Top Data Science Tools in 2021

Let’s explore the best tools used by data scientists. The ranking of paid and free tools is based on popularity and performance.

Xplenty

Xplenty is data integration, ETL, and an ELT platform that can bring together all your data sources.

This is a comprehensive toolkit for building data pipelines. This elastic and scalable cloud platform can integrate, process, and prepare data for analysis in the cloud. Offers marketing, sales, customer service, and developer solutions.

Features:

The sales solution has the functionality to understand your customers, enrich data, centralize sales metrics and tools, and keep your CRM organized.

The customer support solution provides comprehensive information, helps you make better business decisions, customized support solutions, and automated upsell and cross-selling functions.

Xplenty’s marketing solution helps you create effective and comprehensive campaigns and strategies.

Xplenty includes the features of data transparency, easy migration, and connection to existing systems.

RapidMiner

RapidMiner is a tool for the entire predictive modeling lifecycle. It has all the functions for data preparation, model creation, validation, and delivery. Provides a graphical interface for connecting predefined blocks.

Features:

  • RapidMiner Studio is used for the preparation, visualization, and statistical modeling of data.
  • RapidMiner Server offers central repositories.
  • RapidMiner Radoop is intended for implementing big data analytics functions.
  • RapidMiner Cloud is a cloud-based repository.

Data robot

Data Robot is the automated platform for machine learning. It can be used by data scientists, software engineers, executives, and IT professionals.

Features:

  • It offers a simple deployment process.
  • It has an SDK for Python and an API.
  • It allows for parallel processing.
  • Optimizing the model.

Apache Hadoop

Apache Hadoop is an open-source framework. Simple programming models built with Apache Hadoop can perform distributed processing of large datasets on computer clusters.

Features:

It’s a scalable platform.

Errors can be recognized and managed at the application level.

It contains many modules like Hadoop Common, HDFS, Hadoop Map Reduce, Hadoop Ozone, and Hadoop YARN.

Trifacta

Trifacta offers three products for data processing and preparation. It can be used by organizations, individuals, and teams.

Features:

  • Trifacta Wrangler helps you explore, transform, clean and compose desktop files.
  • Trifacta Wrangler Pro is an advanced self-service data preparation platform.
  • Trifacta Wrangler Enterprise aims to strengthen the team of analysts.

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Alteryx

Alteryx provides a platform to find, prepare and analyze data. It also helps you gain deeper insights by providing and sharing analytics at scale.

Features:

  • It provides the functionality for data discovery and collaboration across the enterprise.
  • It has model preparation and analysis functions.
  • The platform allows you to centrally manage users, workflows, and databases.
  • It allows you to integrate R, Python, and Alteryx models into your processes.

KNIME

It is available for free. KNIME for Data Scientists helps you combine tools and data types. It’s an open-source platform. It allows you to use the tools of your choice and expand them with additional functions.

Features:

KNIME is very useful for repetitive and time-consuming aspects.

Try and extend it to Apache Spark and Big Data.

It can work with many data sources and different types of platforms.

Excel

Excel can also be used as a tool for data science. It’s an easy-to-use tool for the non-technical. It’s good for analyzing data.

Features:

  • It has good functions for organizing and summarizing data.
  • It allows you to sort and filter data.
  • It has conditional formatting features.

Matlab

Matlab gives you the solution to analyze data, develop algorithms and create models. It can be used for data analysis and wireless communication.

Features:

  • Matlab offers interactive applications that show you how different algorithms work with your data.
  • It has the ability to develop.
  • Matlab algorithms can be directly converted into C / C ++, HDL, and CUDA code.

Java

Java is an object-oriented programming language. Compiled Java code can run on any Java-compatible platform without the need to recompile it. Java is simple, object-oriented, architecture-independent, platform-independent, portable, multithreaded, and secure.

Features:

  • As resources, we’ll look at why Java is used for data science:
  • Java offers a number of tools and libraries useful for machine learning and data science.
  • Java 8 with Lambdas: Use it to develop large data science projects.
  • Scala supports data science.

Python

Python is a high-level programming language and offers a large standard library. It has the characteristics of object-oriented, functional, procedural, dynamic, and automatic memory management types.

Features:

It is used by data scientists because it has a good number of useful packages available for free download.

Python is extensible.

Offers free libraries for data analysis. To master in the Data Science visit Data Science Training in Pune

Additional Data Science Tools

R.

R is a programming language and can be used on UNIX, Windows, and Mac OS platforms.

SQL

This domain-specific language is used to manage RDBMS data programmatically.

Tableau

Tableau can be used by individuals, teams, and organizations. It can work with any database. It’s easy to use with its drag and drop functionality.

Cloud Dataflow

Cloud DataFlow is intended for both streaming and batch data processing. It is a fully managed service. It can transform and enrich data in stream and batch mode.

Kubernetes

Kubernetes offers an open-source tool. It is used to automate the deployment, sizing, and management of containerized applications.

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