Top 9 Data Science Tools [Most Used in 2023]

Data Science is all about leveraging large datasets to extract meaningful insights that can be further transformed into actionable business decisions. That’s the reason data science courses are in high demand these days.

Data Scientists are the brilliant minds responsible for accumulating, processing, manipulating, cleaning, and analyzing data to extract valuable insights from within it. Day-in and day-out, Data Scientists have to deal with massive amounts of structured and unstructured data. Various data science statistical and programming tools help data scientists to make sense of the accumulated data.

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This is the topic of discussion today – the top Data Science tools used by Data Scientists all over the world.

Top Data Science Tools in 2019

  1. Apache Spark

Apache Spark is one of the most popular Data Science tools. It is a robust analytics engine explicitly designed to handle batch processing and stream processing. Unlike other Big Data platforms, Spark can process data in real-time and is way faster than MapReduce. Also, Spark excels in cluster management – a feature that’s responsible for its fast processing speed. 

Spark comes with numerous Machine Learning APIs that allows Data Scientists to make accurate predictions. Apart from this, it also has various APIs that are programmable in Java, Python, Scala, and R.

  1. BigML

BigML is a cloud-based GUI environment designed to process ML Algorithms. One of the best specialization features of BigML is Predictive Modeling. By leveraging BigML, companies can use and implement different ML algorithms across various business functions and processes. For instance, BigML can be used for product innovation, sales forecasting, and risk analytics. 

BigML uses REST APIs to create user-friendly web-interfaces, and it also facilitates interactive visualizations of data. To add to that, BigML comes equipped with a host of automation techniques that allow you to automate workflows and even the tuning of hyperparameter models. 

  1. D3.js

D3.js is a Javascript library used for creating and designing interactive visualizations on web browsers. It is an excellent tool for professionals working on applications/software that require client-side interaction for visualization and data processing. D3.js APIs allow you to make use of its various functions to both analyze data and create dynamic visualizations on a web browser. It can also be used for making documents dynamic by enabling updates on the client-side and actively monitoring the alterations in data to reflect visualizations on the browser.

The great thing about D3.js is that it can be integrated with CSS to create illustrious visualizations for implementing customized graphs on web pages. Plus, there’s also animated transitions if you need it. 


MATLAB is a high-performance, multi-paradigm numerical computing environment designed for processing mathematical information. It is a closed-source environment that allows for algorithmic implementation, matrix functions, and statistical modeling of data. MATLAB combines computation, visualization, and programming within an easy-to-use environment where both problems and their solutions are expressed in mathematical notations.

MATLAB, as a popular data science tool, finds numerous applications in Data Science. For instance, it is used for image and signal processing and for simulating neural networks. With MATLAB graphics library, you can create compelling visualizations. Additionally, MATLAB allows for easy integration for enterprise applications and embedded systems. This makes it ideal for a host of Data Science applications – from data cleaning and analysis to implementing Deep Learning algorithms.

  1. SAS

SAS is an integrated software suite designed by the SAS Institute for advanced analytics, business intelligence, multivariate analysis, data management, and predictive analytics. However, it is a closed-source software that can be used via a graphical interface, or the SAS programming language, or Base SAS.

Many large organizations use SAS for data analysis and statistical modeling. It can be a convenient tool for accessing data in almost any format (database files, SAS tables, and Microsoft Excel tables). SAS is also great for managing and manipulating existing data to get new results. Also, it has an array of useful statistical libraries and tools that are excellent for data modeling and organization.

  1. Tableau

Tableau is a powerful, secure, and flexible end-to-end analytics and data visualization platform. The best part about operating Tableau as a data science tool is that it doesn’t demand any programming or technical flair. Tableau’s power-packed graphics and easy-to-use nature have made it one of the most widely used data visualization tools in the Business Intelligence industry.

Some of the best features of Tableau are data blending, data collaboration, and real-time data analysis. Not just that, Tableau also can visualize geographical data. It has various offerings like Tableau Prep, Tableau Desktop, Tableau Online, and Tableau Server to cater to your different needs. 

  1. Matplotlib

Matplotlib is a plotting and visualization library designed for Python and NumPy. However, Even SciPy uses Matplotlib. Its interface is similar to that of MATLAB.

Perhaps the best feature of Matplotlib is its ability to plot complex graphs by simple lines of code. You can use this tool to create bar plots, histograms, scatterplots, and basically any other kind of graphs/charts. Matplotlib comes with an object-oriented API for embedding plots into applications using general-purpose GUI toolkits (Tkinter, wxPython, GTK+, etc.). Matplotlib is the perfect tool for beginners who wish to learn data visualization in Python. 

  1. Scikit-learn

Scikit-learn is a Python-based library that is packed with numerous unsupervised and supervised ML algorithms. It was designed by combining features of Pandas, SciPy, NumPy, and Matplotlib. 

Scikit-learn supports various functionalities for implementing Machine Learning Algorithms such as classification, regression, clustering, data pre-processing, model selection, and dimensionality reduction, to name a few. The primary job of Scikit-learn is to simplify complex ML algorithms for implementation. This is what makes it so ideal for applications that demand rapid prototyping.

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  1. NLTK

Another Python-based tool on our list, NLTK (Natural Language Toolkit), is one of the leading platforms for developing Python programs that can work with natural human language data. Since Natural Language Processing has emerged as the most popular field in Data Science, NLTK has become one of the favorite tools of Data Science professionals.

NLTK offers easy-to-use interfaces to over 50 corpora (collection of data for developing ML models) and lexical resources, including WordNet. It also comes with a complete suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. NLTK is useful for various NLP applications like Parts of Speech Tagging, Machine Translation, Word Segmentation, Text-to-Speech, and Speech Recognition.

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Bonus:  TensorFlow

TensorFlow is a Python-friendly, end-to-end, open-source platform for Machine Learning. It is a comprehensive and flexible ecosystem of tools, libraries, and community resources that facilitate fast and easy numerical computation in ML. TensorFlow allows for easy ML model building and training and deploying ML models anywhere. It has a neat and flexible architecture for encouraging the development of state-of-the-art models and experimentation.


Thanks to its active community, TensorFlow is an ever-evolving toolkit that is popular for its high computational abilities and exceptional performance. It can run on not only CPUs and GPUs but also on TPU platforms (a recent addition). This is what has made TensowFlow a standard and globally acknowledged tool for ML applications. 

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Wrapping up…

Data Science is a complex domain that requires a wide variety of tools for processing, analyzing, cleaning and organizing, munging, manipulating, and interpreting the data.  The work doesn’t stop there. Once the data is analyzed and interpreted, Data Science professionals must also create aesthetic and interactive visualizations for the ease of understanding of all the stakeholders involved in a project. Further, Data Scientists have to develop powerful predictive models using ML algorithms. All such functions cannot be accomplished without the help of such Data Science tools. 

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So, if you wish to build a successful career in Data Science, you better start getting your hands dirty with these tools right away!

What are the most popular data science tools?

Data science is all about using large datasets and useful tools for extracting meaningful insights from a huge amount of data and turning them into actionable business insights. To make the work really easy, data scientists need to use some tools for better efficiency.
Let us have a look at some of the most widely used data science tools:
1. SAS
2. Apache Spark
3. BigML
5. Excel Tableau
6. Jupyter
If you utilize these data science tools, you will find it pretty easy to develop actionable insights by analyzing the data. Data Scientists find it easy to deal with a huge amount of structured as well as unstructured data by using the right tool.

What is the most widely used data science method?

Different data scientists make use of different methods as per their requirements and convenience. Every method has its own importance and working efficiency. Yet, there are certain data science methods that are on the list of every data scientist for analyzing data and coming up with actionable insights from it. Some of the most widely used data science methods are:
1. Regression
2. Clustering
3. Visualization
4. Decision Trees
5. Random Forests
6. Statistics
Other than that, it has also been found that among the KDnuggets readers, Deep Learning is only used by 20% of data scientists.

How much math do you need to learn to become a Data Scientist?

Math is considered to be the foundation of Data Science. But, you don't need to worry because there is not so much math you need to learn to build your career in data science. If you Google up the math requirements for becoming a data scientist, you will constantly come across three concepts: calculus, statistics, and linear algebra. But, let's get it clear that you need to learn a major portion of statistics for becoming a good data scientist. Linear algebra and calculus are considered to be a bit less important for data science.
Other than that, one also needs to be clear with the fundamentals of discrete math, graph theory, and information theory for understanding and working efficiently with different data science methods and tools.

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