Top Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Below is the list of some popular tools explain briefly:

• SAS :SAS was a programming language developed by the SAS Institute for performed advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. , SAS was developed for very specific uses and powerful tools are not added every day to the extensive already existing collection thus making it less scalable for certain applications.
• Microsoft Excel :It is an important spreadsheet application that can be useful for recording expenses, charting data, and performing easy manipulation and lookup and or generating pivot tables to provide the desired summarized reports of large datasets that contain significant data findings. It is written in C#, C++, and .NET Framework, and its stable version was released in 2016.
• R :It is one of the leading programming languages for performing complex statistical computations and graphics. It is a free and open-source language that can be run on various UNIX platforms, Windows, and macOS. It also has a command-line interface that is easy to use. However, it is tough to learn especially for people who do not have prior knowledge about programming.
• Python:It is a powerful high-level programming language that is used for general-purpose programming. Python supports both structured and functional programming methods. Its extensive collection of libraries make it very useful in data analysis. Knowledge of Tensorflow, Theano, Keras, Matplotlib, Scikit-learn, and Keras can get you a lot closer to your dream of becoming a machine learning engineer.
• Tableau Public:Tableau Public is free software developed by the public company “Tableau Software” that allows users to connect to any spreadsheet or file and create interactive data visualizations. It can also be used to create maps, dashboards along with real-time updation for easy presentation on the web. The results can be shared through social media sites or directly with the client making it very convenient to use.
• RapidMiner: RapidMiner is an extremely versatile data science platform developed by “RapidMiner Inc”. The software emphasizes lightning-fast data science capabilities and provides an integrated environment for the preparation of data and application of machine learning, deep learning, text mining, and predictive analytical techniques. It can also work with many data source types including Access, SQL, Excel, Tera data, Sybase, Oracle, MySQL, and Dbase.
• Knime :Knime, the Konstanz Information Miner is a free and open-source data analytics software. It is also used as a reporting and integration platform. It involves the integration of various components for Machine Learning and data mining through the modular data-pipe lining. It is written in Java and developed by KNIME.com AG. It can be operated in various operating systems such as Linux, OS X, and Windows.

Data is Everywhere, in sheets, in social media platforms, in product reviews and feedback, everywhere. In this latest information age it’s created at blinding speeds and, when data is analyzed correctly, can be a company’s most valuable asset. “To grow your business even to grow in your life, sometimes all you need to do is Analysis!”

In this article, we will explore What is Analysis of data? How it works, the types of data analysis, Tools required for data analysis.