Top 10 Data Analytics Tools

The level of demand and importance of analytical data on the market has produced many openings throughout the world. It becomes a little difficult to avoid the top data analysis tool as an open source tool is more popular, user friendly and performance oriented than the paid version. There are many open source tools that don’t need a lot / every coding and successfully produce better results than the paid version of MIS. – Programming in data mining and public tables, Python in data visualization. Below is a list of the top 10 data analysis tools, both open source and paid versions, based on their popularity, learning and performance.

1. R Programming

R is a leading analytical tool in the industry and is widely used for statistics and data modeling. It can easily manipulate your data and come in different ways. It has surpassed SAS in many ways such as data capacity, performance and results. R compiles and runs on various Viz platforms, Windows and Maco. It has 11,556 packages and allows you to browse packages by category. R also provides tools to automatically install all packages according to user requirements, which can also be assembled with large data.

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2. Public Tableau:

Public Tableau is a free software that connects any data source data warehouse data company, Microsoft Excel or web-based data, and makes data visualization, maps, dashboards etc. With real time updates that appear on the web. They can also be shared through social media or with clients. This allows access to download files in different formats. If you want to see the power of Tablo, then we must have a very good data source. The ability of Big Tableau data makes it important and one can analyze and visualize data better than other data visualization software on the market.

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3. Python.

Python is an easy-to-read oriented scripting language, writing, maintaining and is a free open source tool. It was developed by Guido Van Rossum in the late 1980s which supported functional and structured programming methods.

Python is easy to learn because it is very similar to JavaScript, Ruby, and PHP. Also, Python has a very good machine learning library that is. SciCitLearn, thano, tensorflow and hard. Another important feature of Python is to be assembled on any platform such as SQL Server, MongoDB or JSON database. Python can also handle text data very well.

4. SAS.

SAS is a programming environment and language for data manipulation and leaders in analytics, developed by the SAS Institute in 1966 and further developed in the 1980s and 1990s. SAS is easily accessible, can be managed and can analyze data from any source. SAS introduces a large set of products in 2011 for customer intelligence and many SAS modules for the web, social media and marketing analysis that are widely used for profiles and profile prospects. It can also predict their behavior, manage, and optimize communication.

5. Apache Spark.

The University of California, Lab Amp Berkeley, developed Apache in 2009. Apache Spark is a fast-scale data processing machine that is fast and executes applications in Hadoop Cluster 100 times faster and 10 times faster on disk. Spark is built on data science and its concept makes data science easy. Spark is also popular for data pipes and developing machine learning models.

Spark also includes a library – MLLIB, which provides a series of progressive machine algorithms for recurrent data science techniques such as classification, regression, collaborative, clustering, etc.

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6. Excel.

Excel is a basic, popular analytic tool and is widely used in almost all industries. Whether you are an expert in Sas, R or Tableau, you still need to use Excel. Excel is important when there are analytical requirements on the client’s internal data. This analyzes complex tasks that summarize data by previewing pivot tables that help multilter data according to client’s requirements.

Excel has the advance business analytics option which helps in modelling capabilities which have prebuilt options like automatic relationship detection, a creation of DAX measures and time grouping.

7. RapidMiner:

RapidMiner is a powerful integrated data science platform developed by the same company that performs predictive analysis and other advanced analytics like data mining, text analytics, machine learning and visual analytics without any programming. RapidMiner can incorporate with any 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 real-life data transformation settings, i.e. you can control the formats and data sets for predictive analysis.


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

9. QlikView

QlikView has many unique features like patented technology and has in-memory data processing, which executes the result very fast to the end users and stores the data in the report itself. Data association in QlikView is automatically maintained and can be compressed to almost 10% from its original size. Data relationship is visualized using colors – a specific color is given to related data and another color for non-related data.

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10. Splunk:

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


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