Top 25 Data Science Tools to Consider Using in 2024

Data science has become the fastest-growing field and a buzzword that every industry knows and leverages. This vast stream of interdisciplinary domains deals with data in different ways. According to the GlobeNewswire report, the projected growth of the data science market will hike up to a CAGR of 25 percent by 2030. 

According to some research, the market demand for data science is increasing every day as companies are opening new doors through data-driven approaches. With the increase in the demand for data science, job opportunities are also exponentially high. You can begin your journey in this field by taking up a data science online course.

In this article, you will learn about all the latest data science tools and frameworks that data science professionals require throughout their career.

List of Top 25 Data Science Tools to Use in 2024

Data science tools are application software or frameworks that help data science professionals to perform various data science tasks like analysis, cleansing, visualization, mining, reporting, and filtering of data. Each of these tools comes with a set of some of these usages. All the different processes that data science uses for analyzing & extracting insight from data require appropriate tools. 

A data science professional, data analyst, data engineer, and data scientist need to have high proficiency in these tools to excel in the data science career. If you go for a Data Science with Python certification, you will be trained on all the current data science tools. Let us now get to know what these tools are and how do they help data scientists and professionals.

Here is the list of Tools that we will be covering in this article:

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General-purpose Data Science Tools

1. MS Excel

It is the most fundamental & essential tool that everyone should know. For freshers, this tool helps in easy analysis and understanding of data. MS. Excel comes as a part of the MS Office suite. Freshers and even seasoned professionals can get a basic idea of what the data wants to say before getting into high-end analytics. 

It can help in quickly understanding the data, comes with built-in formulae, and provides various types of data visualization elements like charts and graphs. Through MS Excel, data science professionals can represent the data simply through rows and columns. Even a non-technical user can understand this representation.

Advantages:

• Intuitive interface makes data manipulation straightforward.
• Widely adopted across businesses, ensuring compatibility.
• Versatile tool capable of handling diverse tasks.

Cloud-based Data Science Tools

2. BigML

BigML is an online, cloud-based, event-driven tool that helps in data science and machine learning operations. This GUI based tool allows beginners who have little or no previous experience in creating models through drag and drop features. For professionals and companies, BigML is a tool that can help blend data science and machine learning projects for various business operations and processes.

 A lot of companies use BigML for risk reckoning, threat analysis, weather forecasting, etc. It uses REST APIs for producing user-friendly web interfaces. Users can also leverage it for generating interactive visualizations over data. It also comes with lots of automation techniques that qualify users to eliminate manual data workflows.

Advantages:

• Simplifies machine learning with an easy-to-use interface.
• Automates data preprocessing and model selection.
• Scales efficiently to handle large datasets.

3. Google Analytics

Google Analytics (GA) is a professional data science tool and framework that gives an in-depth look at an enterprise website or app performance for data-driven insights. Data science professionals are scattered across various industries. One of them is in digital marketing. This data science tool helps in digital marketing & the web admin can easily access, visualize, and analyze the website traffic, data, etc., via Google Analytics. It can help businesses understand the way customers or end-users interact with the website.

 This tool can work in close tandem with other products like Search Console, Google Ads, and Data Studio, which makes it a widespread option for anyone using leveraging different Google products. Through Google Analytics, data scientists and marketing leaders can make better marketing decisions. Even a non-technical data science professional can utilize it to perform data analytics with its high-end functionalities and easy-to-work interface.

Advantages:

• Provides detailed insights into website traffic and user behavior.
• Seamlessly integrates with other Google services and tools.
• Allows creation of custom reports and dashboards.

Multipurpose Data Science Tools

4. Apache Spark

Apache Spark is a well-known data science tool, framework, and data science library, with a robust analytics engine that can provide stream processing and batch processing. It can analyze data in real-time and can perform cluster management. It is much faster than other analytic workload tools like Hadoop. Apart from data analysis, it can also help in machine learning projects.

 It caters to various built-in Machine Learning APIs that allow machine learning engineers and data scientists to create predictive models. Along with all these, Apache spark caters to different APIs that are Python, Java, R, and Scala programmers can leverage in their program.

Advantages:

• Fast data processing with in-memory computing.
• Supports various data processing tasks including streaming.
• Scales easily across multiple machines for big data workloads.

5. Matlab

Matlab is a closed-source, high-performing, numerical, computational, simulation-making, multi-paradigm data science tool for processing mathematical and data-driven tasks. Through this tool, researchers and data scientists can perform matrix operations, analyze algorithmic performance, and render data statistical modeling. 

This tool is an amalgamation of visualization, mathematical computation, statistical analysis, and programming, all under an easy-to-use ecosystem. Data scientists find various applications of Matlab, especially for signal and image processing, simulation of the neural network, or testing of different data science models.

Advantages:

• Offers advanced mathematical functions for complex computations.
• Excellent tools for high-quality data visualization.
• Integrates well with other programming languages and tools.

6. SAS

SAS is a popular data science tool designed by the SAS Institute for advanced analysis, multivariate analysis, business intelligence (BI), data management operations, and predictive analytics for future insights. This closed-source software caters to a wide range of data science functionalities through its graphical interface, along with its SAS programming language, and via Base SAS. A lot of MNCs and Fortune 500 companies are utilizing this tool for statistical modeling and data analysis. 

This tool allows easy accessing of data from database files, online databases, SAS tables, and Microsoft Excel tables. It is also used for manipulating existing data sets to get data-driven insights by leveraging its statistical libraries and tools.

Advantages:

• Known for powerful statistical analysis capabilities.
• Ensures strong data security and governance.
• Provides comprehensive customer support and documentation.

7. KNIME

KNIME is another widely used open-source and free data science tool that helps in data reporting, data analysis, and data mining. With this tool, data science professionals can quickly extract and transform data. It allows integrating various data analysis & data-related components for machine learning (ML) and data mining objective by leveraging its modular data pipelining concept. 

It caters to an excellent graphical interface through which data science professionals can more likely define the workflow between the various predefined nodes provided in its repository. Because of this, data science professionals require minimum programming expertise to carry out data-driven analysis and operations. It has visual data pipelines that help in rendering interactive visuals for the given dataset.

Advantages:

• Automates workflows with a visual interface.
• Extensible with support for various data sources and tools.
• Backed by a strong and active user community.

8. Apache Flink

Flink is another of Apache's data science software that helps perform real-time data analysis. It is one of the most popular open-source batch-processing data science tools and frameworks that utilizes its distributed stream processing engine to perform various data science operations. A lot of time, data scientists & professionals require performing real-time analysis & computation on data such as data from users' web activities, measuring data emitted from the Internet of Things (IoT) devices, location-tracking feeds, financial transactions from apps, or services, etc. That is where Flink can deliver both parallel and pipelined execution of data flow at a lower latency. 

It uses batch processing to handle this flow of enormous data streams (that are unbounded - i.e., they do not have a fixed start and endpoint) as well as stored datasets (that are bounded). Apache Flink has a reputation for rendering high-speed processing and analysis while reducing the complexity of dealing with real-time data processing.

Advantages:

• Excels in real-time data stream processing.
• Fault-tolerant to ensure reliable data processing.
• Scales horizontally to handle large data volumes.

Programming Language-driven Data Science Tools

9. Python

Python is, by far, the most widely used data science programming language. Also considered a data science tool, Python helps data science professionals to perform data analysis over large datasets and data of different sorts (structured, semi-structured, and unstructured). This high-level, general-purpose, dynamic, interpreted programming language has a built-in data structure and a massive collection of libraries that can help in data analysis, data cleaning, data visualization, etc. 

It has a simple syntax and is easy to learn. It also reduces the cost of maintaining data science programs. Since this programming language helps develop mobile, desktop, and web applications along with data science capabilities - many prefer to learn this to leverage both data science and software development capabilities that this tool renders. It has outstanding community support and contributors keep on developing modules and libraries that can make data science and programming tasks easier.

Advantages:

• Versatile language for data manipulation and analysis.
• Rich ecosystem of libraries for scientific computing.
• Supported by a large, active community.

10. R Programming

R is a robust programming language that competes with Python when it comes to data science. Professionals and companies widely use it for statistical computing and data analysis. It has an excellent user interface and spontaneously updates its interface for better programming and data analysis experience. It has exceptional team contribution and community support that make it a valuable tool for data science. 

It is scalable because it has a huge collection of data science packages and libraries such as tidyr, dplyr, readr, SparkR, data.table, ggplot2, etc. Apart from statistical and data science operations, R also leverages powerful machine learning algorithms in a simple and fast manner. This open-source programming language comes with 7800 packages and object-oriented features. The entire language runs on RStudio.

Advantages

• Designed for statistical computing and analysis.
• Strong capabilities in data visualization.
• Free and open-source with a large community.

11. Jupyter Notebook

This computational notebook is a popular data science and web application that helps manage and interact with data effectively. Apart from data science professionals, researchers, mathematicians, and even beginners in Python also leverage this tool. It is prevalent for its easy data visualization feature and computational abilities mostly. Data science professionals and analysts can run a single line or multiple lines of code. It is a spin-off of the Python project and supports programming languages like Julia, Python, and R.

Advantages:

• Interactive environment for data analysis and visualization.
• Supports multiple programming languages.
• Facilitates sharing and collaboration of notebooks.

12. MongoDB

MongoDB is a cross-platform, open-source, document-oriented NoSQL database management software that allows data science professionals to manage semi-structured and unstructured data. It acts as an alternative to a traditional database management system where all the data has to be structured. MongoDB is a data science tool that helps data science professionals in managing document-oriented data, store & retrieve information as and when required. 

It can easily handle large volumes of data and caters to all the capabilities of SQL and more. It also endorses executing dynamic queries. MongoDB caches data in the JSON-like format as documents and delivers high-level data replications capabilities. Handling Big Data has become easier with the advent of MongoDB as it enables increased data availability. 

Apart from necessary database queries, MongoDB has the potential to execute advanced analytics. It also allows data scalability, making it one of the widely used Data Science tools (2024).

Advantages:

• Scales horizontally to manage large datasets.
• Flexible schema-less database structure.
• Supports real-time data processing and analytic

13. D3.js

Its full form is Data-Driven Document, and it is a popular JavaScript library for data science operations. It helps create and format interactive visualization of data science outcomes on web browsers. This excellent data science tool requires client-based interactions for data processing and visualization. D3.js also caters to APIs that allow its users to implement various functionalities for both analyzing datasets & producing dynamic visualizations that the application can run on any web browser. 

This tool also integrates with CSS for developing illustrious visuals of data & helps create animated data transitions. It also enables making dynamic documents by allowing updates on the client-side and actively monitoring the data alterations to render rich visualizations. It can use static data or can even fetch data from remote servers. It can work with different formats such as Objects, JSON, Arrays, CSV, XML, etc., for creating distinct types of charts and graphs.

Advantages:

• Enables creation of customized, interactive visualizations.
• Flexible integration with various web technologies.
• Efficiently handles large datasets for smooth visualizations.

14. Julia

Julia is a high-level, robust, general-purpose programming language that helps write data science code faster and easier. It can rapidly execute and implement scientific calculations and optimize experimentation and strategy implementation in datasets. Many data science experts consider it the successor of Python. Its Just-in-Time (JIT) compiling power can match the speed of various efficient programming languages like C++, C, etc., specifically in performing Data Science operations.

 It requires less processing power with high speed to accomplish complex statistical calculations associated with Data Science. It also allows manual garbage collection, and data science professionals do not have to worry about memory management. After Python and R, it is the most popular data science programming language that is gaining momentum and popularity among data scientists.

Advantages:

• High-performance computing similar to C.
• Simple syntax with powerful computational capabilities.
• Supports parallel and distributed computing.

Visualization Tools for Data Science

15. Tableau

Founded in 2003, Tableau has become one of the top-level data visualization and Business Intelligence(BI) tools that top MNCs and enterprises from diverse industry backgrounds leverage. Data scientists can understand and solve advanced and complex data analysis and visualization problems in time through Tableau. It caters to rich data visualization with various options that make information easier to understand. More than 60,000 companies utilize this tool for data visualization and creating interactive dashboards.

Advantages:

• Intuitive interface for creating complex visualizations.
• Allows interactive and shareable dashboards.
• Connects with numerous data sources.

16. Matplotlib

It is a popular 2D data visualization library for generating 2D plots and charts from data. It requires Python programming skills and works specifically with NumPy, SciPy, and Pandas. John Hunter introduced this multi-platform data visualization library in the year 2002. One of the best features of Matplotlib is the power to render complex graphs and plots using simple lines of codes. 

Through Matplotlib, data analysts and data scientists can create bar plots, pie charts, histograms, scatterplots, and other types of graphs. This data science library comes with an object-oriented API that helps to embed plots with other applications via general-purpose GUI modules like Tkinter, wxPython, etc. This tool is perfect for beginners who want to understand and make fast-loading data visualization using Python.

Advantages:

• Offers extensive options for creating various plots.
• Seamlessly integrates with other scientific libraries.
• Highly customizable visualizations.

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17. Minitab

Minitab is a well-known statistical software package for solving problems, identifying trends & discovering insights from granular data. It provides complete and best-in-class results. Data science professionals leverage this tool for data analysis and data manipulation operations. This tool also helps recognize patterns & data flowing tendencies from unstructured data. 

Minitab also enables data science professionals to automate various operations & graph generation. Minitab also helps create descriptive statistics from different data points in data like standard deviation, mean, median, etc. Apart from statistical and graphical data handling, this tool also helps in performing regressions analysis.

Advantages:

• Provides comprehensive tools for statistical analysis.
• User-friendly interface with guided analysis.
• Strong visualization capabilities for identifying trends

Machine Learning and NLP Tools for Data science

18. Scikit Learn

Scikit-learn is a free machine learning library designed on top of Python code. It has a broad spectrum of supervised and unsupervised machine learning algorithms that were designed with the help of other data science features and libraries like Matplotlib, Pandas, NumPy, and SciPy. This library is a package of different functionalities like regression analysis, data classification, clustering of data, model selection, data pre-processing, dimensionality reduction, etc., to name a few. 

The preliminary use of Scikit-learn is to streamline intricate ML algorithms for execution. It is has become a well-known tool and an ideal one for implementing machine learning in applications that require rapid prototyping. Scikit Learn has huge community support & anyone can contribute to its development and enhancement.       

Advantages:

• Comprehensive library for machine learning.
• Simple and consistent interface.
• Integrates well with other Python libraries.

19. TensorFlow

It is the most widely accepted data science tool and is popular because of its Machine Learning (ML) and Deep Learning (DL) libraries. It enables data science professionals and ML engineers to develop data analysis & ML algorithms or models. It also supports visualization features. Using TensorFlow, data scientists can cluster data science and machine learning models and induce them via large datasets for training the models or helps in developing systems that can generate sensible outcomes on their own. 

Data scientists and ML engineers employ TensorFlow with Python to analyze data and bring meaningful insight from the extracted data. Google Brain team has created this open-source library to handle complex numerical computation & large-scale supervised, semi-supervised, & unsupervised learning. 

Enterprises use TensorFlow for hand-written character classification, image recognition, word embeddings, NLP to teach machines human languages, recurrent neural networks, sequence-to-sequence models for machine translation, and PDE (partial differential equation) simulations. This easy-to-use tool helps data science professionals perform differential programming. Data scientists can easily deploy Data Science models on various applications and devices through this tool. TensorFlow leverages an N-dimensional array as its data type, called a tensor, and hence its name.

Advantages:

• Scales from single CPUs to thousands of GPUs.
• Supports various machine learning algorithms.
• Backed by a strong community and documentation

20. Rapidminer

Rapidminer is a comprehensive data science tool that caters to a visual workflow design and complete automation. It can prepare any Data Science and ML model from scratch without hurdles. Also, this tool helps Data scientists track data in real-time plus perform high-end analytics. Developers, non-developers, newcomers in the field of data science, and even non-technical aspirants can use this tool to practice rapid data mining, build custom workflows, and render data science functionalities. 

This GUI tool can perform various data science operations like real-time data analysis, predictive analysis, text mining, comprehensive data reporting, model validation, etc. It also caters to high scalability & security, which makes it a remarkable tool. Organizations can build commercial data science algorithms and apps from scratch through this tool.

Advantages:

• Visual workflow designer for building models.
• All-in-one data science platform.
• Integrates with various data sources and tools.

21. DataRobot

DataRobot is a well-known data science tool where data science professionals and ML engineers can integrate data science tasks with machine learning and artificial intelligence and enhance the overall team productivity. It allows dragging and dropping datasets on to the interface to operate on them. It has a simple and easy-handling GUI that enhances the productivity of different data analytics operations for both beginners and expert data science professionals. 

DataRobot also allows professionals to build and deploy hundreds of data science models at once. Enterprises leverage this tool to perform high-end automation on user data. This tool is also great at predictive analysis and can help people to make intelligent and compelling data-driven decisions.

Advantages:

• Automates machine learning model building and deployment.
• Intuitive interface accessible to non-experts.
• Scales efficiently for large datasets.

22. NLTK

Natural Language Toolkit (NLTK) is another Python-based toolkit that helps understand and algorithm work with natural languages. With the advancements in comprehending natural language processing to understand spoken or written language data or implementing NLP in apps, data science firms have started leveraging data science tools like NLTK. NLTK has become a common and popular tool among data science professionals because of its easy-to-work interface. Also, it caters to 50+ collections of data for developing language-oriented ML models along with lexical resources and WordNet. 

Lots of applications require NLTK for tokenizing, visualizing words, and generating parse tree to understand the language better. It has an easy-to-use text processing and helps companies develop different language-based applications Machine Translation, Speech Recognition, Parts of Speech Tagging, Text-to-Speech, and Word Segmentation.

Advantages:

• Comprehensive libraries for natural language processing.
• Extensive documentation and tutorials.
• Integrates well with other Python libraries.

Big Data Tools for Data Science

23. Apache Hadoop

Apache's Hadoop, written in Java, has large-scale implementation over data science. This open-source software is widely accepted for its parallel data processing. It can handle storage and processing of Big Data required for data analysis. Any large file gets distributed or split into smaller chunks and then passed over to different nodes. In other words, Hadoop works by distributing large data sets & data analytics processes across smaller nodes in a computing cluster. 

These nodes help in breaking data down into smaller workloads that can be executed in parallel. Hadoop can scale up the processing of both structured and unstructured data and ultimately help data scientists and professionals effectively manage different forms of data irrespective of growing volumes.

Advantages:

• Processes large datasets across distributed environments.
• Scales horizontally to handle increasing data volumes.
• Built-in fault tolerance for reliable processing.

Business Intelligence Data Science Tools

24. Microsoft Power BI

 Microsoft Power BI is a powerful business intelligence suite and one of the most recommended data science tools of 2024 that help create stunning data reports and visualization services for both individuals and teams. You can combine it with other Microsoft data science tools like MS. Excel, Azure Synapse Analytics, Azure Data Lake, etc., to enhance your team's performance and as an individual, boost your own productivity also. Many data analytics and business intelligence companies leverage this tool to construct their data analytics dashboard. 

Enterprises also use Power BI to transform incoherent data sets into coherent data sets. This tool also helps in developing a logically uniform and invariant dataset from other original data that can help us generate rich insights. Its eye-catching and rich data visualization helps even a non-technical person to easily understand data insights.

Advantages:

• User-friendly drag-and-drop functionality.
• Integrates with various data sources and Microsoft services.
• Capable of real-time data analysis and visualization.

25. QlikView

It is one of the business-leading data science tools that have unique features compared to legacy BI tools. Through QlikView, data science professionals can determine relationships between semi-structured and unstructured data & perform data analysis on these data. Also, it does data processing at a much faster rate compared to other data science tools in the market. QlikView users can also use visually appealing colors to understand the relationship among different data. Data science professionals can execute data accumulation and compression via QlikView in lesser time. QlikView automatically determines the relationships between data entities & professionals do not have to spend much time doing this.

Advantages:

• Creates highly interactive and customizable dashboards.
• Uses in-memory processing for fast data retrieval.
• Connects with various data sources for comprehensive views.

Mastering The Data Science Tools of The Trade

We hope this article has given you a crisp idea of all the popular data science tools that data science professionals use to reduce work latency. Data scientists & data science experts have to work on multiple tools in various categories such as programming tools, big data tools, data science libraries, ML tools, data visualization tools, and data analysis tools. All of these data science tools and frameworks help them analyze and extract meaning from granular data. You can learn to leverage these tools with the help of the right education. So, if you wish to build a successful career in Data Science, you better start getting your hands dirty with these tools right away!

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