1- KNIME<\/strong><\/a><\/h3>\n![\"KNIME](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/11\/KNIME-data-science-bootcamp-in-silicon-valley-300x78.png\")
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KNIME<\/em> Analytics Platform is one of the most popular solutions for data-driven innovation. It helps you discover the hidden potential in the data, predict new futures, or derive fresh insights. With a wide range of integrated tools, the most comprehensive choice of advanced algorithms, hundreds of ready-to-run examples, and over a thousand modules, this is one of the best toolboxes for any data analysis<\/strong> professional.<\/p>\n2- Tableau Public<\/strong><\/a><\/h3>\n![\"Tableau](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/11\/Tableau-Public-data-science-bootcamp-in-silicon-valley.jpg\")
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It\u2019s<\/em> one of the highly effective data analysis tools<\/strong> with good functionalities and features. Tableau Public is considered exceptionally powerful in the business domain because it communicates insights via data visualization. It comes with a million row limit which offers a great working ground for tasks related to data analysis<\/strong>. With the help of Tableau\u2019s visuals, you can chalk out a hypothesis quickly, sanity check the instinct, and start exploring the data.<\/p>\n3- RapidMiner<\/strong><\/a><\/h3>\n![\"RapidMiner](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/11\/RapidMiner-data-science-bootcamp-in-silicon-valley-300x150.jpg\")
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This<\/em> data analysis<\/strong> tool works similar to KNIME i.e. through visual programming and can help you manipulate, analyze, and model data. RapidMiner helps data science teams to become more productive via an open-source platform for data preparation, machine learning, as well as, model deployment. It comes with a unified data science platform which expedites the development of complete analytical workflows in a single environment, thus improving efficiency dramatically.<\/p>\n4- OpenRefine<\/strong><\/a><\/h3>\n![\"OpenRefine](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/11\/OpenRefine-data-science-bootcamp-in-silicon-valley-300x76.jpg\")
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Formerly<\/em> GoogleRefine, OpenRefine can help you clean, transform, and extend even messy data. This data analysis<\/strong> tool comes with a number of clustering algorithms that help you explore massive datasets with ease. You\u2019d also be able to extend the data utilizing external data and web services. It supports lots of file formats to facilitate import and export.<\/p>\n5- R-Programming<\/strong><\/a><\/h3>\n![\"R-Programming](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/11\/R-Programming-data-science-bootcamp-in-silicon-valley.jpg\")
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The<\/em> popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis<\/strong> and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.<\/p>\nConclusion<\/em><\/strong><\/h3>\n![\"conclusion\"](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/08\/conclusion-225x300.jpg\")
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While<\/em> all the above-mentioned data analysis tools<\/strong> are designed to make your job a tad easier, they\u2019re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis<\/strong>, you should identify your own professional inclination first before you start learning these tools. Data analysis tools<\/strong> aren\u2019t only available in a huge number, they\u2019re highly diversified as well. And that\u2019s why it\u2019s crucial to determine the aspect of data analysis<\/strong> you want to head to.<\/p>\n
<\/p>\n2- Tableau Public<\/strong><\/a><\/h3>\n<\/p>\n
It\u2019s<\/em> one of the highly effective data analysis tools<\/strong> with good functionalities and features. Tableau Public is considered exceptionally powerful in the business domain because it communicates insights via data visualization. It comes with a million row limit which offers a great working ground for tasks related to data analysis<\/strong>. With the help of Tableau\u2019s visuals, you can chalk out a hypothesis quickly, sanity check the instinct, and start exploring the data.<\/p>\n3- RapidMiner<\/strong><\/a><\/h3>\n<\/p>\n
This<\/em> data analysis<\/strong> tool works similar to KNIME i.e. through visual programming and can help you manipulate, analyze, and model data. RapidMiner helps data science teams to become more productive via an open-source platform for data preparation, machine learning, as well as, model deployment. It comes with a unified data science platform which expedites the development of complete analytical workflows in a single environment, thus improving efficiency dramatically.<\/p>\n4- OpenRefine<\/strong><\/a><\/h3>\n<\/p>\n
Formerly<\/em> GoogleRefine, OpenRefine can help you clean, transform, and extend even messy data. This data analysis<\/strong> tool comes with a number of clustering algorithms that help you explore massive datasets with ease. You\u2019d also be able to extend the data utilizing external data and web services. It supports lots of file formats to facilitate import and export.<\/p>\n5- R-Programming<\/strong><\/a><\/h3>\n<\/p>\n
The<\/em> popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis<\/strong> and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.<\/p>\nConclusion<\/em><\/strong><\/h3>\n<\/p>\n
While<\/em> all the above-mentioned data analysis tools<\/strong> are designed to make your job a tad easier, they\u2019re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis<\/strong>, you should identify your own professional inclination first before you start learning these tools. Data analysis tools<\/strong> aren\u2019t only available in a huge number, they\u2019re highly diversified as well. And that\u2019s why it\u2019s crucial to determine the aspect of data analysis<\/strong> you want to head to.<\/p>\n
<\/p>\n3- RapidMiner<\/strong><\/a><\/h3>\n<\/p>\n
This<\/em> data analysis<\/strong> tool works similar to KNIME i.e. through visual programming and can help you manipulate, analyze, and model data. RapidMiner helps data science teams to become more productive via an open-source platform for data preparation, machine learning, as well as, model deployment. It comes with a unified data science platform which expedites the development of complete analytical workflows in a single environment, thus improving efficiency dramatically.<\/p>\n4- OpenRefine<\/strong><\/a><\/h3>\n<\/p>\n
Formerly<\/em> GoogleRefine, OpenRefine can help you clean, transform, and extend even messy data. This data analysis<\/strong> tool comes with a number of clustering algorithms that help you explore massive datasets with ease. You\u2019d also be able to extend the data utilizing external data and web services. It supports lots of file formats to facilitate import and export.<\/p>\n5- R-Programming<\/strong><\/a><\/h3>\n<\/p>\n
The<\/em> popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis<\/strong> and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.<\/p>\nConclusion<\/em><\/strong><\/h3>\n<\/p>\n
While<\/em> all the above-mentioned data analysis tools<\/strong> are designed to make your job a tad easier, they\u2019re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis<\/strong>, you should identify your own professional inclination first before you start learning these tools. Data analysis tools<\/strong> aren\u2019t only available in a huge number, they\u2019re highly diversified as well. And that\u2019s why it\u2019s crucial to determine the aspect of data analysis<\/strong> you want to head to.<\/p>\n
<\/p>\n4- OpenRefine<\/strong><\/a><\/h3>\n<\/p>\n
Formerly<\/em> GoogleRefine, OpenRefine can help you clean, transform, and extend even messy data. This data analysis<\/strong> tool comes with a number of clustering algorithms that help you explore massive datasets with ease. You\u2019d also be able to extend the data utilizing external data and web services. It supports lots of file formats to facilitate import and export.<\/p>\n5- R-Programming<\/strong><\/a><\/h3>\n<\/p>\n
The<\/em> popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis<\/strong> and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.<\/p>\nConclusion<\/em><\/strong><\/h3>\n<\/p>\n
While<\/em> all the above-mentioned data analysis tools<\/strong> are designed to make your job a tad easier, they\u2019re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis<\/strong>, you should identify your own professional inclination first before you start learning these tools. Data analysis tools<\/strong> aren\u2019t only available in a huge number, they\u2019re highly diversified as well. And that\u2019s why it\u2019s crucial to determine the aspect of data analysis<\/strong> you want to head to.<\/p>\n
<\/p>\n5- R-Programming<\/strong><\/a><\/h3>\n<\/p>\n
The<\/em> popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis<\/strong> and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.<\/p>\nConclusion<\/em><\/strong><\/h3>\n<\/p>\n
While<\/em> all the above-mentioned data analysis tools<\/strong> are designed to make your job a tad easier, they\u2019re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis<\/strong>, you should identify your own professional inclination first before you start learning these tools. Data analysis tools<\/strong> aren\u2019t only available in a huge number, they\u2019re highly diversified as well. And that\u2019s why it\u2019s crucial to determine the aspect of data analysis<\/strong> you want to head to.<\/p>\n
<\/p>\nConclusion<\/em><\/strong><\/h3>\n<\/p>\n
While<\/em> all the above-mentioned data analysis tools<\/strong> are designed to make your job a tad easier, they\u2019re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis<\/strong>, you should identify your own professional inclination first before you start learning these tools. Data analysis tools<\/strong> aren\u2019t only available in a huge number, they\u2019re highly diversified as well. And that\u2019s why it\u2019s crucial to determine the aspect of data analysis<\/strong> you want to head to.<\/p>\n
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