Most Popular Data Science Software Tools

Most Popular Data Science Software Tools

Most-Popular-Data-Science-Software Tools-nareshit

Most Popular Data Science Software Tools

Data science is a data-driven process which is helpful in extracting useful insight from various forms of data. Data science focuses on the present and future for decision making. Decision making is not easy when we are getting a huge amount of resources from various internal and external resources. To make informed decisions you must clean data and make it useful for decision making. There are many tools available that helps in extracting information from huge volumes of data. Let’s read about most popular data science.

Data Science Software Tools
  • is one of the most popular data science platforms that make streaming analytics simple and easy.
  • It’s an intelligent platform optimized for real time classification of streaming data from apps, devices and sensor.
  • Raw information gathered from various resources could be turned into usable information and apply machine learning for streaming information.
  • It simplifies machine learning process by addressing issues like scalability, security and infrastructure.
Apache Graph
  • Apache graph is used to perform graph processing on data. It uses Hadoop’s MapReduce implementation to process graph.
  • Giraph is an open-source project and it uses bulk synchronous parallel model of distributed computing.
  • Apache graph is best choice for unleashing the potential of structured datasets at massive scale.
  • Rapidminer is a software platform for data science that could be used for data prep, machine learning and predictive model deployment.
  • It offer features like code free workflow, seamless transformation of raw data into usable one.
  • With Rapideminer you can connect to any data source, any format, at any scale.
  • Data scientists can quickly discover patterns and issues with data quality.
  • Clean data for advanced algorithm, create optimal data that used for predictive analysis.

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  • Data robot offers an automated machine learning platform to transform raw data into organized data useful for predictive analysis.
  • DataRobot automates entire modeling lifecycle, enabling users to quickly and easily build accurate predictive models.
  • DataRobot automatically searches through millions of combination of algorithms, data preprocessing steps, transformations, features and tuning parameters for the best machine learning model.
  • DataRobot offers parallel modeling engine that can build and tune machine learning models.
  • The intuitive web-based interface allows anyone to interact with powerful platform irrespective of machine learning experience.
  • It offers best-in-class algorithms from R, Python, H20, Spark, and other sources, giving users the best set of analytics tools for predictive challenges.
Apache Hadoop
  • Apache Hadoop is an open source programming framework that is used for processing big data sets. It uses MapReduce programming model while dealing with big data.
  • Hadoop manges data whether it’s structured or unstructured and in any other format.
  • It’s extremely scalable where one can easily add new nodes for adding new volumes of data.
  • Hadoop uses parallel processing method that is the reason it can process high volumes of data with ease.
  • From small to large organizations any one could use Hadoop for data processing.
Above are some of the most popular data science tools that help in data processing. You can start working with these tools and transform huge meaningless data into useful one. It’s not easy to process huge data when you are getting information from internal and external resources. These tools with robust features make data processing simple, fast and accurate

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