Best Data Science Tools

  • Soumyaa Rawat
  • Aug 13, 2021
  • Big Data
Best Data Science Tools title banner

Learning about Data Science 

 

The way an atom is the building block of mass, data is the building block of research. However, this blog is going to represent how data is used in data science that has led to numerous developments in the field of business analytics and development. 

 

What is data science? Data science is the discipline of digging deep into humongous amounts of data that provides business-related insights for effective decision-making and better productivity. 

 

With applications varying from medical research to software engineering, data science has made its own mark in the way that nothing seems achievable without it. 

 

With the help of technology, data science has evolved to the extent that it now has the capacity to accommodate big data and analyze vast amounts of data in very short spans of time. Look at the link to learn more about the data analytics process.

 

Even though raw data might appear to be far from being analyzed, actionable insights can be successfully extracted from data science with the help of artificial intelligence technology and other technological tools. 

 

Data-driven insights have always been successful in leading mankind to better decisions and effective actions. Similarly, data science tools are equipped with machine learning algorithms that enable them to extract patterns from raw data that otherwise seem to be tough to analyze. 

 

(Must check: Data Science vs Machine Learning)

 

However, with the coming of data science tools, the analysis of raw data has become a much more efficient and cost-effective job. In this blog, we will be discovering the top 15 data science tools to understand how various corporations use these tools to make data science approachable and effective.  

 

Top 15 Data Science Tools

 

  1. NLTK

 

Natural Language Toolkit or NLTK is a data science tool that has become inevitable in the field of data science. By preprocessing text data for further analysis by ML models, NLTK is a leading data science tool for Python programs. 

 

With an easy-to-use framework comes a series of features that have advanced the field of data science altogether. Be it classification or tagging, this open-source tool has got it all! 

 

  1. D3js 

 

One of the rapidly budding data science tools is the D3js by Javascript. Data-Driven Documents or D3 js machine learning is an online data visualization framework that offers interactive features in data science. 

 

Launched in 2011, this data science tool offers the approach of a Document Object Model (DOM) which helps the user to manipulate documents based on raw data. 

 

A fast framework that supports large amounts of data, D3js is the right pick for data science!

 

  1. Julia 

 

A data science tool meant for automatic memory management, Julia is a data science tool powered by its own machine learning algorithms. Meant for statistical programming and data insights extraction, Julia is often seen as a fast and efficient means for venturing into the realm of machine learning. 

 

Unlike other traditional data science tools, the mechanism of Julia is constructed to work well in the case of machine learning. What’s more, Julia has the capability of translating data written in one language to another.   

 

(Suggested blog: Julia vs Python)

 

  1. Apache Flink 

 

Offering a multiple API platform, Apache Flink comes next in the list of the top 15 data science tools. 

 

A hassle-free user interface for computing unbounded and bounded data sets, this data science tool is renowned for its low latency levels (the ability to process through a huge amount of data without causing maximum delays). 

 

Launched in 2011, Apache Flink is surely the right pick if you are looking for a scalable application that can handle big data quite well. 

 

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  1. Python

 

A much-renowned data science tool and programming language, Python is all you need to know about when it comes to top data science tools. With multiple open-sourced libraries powered by it, Python is widely used by data scientists for everyday applications. 

 

Owing to its popularity and the dynamic language that it offers, Python offers data scientists the chance to unfold the secrets of data like no other tool. 

 

Not only has it helped data to be deciphered in an advanced way, but it has also led data scientists to succeed in the field of ML too.  

 

(Must read: Machine learning models)

 

  1. Jupyter

 

A web-based interface for data science, Jupyter Notebook Python comes with many uses - data cleaning, statistical configuration, data visualization, and machine learning. With the power of handling big data quite efficiently as compared with Python or any other tool, Jupyter is the real deal. 

 

A user-friendly environment and unified data management are some of the striking features of Jupyter that was established in 2015. 

 

  1. Hive

 

Data summarization, analysis, and query are some of the major highlights of this data science tool. Known as Apache Hive, Hive is a ‘data warehouse solution’ created by Facebook. 

 

Be it small chunks of data or big data analysis, Hive best works with data science by simply putting its features to use - data extraction, data cleaning, etc. 

 

Even when beginners are trying to explore data science and similar programming tools, Hive is the right choice for them.  

 

(Must read: Latest programming languages)

 

  1. MongoDB 

 

A NoSQL database, MongoDB is often considered to be one of the most potential data science tools in today’s time. Why? Well, its flexibility to work with versatile schemas for data and its high speed. 

 

Even though SQL databases are preferred over NoSQL databases, MongoDB is making a clear cut when it comes to data science tools. Data Science often requires tools that can scan through rich data consisting of invisible details and whatnot. 

 

To extract insights from such vast data, big data analytics with MongoDB is your destination if you are looking for real-time insights and actionable leads. 

 

  1. SAS 

 

SAS or Statistical Analytical System is another data science tool that helps data scientists to retrieve and analyze datasets for particular purposes. 

 

One of the unchallenged tools in data science as of now, SAS continues to be the topmost choice for data scientists for data curation and AI development. 

 

A veteran tool developed in the late 1900s, SAS has evolved over time for better data curation and analysis.  

 

  1. Google Analytics

 

Launched in 2005, Google Analytics is one of the most renowned google data science tools. With real-time insights and data segmentation. 

 

More than that, the feature of predictive analysis has always succeeded in making this tool the better ones out of all others. 

 

Having said that, Google Analytics is undoubtedly the most sought-after data science tool that has not only made the work easier but has also added to the excitement. 

 

  1. WEKA

 

When it comes to data mining and related activities, WEKA is the right pick. To begin with, WEKA is a data science tool that uses various ML algorithms to accomplish data mining tasks on a day-to-day basis.

 

It includes tools that are capable of data regression, classification, and pre-processing. With all such features included in one tool, WEKA is suitable for usage by both beginners and experienced professionals. 

 

For every task that needs to be done, WEKA has multiple ML algorithms to get the work done! Perhaps data science can be made easier with this tool!    

 

(Also read: Python interview questions for data science)

 

  1. Tableau

 

One of the most widely used data science tools by data scientists, Tableau is abundant in features that help one to extract actionable insights from raw data apart from creating statistical visuals like graphs or charts. 

 

If you are thinking of the features that make this tool one of the most desirable among all, then here are some - fast speed, hassle-free user interface, huge data capacity, and accurate data summarization. Learn more about Tableau and its working

 

  1. TensorFlow

 

Launched in 2017, TensorFlow is an open-source library for numerical computation and large-scale ML algorithms. When it comes to data science, TensorFlow is known for its mechanism to acquire big data and train ML models to dig deep into data sets for serving predictions. 

 

Largely known for its deep learning applications, TensorFlow models approach data in the form of numerical computations that help users to get actionable insights. 

 

All in all, it is an effective tool that is among the top 15 data science tools 2021.

 

  1. MATLAB

 

To extract insights from data, one needs to ensure that the data science tool is well-equipped with ML algorithms to run through the vst data. MATLAB, a data science tool works with predictive ML models that help data scientists to conduct pre-processing on raw data. 

 

A renowned programming language tool that works well with data science, MATLAB was launched in 1984. Today, it enables users to access and analyze data with several interesting features. 

 

  1. PowerBI

 

One of the most sought-after data science tools for data visualization, PowerBI is a collection of data analysis tools that help users derive insights from data that otherwise seems to be quite hefty for manual analysis. 

 

Data collection, storage, cleaning, and analysis are some processes that can be conducted with the help of this tool that is used by many businesses in the industrial sphere. 

 

As PowerBI works towards data analysis, one gets to structure data in a desirable form with the help of a variety of applications. 

 

(Read also: PowerBI and Tableau for data visualization)

 

 

In the End 

 

In the end, data science tools can be used to identify actionable insights that otherwise can not be found by manual analysis. As more and more data science tools are coming up to help humans filter through raw data, 

 

ML algorithms are simultaneously progressing to achieve data analysis in the best way possible. High-speed processing, large capacity, and in-depth analysis are some features that each and every data scientist looks for. 

 

Perhaps with these data science tools and others emerging in the current scenario, the world can expect a lot more than just data analysis when it comes to data science.  

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