Want to know how modern and digital businesses manage to target the right customer profile to market their products or services? They do it by extracting valuable insights from data using various data science tools and models. In today’s connected world, data science is no longer a technical buzzword – but is being implemented by business enterprises and IT companies
Thanks to the growing popularity and adoption of data science, various solution providers have come up with easy and user-friendly data science tools that can be used to design and build complex data models. The best part of using these tools is that you do not need expertise in programming languages – and are built with a variety of predefined functions and algorithms.
As a result, businesses can choose from a variety of data science tools that are used for functions like data storage, data analysis, data modeling, and data visualization – depending on their business requirements.
Which are the top data science tools that data scientists commonly use to collect and transform data for a better decision-making process?
Let us now look at 10 popular Data Science Tools that are being used in the coming future.
With its collection of free and open-source software tools, Apache Hadoop is a framework that can resolve issues related to the storage of massive data volumes and processing. It can facilitate the storage and processing of Big Data using MapReduce programming. Used for high-volume data computation and processing, Apache Hadoop allows distributed processing of datasets across network clusters – and is designed to high scalability from a few to thousands of connected machines.
Like Apache Hadoop, Apache Spark (or simply Spark) is an open-source and distributed data science tool – used primarily as a cluster computing framework. Designed for machine learning or ML-related applications, Spark is built with multiple ML APIs that can be used to easily design ML models. Based on MapReduce, Spark extends the MapReduce model for more number and speed of computations in stream processing and interactive querying.
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As an effective platform for data science, RapidMiner provides an efficient environment for integrating data preparation, deep learning, machine learning, text mining, and predictive analytics. Thanks to its overall functionality, RapidMiner is ranked 1 by Gartner in its magic quadrant for data science platforms. RapidMiner offers an all-in-one platform for the entire data modeling – starting from data preparation to model building and deployment.
Azure HDInsight is the popular cloud offering from Microsoft that is designed to process high volumes of streaming and historic data. As a cloud-based platform, Azure HDInsight can be used for data storage, data processing, and analytics. It can also be easily integrated with Apache Hadoop and Spark clusters for the purpose of data processing. Along with being cost-effective and scalable, HDInsight offers data security (with Azure Virtual Network) and cluster monitoring with its integration with Azure Monitor.
As a free and open-source platform, H20.ai is a global leader in artificial intelligence (AI) and machine learning (ML) applications. H20 has been successfully used to implement AI in diverse industries including financial services, insurance, and retail. H20 supports a range of ML algorithms such as gradient machines, generalized models, and deep learning. As a user-friendly data science tool, H20 is designed to simplify data modeling – and has a growing online community of data scientists and AI-adopting organizations.
Among the leading tools for data scientists, DataRobot is used extensively as an AI and machine learning platform – to develop advanced predictive models. This platform simplifies the use of ML algorithms for data clustering and regression. With its enterprise-wide AI implementation, DataRobot is used by many business stakeholders including data scientists, business analysts, and IT teams – to extract deep business value from large volumes of data.
Among the popular data visualization tools used by business enterprises, Tableau is a tool that is also used for data science and business intelligence (or BI). As a visualization tool, Tableau uses visual tools to represent data and showcase its insights. Other capabilities of this tool include real-time data analytics and support on cloud platforms. Among its many functionalities, Tableau can be integrated with multiple data sources and process their raw and unstructured data into a structured and organized form.
As the name suggests, BigML is a cloud-powered GUI tool that is used to process ML algorithms. With its web-based interface, this tool allows you to use the BigML.io REST APIs to customize your machine learning models. It also enables interactive data visualizations along with the ability to share visual elements on mobile or IoT-enabled devices. With BigML, you can develop predictive models that come with interactive visualizations and can be exported on any computing device.
Short for Matrix Laboratory, MATLAB is a widely-used programming language in computational mathematics. With its built-in graphics, this language facilitates in visualizing data and deriving insights from it. MATLAB can also be used to run analysis on large datasets that can be scaled up to cluster and cloud platforms. As a data science tool, MATLAB is used to simulate neural networks and perform functions like data cleaning and analysis – and developing deep learning algorithms.
As a metadata-driven platform, PowerCenter from Informatica is a data integration tool that can quickly deliver business data to organizations. Its data integration capability is based on the ETL – or Extract, ‘Transform, Load – architecture. Using PowerCenter, businesses can design the entire data integration lifecycle – from the first data project to deploying mission-critical applications. Additionally, businesses can use machine learning to monitor PowerCenter installations across different domains and locations.
As discussed in this article, there are different types of data science tools that are used for a variety of data-related functions including data storage, integration, and visualization. We have outlined 10 of the most popular tools and platforms that are currently being used by global businesses.
What do you make of this list of tools used in data sciences? Have we missed out on any other useful and popular tool that is widely used? Let us know by leaving behind your comments below.