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Most Used Data Science Tools for Beginners

Revolutionary technologies like Machine Learning and Artificial Intelligence have a methodology and processes behind them – Data Science. There are a multitude of data science tools that have made implementing AI easier and more scalable. In this article, we will be discussing the most used tools for Data Science tools for beginners.

The main advantage of using these tools is that it allows you to implement data science without using programming languages. They come with predefined algorithms, functions, and a user-friendly GUI. That is why these tools are used for building convoluted machine learning models.

There have been several tech giants and start-ups working on creating user-friendly data science tools that can be used by beginners. Here are a few examples of such tools:

1.    QlikView

This is another tool used for data visualization that is used by over 24,000 organizations all over the world. It is an effective platform that can visually analyze data for deriving useful business insights. Here are the features of QlikView that you can use:

  • It provides visualizations for creating detailed reports and dashboards that allow the viewers to have a clear understanding of the data.
  • It offers in-memory data processing that creates reports and helps in communicating them to the end-users.

2.    Tableau

This is among the most popular tools used for data visualization in the market. Through this tool, you will be able to break down unformatted, raw data into an understandable and processable format. Using Tableau to create visualizations can help you in understanding the dependencies between predictor variables. Check out the features of Tableau:

  • The tool can be used for connecting to different data sources and visualizing massive data sets to find patterns and correlations.
  • Through the Tableau Desktop feature, you will be able to create customized dashboards and reports and get real-time updates.
  • The tool also offers cross-database join functionality that helps in creating join tables and calculated fields and solving complex data-driven problems.

3.    DataRobot

This is an AI-driven automation platform that can help in developing accurate predictive models. It makes it easy for data scientists to implement different ML algorithms including classification, clustering, and regression models. Here are a few features of this tool:

  • It supports parallel programming by using thousands of servers by performing simultaneous data analysis, validation, data modeling, and so on.
  • It is used for building, testing, and training ML models at a fast speed. The tool tests the models on different use cases and compares the results to find out which model gives the most accurate predictions.
  • The tool is used for implementing machine learning at a large scale. It can be used for making model evaluation effective and easier. It implements parameter tuning and other validation techniques.


This company has developed open-source ML products such as H2). There are about 14,000 organizations and 130,000 data scientists in the community. It is an open-source tool that can make data modeling easier. Here are a few of its features:

  • The tool was built using Python and R- the most sought-after programming languages in the field of Data Science. Since most of the data scientists and developers are already familiar with both these languages, it will be easier to implement machine learning.
  • It can help in implementing several ML algorithms such as Classification Algorithms, Generalized Linear Models (GLM), Boosting Machine Learning, and more. It can also support Deep Learning.
  • The tool can provide support for integrating with Apache Hadoop for processing and analyzing large data volumes.

5.    RapidMinder

When it comes to implementing Data Science, RapidMiner is among the most popular tools. According to the Gartner Magic Quadrant for Data Science Platforms 2017, it was at the number one position. Here are a few features of this tool:

  • A single platform to process data, build and deploy ML models.
  • Offers support to integrate Hadoop framework with the in-built RapidMinder Radoop.
  • Model ML algorithms through a visual workflow designer. It can be used for generating predictive models using automated modeling.

6.    Informatica PowerCenter

The popularity of Informatica can be explained by the fact that their revenue has reached $1.05 billion. It has various products that help in data integration. However, PowerCenter is the one that has the strongest data integration capabilities. Check out the features of this tool:

  • This data integration tool is based on the Extract Transform Load (ETL) architecture.
  • It helps in data extraction from different sources, transform it, and process it on the basis of the business requirements and load or deploy it into a warehouse.
  • It supports grid computing, distributed processing, dynamic partitioning, pushdown optimization, and grid computing.

7.    Microsoft HD Insights

AzureHD Insight, a cloud platform, is a service provided by Microsoft for storing, processing, and analyzing data. Enterprises like Jet, Adobe, and Milliman have used Azure HD Insights for processing and managing large volumes of data. Here are a few features of this tool:

  • Full-fledged support for integrating with Spark clusters and Apache Hadoop for data processing.
  • The default storage for Microsoft HD Insights is Windows Azure Blob. It can manage sensitive data across different nodes effectively.
  • Offers Microsoft R Server for supporting enterprise-scale R to perform statistical analysis and build strong ML models.

8.    Apache Hadoop

This is a free and open-source framework capable of storing and managing tons and tons of data. It will provide you distributed computing of a dataset that is used for high-level data processing and computations. Here are a few features of Apache Hadoop:

  • Scale large data effectively on thousands of Hadoop clusters.
  • With the Hadoop Distributed File System (HDFS), you can store data. It distributed the large volume of data across nodes for parallel and distributed computing.
  • The functionality of data processing models like Hadoop YARN, Hadoop MapReduce, and others.

These are just some of the many tools that can be used for beginners in the field of Data Science. In order to become an expert in these tools and start your journey in the field, you can join a data science course in Chennai.

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