Data Science Tools

Data science is the practice of implementing scientific principles and analytic methods to take out crucial information from data for business planning and decision-making. Data science is becoming the most popular field of the 21st century. Because of this, the demand for data scientists is increasing. Companies are hiring these professionals to produce better products and boost business.

The aspirants who are interested in becoming data scientists should join the top data science online course India where they will be taught about data science and its various tools.

Popular Data Science Tools

Data scientists use various data science tools to carry out data operations. Below is the list of most used data science tools:


SAS stands for Statistical Analysis System. This tool is used for statistical operations. It is closed-source proprietary software used by large companies to examine data. SAS tool is based on SAS programming language which is used to do statistical modeling. The tool offers several statistical libraries for organizing data. As the SAS tool is very expensive, it is used by big industries that rely on commercial software. 

The features of the SAS tool are:

  1. Data analysis abilities
  2. Management
  3. Report output format
  4. Data encryption algorithms
  5. SAS studio

Apache Spark

It is an analytics engine tool designed to handle stream and batch processing. This tool has machine learning APIs that allow data scientists to make predictions with the given data. Apache spark can process real-time data. It comes with a Scala programming language based on Java Virtual Machine. 

Some features of the Apache spark tool are:

  1. Lazy evaluation
  2. Dynamic in nature
  3. Reusability
  4. Advanced analytics
  5. Fault tolerance


This tool is used to process mathematical information. MATLAB is a closed-source software that facilitates data statistical modeling, algorithmic implementation, and matrix functions. Data scientists use this tool for simulating fuzzy logy and neural networks. The tool is also used in signal and image processing. MATLAB automates various decision-making tasks. 

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Some MATLAB features are:

  1. Deep learning tool
  2. Process complicated mathematical operations
  3. Useful graphics library
  4. Embedded systems
  5. Numerical computing tool


Excel is a widely used data analytical tool. The tool is used for calculation, visualization, and processing. Data scientists use excel for data cleaning as it delivers an interactive GUI environment for pre-processing data. 

Following are the features of Excel:

  1. Performs small-scale data analysis
  2. Excel ToolPack performs complicated data analysis
  3. Allow doing visualizations and spreadsheet calculations
  4. Connects with SQL


D3.js is a JavaScript library that allows data scientists to create correlative visualizations on the web browser. The tool offers animated transitions and helps to create dynamic documents. Data scientists working on IoT-based devices use this tool for data processing. The features of D3.js are:

  1. Open-source
  2. Combined with CSS
  3. Based on JavaScript
  4. Client-side interaction tool
  5. Make interactive visualizations


This is a data visualization software that offers effective graphics to develop interactable visualizations. Tableau is used by business intelligence companies. This tool easily interfaces with online analytical processing (OLAP), cubes, spreadsheets, databases, etc. Tableau picturizes geographical data and can plot latitudes and longitudes on maps. Here are some features of Tableau:

  1. Enhancements of JavaScript API
  2. Revision history
  3. Mobile device management
  4. ETL refresh
  5. Rest API enhancements 


NLTK stands for Natural Language Toolkit. This tool is used for various language processing methods like machine learning, parsing, stemming, tagging and tokenization. NLTK contains data collection that is used for making machine learning models. The applications of this tool are machine translation, text-to-speech, speech recognition, parts of speech tagging, word segmentation, etc. Below are the features of the NLTK tool:

  1. Open-source
  2. Consists of several text corpora
  3. Perform text analysis
  4. A python library
  5. Perform natural language processing tasks
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It is a data science tool for machine learning. TensorFlow is an open-source toolkit that has high computational abilities. This tool can work on both GPUs and CPUs. TensorFlow has various applications such as image classification, speech recognition, language generation, drug discovery, etc. The features of the TensorFlow tool are as follows:

  1. Layered components
  2. Parallel neural networking training
  3. Event logger
  4. Responsive construct
  5. Easily trainable


It is a data science tool that offers an interactive cloud-based GUI environment for processing machine learning algorithms. These algorithms include forecasting, time series, clustering, classification, etc. The tool delivers a convenient web interface with the help of Rest APIs. Data scientists can create a premium or free account based on the data needs. 

Furthermore, using BigML, one can export visual charts on IoT devices. It gives several automation techniques that are used to automate the hyperparameter models and the workflow of transformable scripts.


This tool is created for the R programming language. ggplot2 replaces the graphics package of R and uses commands to make visualizations. Data scientists use this library to make visualizations from the data. It is the most common data science tool used for developing different styles of maps such as cartograms, hexbins, choropleths, etc.


It is a 100% open-source tool based on IPythonused for creating open-source software. This tool supports various languages like R, Python, and Julia. Jupyter, a web application, meets all needs of data scientists and is used for presentations, writing live code, and visualizations. Data scientists use Jupyter Notebooks to perform statistical computation, data cleaning, visualization, and making machine learning models. Jupyter is a powerful and free-of-cost tool.


It is a visualization and plotting library made for Python. Data scientists use this tool to make graphs according to the analyzed data. The main use of this tool is to plot complicated graphs using simple code lines. Matplotlib enables professionals to create scatterplots, bar plots, histograms, etc. Data scientists prefer this tool over other tools as it contains various important modules.

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Scikit-learn is a Python library-based tool used for executing machine learning programs, data science, and analysis. The python libraries used by this tool are Numpy, Matplotlib, SciPy, etc. The tool needs fast prototyping as it allows professionals to easily use complicated machine learning algorithms. Some of its features are:

  1. Classification
  2. Pre-processing
  3. Reduction
  4. Regression
  5. Clustering
  6. Dimensionality


Weka stands for Waikato Environment for Knowledge Analysis. It is a machine learning program coded in Java. Weka is used for data mining as it has a collection of different machine learning algorithms. It offers a variety of machine learning tools such as clustering, data preparation, visualization, and regression. Weka is an open-source GUI program used to execute machine learning algorithms.

Google Analytics

Data scientists use this tool for digital marketing. Google Analytics uses users’ data to make better marketing decisions. With the help of this tool, web admins can analyze data to understand users’ interactions with websites.

  • R

It is a programming language used for graphics applications, statistical computing, analysis, visualization, and data manipulation. Data scientists use this tool to cleanse, analyze, present, and extract data. The R Foundation supports this open-source environment. There are many user-created packages with libraries that improve the functionality of R.

The Bottom Line

The field of data science needs a variety of data science tools. Candidates who join online data science Bootcamps have a better knowledge of the data science tools. They know how to use these tools and other programming languages. 

These tools enable data scientists to analyze data and create predictive models for machine learning methods. The data scientists can use the above-mentioned tools to enhance work efficiency as they provide a user-friendly and interactive interface.