Top 20+ Most Popular Data Science Tools in 2024

Data Science has emerged as one of the most popular fields of knowledge in the past few years. With the help of several Data Science Tools, different types of data can be analysed, which helps gain better insights about the market and improve businesses. These tools are required to extract, manipulate, pre-process, and generate predictions from large volumes of data. With the help of Data Science Tools, Data Scientists can be better decision-makers. Explore this article to learn about the 20 most popular and efficient Data Science Tools and how these tools play significant roles in business growth. 

Table of Contents 

1) 24 best Data Science Tools 

     a) Matplotlib 

     b) Pandas 

     c) NumPY 

     d) Sci-Kit Learn 

     e) Scrapy 

     f) KNIME 

     g)  WEKA 

     h) Apache Hadoop 

     i) TensorFlow 

     j) Apache Spark 

     j) Jupyter Notebook 

     i) Tableau 

     m) RapidMiner 

     n) MATLAB 

     o) MS Excel 

     p) SAS 

     q) ggplot2

     r) BigML 

     s) D3.js 

     t) NLTK 

2) Importance of Data Science Tools 

3) Conclusion 

20 Best Data Science Tools 

Data Science is one of the most rapidly developing fields that attracts all industries today. It is the process of extracting and processing useful insights from raw data by the use of a combination of different types of components. Data Science carries so much importance today because almost all industries, including healthcare, tourism, automobile, defence, and manufacturing, have applications associated with the analysis of large volumes of crucial data.   

These wide applications and increased demand for software that enable business to analyse data has led to the development of various Data Science Tools today. These tools are the framework that helps Data Scientists to perform the analysis, visualisation, mining, reporting, and filtering of data.


 

Matplotlib 

Matplotlib is a Python-based Data Science tool for reading, importing, and visualising data in various platforms and applications. Python is a popular programming language. The best thing about Matplotlib is that it has an object-oriented interface. It means it can be used with Pyplot or independently. It also offers low-level functions for complex and challenging data visualisation. Although the Matplotlib library focuses on generating 2D visualisations, it also includes a 3D charting toolkit as an add-on.  

Matplotlib contains a vast code base that can be challenging sometimes. However, its hierarchical manner allows users to create visualisations primarily using high-level commands. Matplotlib can be implemented in Python scripts, Python and IPython shells, web application servers, Jupyter Notebook, as well as different Graphical User Interface (GUI) toolkits to develop static, animated, and interactive data visualisations. 

Pandas 

Pandas is another popular open-source Python library. It was developed by Wes McKinney in 2008 and given the name Panda due to the “Panel Data” reference. It is helpful in data analysis and manipulation because it’s especially ideal for dealing with numerical tables and data in time series. It has a flexible data structure that allows efficient data manipulation and makes it easier to represent that data.  

Series and DataFrame Objects are the two array and table structures in Pandas that represent diverse data sets. This Python library is accompanied by an in-built data visualisation feature that enables the creation of various plots and charts. Pandas library also offers to load and save data in multiple formats like JSON, CSV, HDF5, etc. It also enables the labelling of series and tabular data. 

NumPY 

Numerical Python, popularly known as NumPy, is also a Python Data Science tool that allows one to work with complex data sets and perform scientific computations. For mathematical calculations, such as slicing items and executing vector operations, programmers rely mainly on NumPy. It offers various standard functions that allow efficient mathematical operations.   

NumPy also provides features for writing and reading large data sets by enabling memory-based file mappings for I/O operations. NumPy ndarray is a multi-dimensional array with strong broadcasting abilities. NumPy is one of the most important libraries in Python because of its extensive built-in functions. 

Sci-Kit Learn 

Sci-Kit Learn is the next open-source data preprocessing tool based on Python library. It helps with machine learning (ML) activities. It offers to create ML models and provides data preprocessing and analysis functions. Sci-Kit Learn combines three Python libraries- SciPy, NumPy, and Matplotlib. It has several classifications and clustering algorithms. It supports feature extraction and normalisation for data analysis. 

Scrapy 

Scrapy is one of the most popular and efficient Data Science Tools available in Python. It’s used to create advanced-level web spiders that crawl across websites and scrape data. It is flexible and effective because it has numerous spider classes as well as a suitable infrastructure for downloading multiple files. Scrapy comes with detailed documentation, which makes it easier to learn. 

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KNIME 

Konstanz Information Miner, abbreviated as KNIME, is an open-source tool that offers end-to-end data analysis, integration, and reporting. It has two aspects. One - create data science, which means data gathering and visualisation; and second - productionise data science, which means deploying the model and optimising insights generated from the model. 

WEKA 

WEKA, shorthand for Waikato Environment for Knowledge Analysis, is a software that provides tools for data processing, ML algorithm implementation, and visualisation. It offers to build solutions by successfully implementing ML algorithms in real-world situations. WEKA algorithms are known as classifiers. These classifiers can be applied to data sets using a GUI or a command-line interface. It can also be implemented using a Java API. WEKA integrates with Python, Spark, and other libraries like Sci-Kit Learn, etc. 

Apache Hadoop 

Apache Hadoop is another tool that helps create programming models for vast data volumes. It helps in data exploration and storage by identifying the intricacies of the data. Its distributed computing design enables it to handle large data volumes. It also gives more processing power. The low-cost storage feature of Hadoop allows for storing unstructured data, such as texts, photos, videos, etc. 

TensorFlow 

TensorFlow is a Google’s ML tool which is popular for creating deep learning neural networks. With the help of Tensor Flow, dataflow graphs can easily be generated. These graphs explain how data flows through a graph or series of processing nodes.  

TensorFlow helps in creating models for various applications, like natural language processing (NLP), image recognition, handwriting identification, and computational simulations. Its main features include running low-level operations on different acceleration systems, production-level scalability, automated gradient computation, and interoperable graph exports. 

Apache Spark 

Apache Spark has the capacity to handle massive volumes of data. Its ability to connect to various data sources, including Cassandra, HDFS, HBase, S3, etc., makes it an efficient Data Science tool. It is a highly effective application because of its speed. Intricate data streams can be analysed and visualised with the help of Apache Spark. 

Jupyter Notebook 

It is an interactive tool that allows data scientists to combine code, computations and data visualisations together in a single file and collaborate among them. The benefit of Jupyter Notebook is that it provides a multi-language interface that supports more than 40 programming languages. 

Jupyter Notebook also helps data scientists streamline end-to-end data science activities. It is known for its interactive nature, which makes it perfect for focusing on data analysis rather than data development. Several experiments can be done here with data to see the code's results for each typed command. Moreover, Jupyter Notebook allows in-line output printing, which is especially beneficial for exploratory data analysis (EDA). 

Tableau 

Tableau is a data visualisation software that comes with powerful graphics to make interactive visualisations. It is especially useful for industries working in the field of business intelligence. Tableau has the ability to interface with databases, spreadsheets, Online Analytical Processing (OLAP) cubes, etc. It can also visualise geographical data and for plotting longitudes and latitudes on maps. 

RapidMiner 

RapidMiner is also a popular Data Science tool that helps in prediction modelling. It’s an end-to-end platform which supports data preparation, model development, validation, and deployment. It offers several features for automated data preparation and model construction, as well as extensive visual tools for developing data and machine learning. Data format can be acquired, loaded, and analysed using RapidMiner Studio. It includes both structured and unstructured data. It also has the ability to extract information from unstructured data and then convert it into structured data. 

MATLAB 

MATLAB is a multi-paradigm numerical programming environment for processing mathematical data. Unlike the above-discussed tools, it’s a closed-source software. It helps in analysing, designing and building embedded solutions for wireless technology. It also facilitates statistical modelling and implementation of data analytics. MATLAB is very useful in automating various tasks, such as extracting data and re-using scripts for decision-making. 

Microsoft Excel  

MS Excel is probably the most used Data Science tool. It was developed for spreadsheet calculations, but today, it is widely used for data processing, visualisation, complex calculations and many more things. MS Excel is really a powerful and effective analytical tool for Data Science.   

MS Excel comes with various formulae, tables, filters, and other facilities that help you create your own custom functions. It can also be connected with SQL to manipulate and analyse data. Data Scientists today use MS Excel for the purpose of data cleaning as it provides an interactable GUI environment to help pre-process information easily. 

SAS 

SAS is a Data Science Tool specially designed for statistical operations. It's an example of closed-source proprietary software. It's used by large organisations, professionals and companies working on reliable commercial software to analyse data. SAS offers several statistical libraries and tools that can be used for modelling and organising the data. Though SAS is a reliable and effective platform, it is highly expensive. This is the reason large industries mainly use it. 

ggplot2 

ggplot2 is an advanced Data Science tool for the programming language R. This tool was created to replace the in-built graphics package of R. It uses powerful commands to create illustrious visualisations. ggplot2 is a part of tidyverse, which is a package in R designed for Data Science. With ggplot2, customised visualisations can be created to engage in enhanced storytelling. It also helps you annotate data in visualisations, add text labels to data points, and boost the intractability of graphs. With ggplot2, various styles of maps, such as choropleths, cartograms, hex bins, etc., can also be created. 

BigML 

BigML is another popular Data Science Tool. It offers an interactable, cloud-based GUI environment that can be used for processing ML algorithms. It provides standardised software using cloud computing for industry requirements. With the help of BigML, you can use ML algorithms across various parts of your company, such as sales forecasting, risk analytics, and product innovation.   

BigML has a specialisation in predictive modelling. It means using a wide variety of ML algorithms, like clustering, classification, time-series forecasting, etc., for predictive modelling. It provides an easy-to-use web interface where free or premium accounts can be created based on the data needs. It also allows interactive visualisations of data and exporting visual charts on your mobile or IOT devices. It also comes with various automation methods that help in the automation of the tuning of hyperparameter models and even the workflow of reusable scripts. 

D3.js 

D3.js is a Javascript library which helps create interactive visualisations on the web browser. With the help of D3.js, several functions can be used to create dynamic visualisation and analysis of data. Another remarkable feature of D3.js is that it can be used for animated transitions. D3.js makes documents dynamic by allowing updates on the client side, as well as actively using the change in data to reflect visualisations on the browser. 

NLTK 

Natural Language Toolkit (NLTK) is a Data Science Tool that is widely used for various language processing techniques like tokenisation, stemming, tagging, parsing and machine learning. It consists of more than 100 corpora, which are a collection of data for building ML models. With the rising importance of Natural Language Processing, NLTK has become a useful Data Science Tool in recent times.  

NLTK has a variety of applications, such as Parts of Speech Tagging, Word Segmentation, Machine Translation, text-to-speech, Speech Recognition, etc. It majorly deals with the development of statistical models that help computers understand human language. 



 

Google Analytics

Google Analytics is a Data Science Tool designed as a framework for enterprises to have a detailed look at the performance of their websites to acquire data-driven insights. Professionals in the Data Science domain are generally employed across a diverse range of industries, one of which is Digital Marketing. Now such a tool aids enterprises with their Digital Marketing needs, and helps the web administrator access, visualise and analyse the traffic and data on their website. Businesses can better comprehend the interaction of their end users or consumers with the website. 

More importantly, Google Analytics can also function in collaboration with other services like Google Ads, Data Studio and Search Console. Such a level of operability makes it a great option for any users that need to leverage their business processes with various products from Google. Moreover, Data Science professionals and marketing experts can optimise their marketing decisions. Professionals can especially use it to carry out Data Analytics without any need of a technical background. They can benefit immensely from its premium functionalities and user-friendly interface.

MongoDB

MongoDB, a cross-platform and document-centric database program, allows Data Scientists to better manage their document-oriented data, store & retrieve necessary information as per their requirements. The service is especially designed to handle massive volumes of data and caters to all available capabilities of SQL. Additionally, MongoDB supports the execution of dynamic queries and caches the data in a format similar to JSON. This is because this data which is cached as documents helps deliver high-level data replications. 

Moreover, the ability to handle Big Data has been made convenient with the introduction of MongoDB as it facilitates the availability of data. In addition to the database queries, MongoDB also has the potential to perform advanced Data Analytics and enables data scalability. Enterprises employing the MongoDB service can also avail its high availability of replica sets, where such sets comprise of two or more data copies. The service is also designed to use sharding to horizontally scale its databases. MongoDB has recently introduced a development to expand the capabilities of its application development process for generative AI applications. 

Microsoft Power BI

Power BI is a powerful suite designed for Business Intelligence processes by Microsoft and is one of the most recommended Data Science tools in 2024. This service helps users create visually attractive data reports and data visualisation services for individual users and enterprise teams. Users can also combine the service with other tools from Microsoft such as Azure Synapse Analytics, Azure Data Lake, Microsoft Excel and so on. 

Such a service integration helps enterprise improve their team performance and individual productivity. Further, majority of Business Intelligence and Data Analytics enterprises leverage the Microsoft Power BI tool for the construction of their Data Analytics dashboard. The service can also be used to transform incoherence in data sets to more coherent information. Users can also retrieve rich insights from original data while they develop logically uniform and invariant datasets. Microsoft designed the service such that non-technical users can easily comprehend the data insights.

QlikView

A powerful Business Analytics service, founded in 1993, has revolutionised the manner by which organisations utilise data. The service helps make Business Intelligence more widespread by facilitating intuitive discovery for people. The platform is designed to help users augment and enhance their human intuition with AI powered insights. Users are assisted in their progress from passive to active Data Analytics for collaborating in real-time, the process which is delivered inside a hybrid cloud setting. Such a setting helps support all end-users and use cases throughout a company at an enterprise-scale.

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Importance of Data Science Tools 

Data Science is all about extracting, reading, processing and analysing huge volumes of data to solve real-world problems. It will certainly require some tools to perform this task. Data Science Tools help data scientists perform this complex task efficiently. Without Data Science Tools, it’s almost impossible to process these many different types of data, and that too from different sources. With the help of these tools, data scientists solve crucial business problems for an organisation. Companies/businesses need data scientists to develop solutions using the power of these Data Science Tools.   

Here are some important reasons why businesses need data science tools and technologies: 

1) Data Science Tools use statistics and predictive analytics to extract complex data by acquiring, manipulating, and analysing business data to derive valuable insights. 

2) Data Science Tools allow businesses to speed up their data analysis by integrating different types of data from different sources. 

3) By analysing crucial data with the help of Data Science Tools, businesses can make faster decisions and implement their projects quickly. 

Conclusion 

Data Science Tools provide a variety of functionalities that are essential for businesses today. Each of these tools possesses its own distinct benefits and limitations. Still, they provide users the flexibility to choose the one that suits their specific needs and preferences. Without these tools, it’s impossible to dive into the ocean of raw, complicated and unstructured data. 

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