Top Data Science Tools

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

Data Science Tools - Introduction

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.

#1 Apache Hadoop

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.

Key features:

• Supports thousands of Hadoop-based clusters.
• Use of Hadoop Distributed File System (or HDFS) for distributed and parallel processing of massive data volumes.
• Supports the Hadoop YARN framework used for job scheduling and cluster management
• Supports other Hadoop-related projects including the web-based tool, Ambari, Hive (data warehousing), and Avro (data serialization). 

#2 Apache Spark

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.

Key features:

• 100 times faster processing of data workloads
• Supports a combination of SQL, data streaming, and analytics.
• Can be run on a standalone cluster mode or on any cloud environment.
• Includes SQL module library that can query structured data within Spark programs.
• Features the DataFrame API that can be used to easily collect data from various sources including Hive, JSON, and JDBC.

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#3 RapidMiner

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.

Key features:

• GUI-based tool interface with predefined blocks.
• Support for data partitioning and data access
• Use of a visual workflow designer tool to design analytics models
• Data exploration functions including descriptive statistics, visualization, and graphs
• Seamless integration with third-party tools like Cloudera, MapR, Talend, and DataStax.

 #4 Microsoft Azure HDInsight

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.

Key features:

• Optimized cluster creation for Apache Hadoop, Spark, Kafka, HBase, and more frameworks.
• Enterprise-wide data protection using Azure Directory services.
• Supports the use of Microsoft Azure Blob storage system for managing data across multiple nodes.
• Built with Microsoft R Server for executing statistical analysis and building ML models.
• Seamless integration with other Microsoft Azure services including Data Factory and Data Lake Storage

#5 H2O.ai

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.

Key features:

• Built using popular programming languages like Python and R.
• H20 Driverless or Automatic AI that includes automatic engineering and machine learning.
• Offers the Sparkling Water open source integration with Apache Spark.
• Integration with Apache Hadoop for analysis of large data volumes.
• Real-time data scoring
• User-friendly and web-based UI

#6 DataRobot

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.

Key features:

• Support of parallel processing that powers multiple servers to perform data analysis and modeling simultaneously.
• The fast building, testing, and training of ML models
• Simplifies model evaluation using techniques like parameter tuning.
• Easy model deployment and optimization
• Deployment of advanced predictive models in a few minutes on the DataRobot Cloud platform

#7 Tableau

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.

Key features:

• Provides a suite of Tableau products including Tableau Desktop, Reader, and Server.
• Used to visualize massive volumes of data from multiple data sources – and detect correlations and data patterns.
• Useful in solving complex data-related problems using its cross-database join feature.
• User-friendly interface and designed for users without programming skills
• Ability to drill down visualization charts and elements to explore insights at a deeper level.

#8 BigML

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.

Key features:

• Provides robust ML algorithms that are useful in solving real-world problems – through a single and standardized framework.
• Immediate access to machine learning models in a matter of seconds – that can be deployed to the cloud (or on-premises) quickly using BigML’s easy web interface.
• A transparent and collaborative platform for team members and better project management
• Effective automation of predictive modeling tasks
• Flexible deployment with BigML ported on any cloud provider or on a virtual private cloud.

#9 MATLAB

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.

Key features:

• Enables statistical data modeling and execution of algorithms.
• Detailed data visualization using the MATLAB graphics library
• Image and signal processing 
• Integration with other programming languages – thus enabling the deployment of data algorithms and applications across enterprise systems.
• Support for data import and analysis including large data files.

#10 Informatica PowerCenter

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.

Key features

• Data extraction from multiple data sources – along with data processing and transformation for loading into data warehouses.
• Supports distributed processing along with adaptive load balancing and dynamic partitioning.
• Use of GUI-based and codeless tools with pre-built transformations
• Script-less automated testing and data validation – across development, testing, and production environments
• Advanced data transformation through the parsing of XML, JSON, and IoT data

Conclusion

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.