TOP 10 DATA SCIENCE PLATFORMS AND TOOLS OF 2020

16Mar - by aiuniverse - 0 - In Data Science

Source: analyticsinsight.net

Data Science has demonstrated to be a boom to both the IT and the business. The innovation incorporates getting value from information, understanding the information and its examples and afterwards foreseeing or producing results from it. Data science is much popular by organizations to analyze their enormous volume of data sets and generate optimized business insights from them, in this manner expanding profits for the organization.

Picking the correct seller and solution can be an entangled procedure, one that requires in-depth research and regularly boils down to something other than the solution and its technical abilities. To make your hunt somewhat simpler, we’ve profiled the best data science platforms and tools.

Altair

Altair Knowledge Works (some time ago Datawatch) offers an advanced data mining and predictive analytics workbench called Knowledge Studio. The product includes licensed Decision Trees, Strategy Trees, and a work process and wizard-driven graphical UI. It additionally incorporates capacities for data preparation tasks, visual data profiling, advanced predictive modeling, and in-database analytics. Users can import and export using common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS.

Mozenda

Mozenda is an enterprise cloud-based web-scraping platform. It assists organizations with gathering and sorting out web information most productively and cost-effectively possible. The tools have a point-to-click interface with an easy to understand UI. The tools have two sections: an application to construct the data extraction project and Web Console to run agents, organize results, and export data. It is anything but difficult to incorporate and permits users to distribute results in CSV, TSV, XML, or JSON format. The tools additionally give API access to get information and have inbuilt storage integrations like FTP, Amazon S3, Dropbox, and much more.

Anaconda

Anaconda is an open-source Python and R data science platform. The tool empowers you to perform data science and machine learning on Linux, Windows, and Mac OS. The platform permits users to download in excess of 1,500 Python and R data science packages, oversee libraries, dependencies, and environments, and analyze data with Dask, NumPy, pandas, and Numba. You would then be able to imagine results produced in Anaconda with Matplotlib, Bokeh, Datashader, and Holoviews.

Octoparse

Octoparse is a customer- side web scraping programming for Windows. It is a web-scraping template that transforms unstructured or semi-structured information from sites into an organized data set without coding. It is helpful for individuals who are not knowledgeable about programming. A web scraping layout is a simple yet amazing element. Its motivation is to input the target website/keywords in the parameters on the pre-formatted tasks, so the user doesn’t need to design any scraping rules nor composing code.

Databricks

Databricks offers a cloud and Apache Spark-based brought together analytics platform that joins data engineering and data science functionality. The platform uses a variety of open source languages and incorporates exclusive highlights for operationalization, performance and real-time enablement on Amazon Web Services. A Data Science Workspace empowers users to explore data and build models collaboratively. It additionally gives single click access to preconfigured ML conditions for augmented machine learning with popular frameworks.

OnBase

OnBase is a tool created by Hyland, is a single enterprise information platform that is intended to deal with user’s content, procedures, and cases. The tool essentially brings together user’s business content in a protected area and afterwards conveys important data to the user when they need it. OnBase permits the enterprise to turn out to be progressively agile, efficient, and capable, subsequently increasing productivity, delivering excellent customer service, and reduce risk across their enterprise.

KNIME Analytics Platform

KNIME makes understanding the information and designing data science workflows and reusable components available to everybody by being natural, open, and ceaselessly integrating new developments. KNIME permits the user to browse 2000 nodes to build workflow, model each step of the analysis, control the flow of data, and ensures the work is updated. The product likewise mixes tools from various areas with KNIME native nodes within a single workflow, incorporating scripting in machine learning, Python or R, or connectors to Apache Spark.

Dataiku

Dataiku offers an advanced analytics solution that permits companies to make their own data tools. The organization’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified structure for advancement and deployment gives prompt access to all the features expected to plan data tools without any preparation. Users would then be able to apply machine learning and data science systems to build and deploy predictive data flows.

Rapid Miner

Fast Miner is a data science platform developed fundamentally for non-programmers and analysts for quick analysis of information. The user has a thought in their brain, and effectively makes processes, import data into them, run them over and throw a prediction model. The tool supports importing ML models as well as to web applications like flask or nodeJS, android, iOS, and more, thereby unifying the entire spectrum of the Big Data Analytics Lifecycle.

DataRobot

DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is controlled by open-source algorithms and can be utilized on-prem, in the cloud or as a completely overseen AI service. DataRobot incorporates three independent yet fully integrated tools (Automated Machine Learning, Automated Time Series, MLOps), and each can be deployed in different manners to coordinate business needs and IT necessities.

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