyze, and understand their data. The software is not free, and the pricing varies as per different data needs. On the plus side, it is easy to learn and deploy Tableau

Tableau can explore any type of data – spreadsheets, databases, and data on Hadoop and cloud services. It is also mobile friendly.


  • Easy to learn and deploy
  • Drag and drop features
  • Data visualization with smart dashboards can be shared within seconds


  • No free version
  • Static and single value parameters


Learn more about Tableau here.


7. Apache Spark

Apache Spark



  • Used for big data processing
  • Can run on Hadoop, Apache Mesos, standalone or in the cloud
  • High-performance
  • Companies that use Apache Spark include Uber, Slack and Shopify

Spark Is an integrated analytics engine for Big Data processing designed for developers, researchers, and data scientists. It is free, open-source and a wide range of developers contribute to its development

Spark is a high-performance tool and works well for batch and streaming data. It can also be used interactively from the Scala, Python, R, and SQL shells as well.

Spark includes libraries such as SparkSQL for SQL and structured data, MLlib for machine learning, SparkStreaming for live data stream processing, and GraphX for graph analytics.


  • High-performance
  • Can access a diverse set of data sources
  • Can be used interactively from the Scala, Python, R, and SQL shells


  • Software isn’t user-friendly
  • High memory usage
  • Doesn’t have much documentation

Learn more about Apache Spark here.


Which Data Analytics Tools Should You Pick?

As you can see, there’s a wide variety of data analytics tools to pick from. What you do end up choosing will be determined by what you need to analyze and your own particular skill set.


For example, if you want a powerful business analytics tool that has third party support, you might want to check Power BI. If you want something (sort of) free for more simple data analysis, then you might want Excel. If you work in the sciences, then Python and R are the ways to go.


The data analysis tools listed here should be a starting point if you’re looking to move ahead in your data analytics journey. The truth is that you need to first invest time in understanding your and/or your organization’s data needs. After that, you can scout for the best data analytics tools that fit your needs.


People are also reading:

Leave a comment

Your email will not be published