Top 10 tools for data analysis

The growing demand and importance of data analytics in the market has generated many openings around the world. Mapping the best data analytics tools becomes a bit difficult as the open source tools are more popular, user-friendly and performance oriented than the paid version. There are many open source tools that do not require much / any coding and are able to deliver better results than paid versions, e.g. – R programming in data mining and Tableau public, Python in data visualization. Below is the list of top 10 data analytics tools, both open source and paid version, based on their popularity, learning and performance.

1. R programming

R is the leading analytics tool in the industry and is widely used for statistics and data modeling. It can easily manipulate your data and present in different ways. It has exceeded SAS in many ways such as data, performance and result capacity. R compiles and runs on a variety of platforms, namely -UNIX, Windows and MacOS. It has 11,556 packages and allows you to browse the packages by categories. R also provides tools to automatically install all packages as per. User requirements, which can also be combined well with Big data.

2. Tableau Public:

Tableau Public is a free software that connects any data source, be it corporate data warehouse, Microsoft Excel or web-based data, and creates data visualizations, maps, dashboards, etc. with real-time updates presented on the web. They can also be shared via social media or with the client. It provides access to download the file in various formats. If you want to see the power of tableau, we need a very good data source. Tableau’s Big Data capabilities make them important, and data can be analyzed and visualized better than any other data visualization software on the market.

3. Python

Python is an object-oriented scripting language that is easy to read, write, maintain and is a free open source tool. It was developed by Guido van Rossum in the late 1980s, which supports both functional and structured programming methods.

Python is easy to learn as it is very similar to JavaScript, Ruby and PHP. Python also has very good machine learning libraries, ie. Scikitlearn, Theano, Tensorflow and Keras. Another important feature of Python is that it can be assembled on any platform such as SQL server, a MongoDB database or JSON. Python can also handle text data very well.

4. SAS

Sas is a programming environment and language for data manipulation and a leader in analytics, developed by the SAS Institute in 1966 and further developed in the 1980s and 1990s. SAS is easily accessible, manageable and can analyze data from all sources. SAS introduced a large set of products in 2011 for customer intelligence and several SAS modules for web, social media and marketing analysis, which are widely used for profiling customers and prospects. It can also predict their behavior, manage and optimize communication.

5. Apache spark

The University of California, Berkeley’s AMP Lab, developed Apache in 2009. Apache Spark is a fast, large data processing engine and executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. Spark is built on data science and its concept makes data science effortless. Spark is also popular for developing data leads and machine learning models.

Spark also includes a library – MLlib, which provides a progressive set of machine algorithms for repetitive data science techniques such as classification, regression, collaborative filtering, clustering, etc.

6. Excel

Excel is a basic, popular and widely used analytical tool in almost every industry. Whether you are an expert in Sas, R or Tableau, you still need to use Excel. Excel becomes important when there is a requirement for analysis of the client’s internal data. It analyzes the complex task that summarizes the data with an example of pivot tables that help filter the data as per. Client requirements. Excel has the potential business analytics capability that helps model capabilities that have pre-built capabilities such as automatic relationship detection, DAX goals creation, and time groups.

7. RapidMiner:

RapidMiner is a powerful integrated data science platform developed by the same company that performs predictive and other advanced analytics such as data mining, text analysis, machine learning and visual analysis without any programming. RapidMiner can incorporate with all data source types including Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, Ingres, MySQL, IBM SPSS, Dbase etc. The tool is very powerful that can generate analytics based on reality settings in data transformation, i.e. You can check formats and data sets for predictable analysis.


KNIME Developed in January 2004 by a team of software engineers at the University of Konstanz. KNIME is a leading open source, reporting and integrated analytics tool that allows you to analyze and model the data through visual programming, integrating various data mining and machine learning components through its modular data pipelining concept.

9. QlikView

QlikView has many unique features such as patented technology and has in-memory data processing which performs the result very quickly to end users and stores the data in the report itself. QlikView data association is automatically maintained and can be compressed to almost 10% from its original size. Data conditions are visualized using colors – one color is given for related data and another color is given for unrelated data.

10. Splunk:

Splunk is a tool that analyzes and searches machine-generated data. Splunk pulls all text-based log data and provides a simple way to search through them, a user can pull in all kinds of data and perform all kinds of interesting statistical analysis on them and present them in different formats.