If you want to become a data professional, you must be familiar with multiple data mining tools, even though you would only use a few. This way, you’d be prepared for any challenge because every tool has its specific advantages.
You might ask, “where do I start?”
To help you there, we’ve assorted a list of the most prominent data mining tools. Read on and find out the various tools you should be familiar with to become a data professional:
If you are a beginner and interested to learn more about data science, check out our data science courses from top universities.
Top Data Mining Tools
Sinsense had won the award for the best business intelligence software in 2016 due to its numerous features. Many people argue it’s still among the top data mining tools for professionals. If you’re looking for a solution to generate reports within an organization, this is the right choice for you.
Sisense enables you to combine data from multiple sources and build a shared repository. It also allows you to refine data to create visually rich reports to make your findings more comfortable to read for a non-technical audience.
It also has different widgets to make report generation easier. You can produce line charts, graphs, pie charts, and similar reports to visualize your data.
Find out more about Sisense.
KNIME is an open-source integration platform for data reporting and analytics. It is based on the modular data pipeline concept and works accordingly. It has multiple data mining and machine learning components embedded with each other.
Its main highlight is its ability to deploy quickly and scale efficiently. People who aren’t much familiar with the technical details of data analytics can use KNIME for predictive analysis too (although, they’d face some difficulty). KNIME is perfect for financial data analysis, business intelligence, and data analysis. From merging your data to deploying it, this platform can help you with nearly all of your data mining requirements.
Find out more about KNIME.
3. IBM SPSS Modeler
IBM SPSS Modeler enables you to perform text analytics and data mining. IBM had acquired it from SPSS.
The SPSS Modeler gets rid of many technical aspects from data transformations, so using predictive models becomes much more comfortable. Its interface has simplified working with data mining algorithms. It has two versions available, Professional and Premium. The Premium version has additional features, such as entity analytics and text analytics.
Find out more about IBM SPSS Modeler.
4. Rapid Miner
One of the most popular data mining tools among professionals is Rapid Miner. It is written in Java and provides you with an integrated environment for predictive analysis, text mining, machine learning, and relevant tasks.
Rapid Miner finds applications in numerous industries such as research, training, machine learning, business applications, and others. You can use this tool on-premise as well as on cloud infrastructure. It has templates for frameworks to reduce the occurrence of errors and increase the speed of your project.
Find out more about Rapid Miner.
Orange is an open-source solution for data mining and machine learning. Its components (called widgets) help you in various tasks, including but not limited to reading data, training the predictors, data visualization, and showing the data table.
It can format the data coming to it in the required pattern, and you can move that to any desired location by using the widgets. Through its multi-functional widgets, Orange allows users to perform data mining tasks in a short time with high efficiency. Learning to use Orange is quite fun too, so if you’re a beginner, you can start your data mining journey with this tool without hesitation.
Find out more about Orange.
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Python is among the most popular programming languages on the planet. To use most of the data mining tools we’ve discussed in this article, you must learn this language. Python has an easy to learn syntax and quite flexible applicability.
A significant reason for its vast popularity is the vast amount of libraries for this language. After learning Python, you can use its libraries for data analysis as well as app development. Such is the versatility of this programming language.
Find out more about Python .
Rattle is a data mining tool that uses R as its primary programming language. It is GUI based and provides multiple data mining functionalities. Its log code tab creates duplicate code for all the activities that happen at the GUI. So, you can either use the UI to perform multiple tasks or use the code and edit it accordingly. You have the option of reviewing the code as well.
Find out more about Rattle.
A prominent name for Teradata is Teradata Database. It provides insights into your company’s crucial data, such as product placement, sales, and others. You differentiate between cold and hot data too, which are separated according to the frequency of their use.
All the server nodes of Teradata have their processing abilities and memory, so it works on a ‘not sharing’ architecture.
Find out more about Teradata.
A product of the University of Waikato, NZ, Weka is a fantastic solution for data visualization, data analysis, and predictive modeling. It has an easily accessible user interface which makes learning the software quite simple. You can use Weka to access SQL databases as it enables database connectivity for enhanced processing.
Because Weka is free software, it is also among the most data mining tools among beginners and professionals alike.
Find out more about Weka.
Also Read: Data Science Tools
Learn More About Data Mining
So there you have it, the list of all the prominent data mining tools. If you have any questions regarding this list or the topic, feel free to ask us.
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