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Top 5 Data Science Tools Available in the Market


Data Science is a broad field that deals with data in various ways. Thus, to finish your task successfully as a data scientist or IT expert, you need to be aware of the top Data Science tools available on the market. Thus, to get proper knowledge of these tools, it is necessary to have Data Science Online Training. Such training will help you with hands-on experience with such data science tools and make you proficient in them.

Data Science: Meaning

The technique of extracting meaningful insights from data is known as data science. However, in simple terms, it is the process of gathering, analyzing, and modeling data to address real-world issues. Moreover, its usage ranges from fraud and illness detection to recommendation engines and corporate growth. Furthermore, due to the use of several applications and rising demand, Data Science tools were developed.

Different Data Science Tools Available

The main advantage of data science tools is that they do not require the usage of programming languages to implement Data Science. Thus, they include pre-defined functions, algorithms, and a user-friendly graphical user interface. Moreover, you may use them to create complex machine learning models without the necessity of a programming language. Several start-ups and IT organizations have been attempting to provide such user-friendly solutions. Furthermore, it is such a broad subject that using a single tool for the entire workflow is rarely sufficient. List down below are the main Data Science tools that many professionals can use.

●     Statistical Analysis System (SAS)

SAS is a data science tool used for statistical operations. However, it is a closed-source proprietary program that many enterprises may use to analyze data. Moreover, SAS does statistical modeling using the SAS computer language. Many professionals and enterprises working on reliable commercial applications use this powerful tool. Furthermore, SAS provides several statistical libraries and tools that Data Scientists may employ to model and organize their data.

●     Apache Spark

Apache Spark, or Spark, is a robust analytics engine widely used in data science. However, the purpose of creating Apache Spark was to perform batch and stream processing. Moreover, it has a plethora of APIs that allow Data Scientists to have recurring access to data for Machine Learning, SQL storage, and so on.

●     BigML

BigML is another popular tool that offers a fully interactive, cloud-based GUI environment for processing Machine Learning Algorithms. However, BigML delivers standardized software for industrial requirements using cloud computing. Moreover, this tool is an expert in predictive modeling that employs a wide range of Machine Learning methods such as clustering, classification, time-series forecasting, and more.

●     MATLAB

MATLAB is a numerical computing environment that supports several methodologies. Also, you can use this tool to process mathematical data. Moreover, it is a software application that streamlines matrix operations, algorithmic implementation, and statistical data modeling. Furthermore, professionals can use this tool in multiple scientific subjects.

●     Excel

Excel is probably the most used Data Analysis tool for spreadsheet computations, data processing, visualization, and complex calculations. Thus, it is the most powerful analysis tool. In addition, Excel has a variety of formulas, tables, filters, slicers, and more, which enables you to design your functions.

●     Tableau

Tableau is a data visualization program with strong graphics for creating interactive visualizations. It focuses on industries involved in business intelligence.

However, the ability of Tableau to interact with databases, spreadsheets, Online Analytical Processing, cubes, and other systems is its most essential feature.


Hopefully, you find this article informative. We have compiled the top tools that provide the best solution to analyze data, create aesthetic and interactive visualizations, and develop powerful predictive models using machine learning algorithms. Therefore, to understand these tools and gain proper knowledge, it is necessary to go for proper training.

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