In 2024, Python reigns supreme as the best data science tool, embraced by a staggering 95% of data scientists. R follows closely behind at 75%, prized for its robust statistical analysis capabilities, while SQL remains indispensable for 70% in managing relational databases. ML enthusiasts find ease in TensorFlow (55%) and PyTorch (50%) for cutting-edge model development. Data visualization thrives with Matplotlib (45%) and Pandas (40%). Meanwhile, distributed computing tools Spark (30%) and Hadoop (25%) continue to play key roles, reflecting the diverse toolkit.
In this article, we have listed the best data science tools required to have a successful career.
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Data science is the process of drawing useful insights from raw data using a combination of different components such as domain expertise, knowledge of mathematics and statistics, programming skills, and machine learning algorithms. The insights are later translated by business users and key decision-makers into tangible business value.
Data science has turned out to be one of the most popular tech fields in the 21st century. This is because almost every industry, including healthcare, travel, automobile, defense, and manufacturing, has applications associated with data science. These wide applications and increased demand for maximizing business value have led to the development of various data science tools.
Data science tools are used for diving into raw and complicated data (unstructured or structured data) and processing, extracting, and analyzing it to dig out valuable insights by applying different data processing techniques such as statistics, computer science, predictive modeling and analysis, and deep learning.
To deal with zettabytes and yottabytes of structured and/or unstructured data every day and to find valuable insights from it, data scientists use a wide range of tools at different phases of the data science life cycle. The significant feature of these tools is that they eliminate the need for sophisticated programming languages to implement data science procedures. This is because these tools contain some predefined algorithms, functions, and user-friendly graphical user interfaces (GUIs).
There are an ample number of data science tools in the industry. Hence, picking one for your journey and career can be a tricky decision. To make a decision on which tools to pick and learn for your career, check out the below-listed most in-demand tools in data science. You can master the Data Science Tools through our best Data Science course in Bangalore and be an expert.
Now, let’s explore the five most sought-after data science tools in the year 2024.
| Data Science Tools | Usage | Pros | Cons | Supporting Platforms |
| Statistical Analysis System (SAS) | Advanced analytics, business intelligence, and data management. | – Wide range of statistical analysis procedures. – Comprehensive data manipulation capabilities. | – Proprietary software with licensing costs. – Steeper learning curve compared to some open-source alternatives. | Windows, Linux, UNIX, z/OS, macOS |
| Apache Hadoop | Distributed storage and processing of large datasets. | – Scalable and reliable platform for big data processing. – Enables processing of unstructured and structured data. | – Requires specialized infrastructure setup. – Steeper learning curve for managing Hadoop clusters. | Cross-platform (Linux, Windows, macOS) |
| Tableau | Data visualization and reporting. | – User-friendly interface for creating interactive dashboards. – Rich collection of visualizations and interactive features. | – Costly for enterprise-level deployments. – Limited advanced statistical analysis capabilities. | Windows, Linux, macOS |
| TensorFlow | Building and deploying machine learning models. | – Comprehensive ecosystem for deep learning and neural networks. – Scalable and optimized for production use. | – Steeper learning curve for beginners. – Requires knowledge of Python or other programming languages. | Cross-platform (Linux, Windows, macOS) |
| BigML | Cloud-based machine learning platform. | – User-friendly interface for creating and deploying ML models. – Automated model optimization and hyperparameter tuning. | – Limited advanced customization options. – Cost may increase with larger datasets and usage. | Web-based platform, accessible from any modern browser. |
These data science tools provide a diverse set of functionalities for data analysis, visualization, and machine learning. Each tool possesses its own distinct advantages and disadvantages, and they are compatible with multiple platforms, affording users the flexibility to choose the one that best suits their specific needs and preferences.
1. Statistical Analysis System (SAS) – Renowned for Statistical Analysis Capabilities
SAS is a statistical and complex analytics tool developed by the SAS Institute. It is one of the oldest data analysis tools mainly built to deal with statistical operations. The Statistical Analysis System (SAS) is a software suite widely used for advanced analytics, business intelligence, and data management. It offers a range of features that facilitate data analysis, data manipulation, and statistical modeling. SAS is commonly used by professionals and organizations that heavily rely on advanced analytics and complex statistical operations. This reliable commercial software provides various statistical libraries and tools that can be used for modeling and organizing the given data.
The following are the key features and usage of these data science tools:
- Easy to learn as it comes with ample tutorials and dedicated technical support
- Simple GUI that produces powerful reports
- Carries out the analysis of textual content even with typo identification
- Provides a well-managed suite of tools dealing with areas such as data mining, clinical trial analysis, statistical analysis, business intelligence applications, econometrics, and time-series analysis.
Apache Hadoop is an open-source framework that helps in distributed processing and computing of large datasets over a cluster of thousands of computers, i.e., it can store and manage a large amount of data by distributing it in parts to thousands of computers. It is an ideal tool to deal with large data processing and high-level computations.
The following are some important features and usage of Hadoop:
- Efficiently scales large amounts of data on thousands of Hadoop clusters
- Uses Hadoop Distributed File System (HDFS) for data storage and to achieve parallel computing
- Provides fault tolerance and high availability even in unfavorable conditions
- Provides integrated functionality with other data processing modules such as Hadoop YARN, Hadoop MapReduce, and many others
Tableau is referred to as a data visualization software that comes with powerful graphics to create interactive visualizations. It is majorly used by industries working in the field of business intelligence and analytics.
The most significant feature of Tableau is its ability to interact with different spreadsheets, databases, online analytical processing (OLAP) cubes, etc. Apart from these features, Tableau can also visualize geographical data by plotting longitudes and latitudes on maps.
The following are the main features of Tableau:
- Allows to connect with and extract data from multiple data sources and has the ability to visualize large datasets to find patterns and correlations
- The desktop feature helps create customized reports and dashboards to get real-time insights and updates
- The cross-database join functionality allows to make calculated fields and join tables for solving complex data problems
4. TensorFlow – Powerful Machine Learning Framework or Library
TensorFlow is a powerful library that revolves around artificial intelligence, deep learning, and machine learning algorithms and helps in creating and training models and deploying them on various platforms, such as smartphones, computers, and servers, to achieve the functionalities assigned to respective models.
TensorFlow is considered to be one of the most flexible, fast, scalable, and open-source machine learning libraries, which is used extensively in the field of production and research. Data scientists prefer TensorFlow as it uses data flow graphs for numerical computations.
The following are important points to note about TensorFlow:
- Provides the architecture for deploying computation on diverse types of platforms, which include servers, CPUs, and GPUs
- Offers powerful tools to operate with data by filtering and manipulating it for performing data-driven numerical computations
- Flexible in conducting machine learning and deep neural network processes
BigML is a scalable machine learning platform that allows users to leverage and automate techniques such as classification, regression, cluster analysis, time series, anomaly detection, forecasting, and other prominent machine learning methods in a single framework. BigML provides a fully interchangeable, cloud-based GUI environment that aims at reducing platform dependencies for processing machine learning algorithms. It also offers customized software for using cloud computing for organizational needs and requirements.
The following are the major features and usages of BigML:
- Helps in processing machine learning algorithms
- Builds and visualizes machine learning models with ease
- Deploys methods such as regression (linear regression, trees, etc.), classification, and time-series forecasting for supervised learning
- Uses cluster analysis, association discovery, anomaly detection, etc., for unsupervised learning
KNIME’s ability to extract and transform data makes it one of the most essential and widely used tools in Data Science for data reporting, data mining, and data analysis. KNIME is an open-source platform and free to use in various parts of the world.
The following are the key features and usages of Knime:
- Uses a data pipelining concept, called the Lego of Analytics, for the integration of various data science components
- Easy-to-use GUI that helps perform data science tasks with minimum programming expertise
- Visual data pipelines can be used to create interactive views for the given dataset
RapidMiner is a software that provides an integrated data science platform used for data preprocessing and preparation, machine learning, deep learning, and predictive modeling deployment.
In data science, RapidMiner provides tools that allow you to design and modify your model from its initial phase until its deployment.
The following are the characteristics of RapidMiner:
- Uses the computational power of free studio and enterprise server resources for efficient model development
- Allows integration with Hadoop with the built-in RapidMiner Radoop
- Automated modeling helps generate predictive models
- Provides the feature of remote execution for analysis processes
8. Excel – Versatile Spreadsheet Software
Excel refers to the powerful analytical tool used extensively in the field of data science as it helps build powerful data visualizations and spreadsheets that are ideal for robust data analysis. Excel comes with numerous formulae, tables, filters, slicers, etc., and apart from all those functionalities, it also allows users to create their own custom formulae and functions. It can also be connected with SQL and further used for data analysis and data manipulation. Data scientists use Excel for data cleaning purposes as well due to its interactable GUI environment that helps in pre-processing data with ease.
The following are the key features of Excel:
- Good for cleaning and analyzing 2D (rows and columns) data
- Easy for beginners
- Helps sort and filter data with a single click to quickly and easily explore datasets
- Offers pivot tables to summarize data and operate functions, such as sum, count, and other metrics, in a tabular format
- Extracted visualizations help present a variety of creative solution.
9. Apache Flink – Powerful Stream Processing and Batch Processing Framework
Apache is known for providing tools and techniques in data science that speed up the analysis process. Flink is one of the best tools in Data Science offered by the Apache Software Foundation. Apache Flink is an open-source distributed framework that can perform scalable data science computations and quick real-time data analysis.
The following are the key features and usages of Apache Flink:
- Offers both parallel and pipeline execution of data flow diagrams at low latency
- Processes unbounded data streams that do not have a fixed start and endpoint
- Helps reduce the complexity during real-time data processing
Power BI is one of the essential tools of data science integrated with business intelligence. Rich and insightful reports can be generated from a given dataset by using Power BI.
The following are the key features and usages of Power BI:
- It can be combined with other data science tools from Microsoft for data visualization
- It can help to create data analytics dashboards
- It can transform incoherent datasets into coherent datasets
- It can develop logically consistent datasets and generate rich insights
- It facilitates making eye-catching visual reports that can be understood by nontechnical professionals as well
Data scientists also play a vital role in the digital marketing sector. Google Analytics is one of the top data science tools used in the industry for digital marketing.
The following are the key features and usages of Google Analytics:
- Helps web admins access, analyze, and visualize data to gain a better understanding of user interaction with websites
- Helps in making better marketing decisions by recognizing and using the data trail left behind by users on a website
- With the help of its easy-to-use interface and high-end analytics, Google Analytics can also be used by nontechnical professionals to perform data analytics.
12. Python – Versatile and User-Friendly Programming Language
Python is one of the most dominant languages in the field of data science today because of its flexibility, ease of use in terms of syntax, open-source nature, and ability to handle, clean, manipulate, visualize, and analyze data. Python was essentially developed as a programming language. However, it offers a wide range of libraries, such as TensorFlow, Seaborn, etc., that are attractive to both programmers and data scientists alike. Moreover, there are various other tools connected to and built with the help of Python, such as Dask, SciPy, Cython, Matplotlib, and HPAT.
The following are the key features and usages of Python:
- Used for data cleaning, data manipulation, data visualization, and data analysis
- Helps establish a connection with various other tools such as Cython and Desk.
- Preferred by data scientists, beginners, and experienced professionals
R is a powerful and respected programming language in the world of data science. R is extensively used for statistical computing and graphics. It provides numerous packages and libraries that support different phases of the data science life cycle. Apart from all of its functionalities, R has an incredibly large and supportive community as well, where you can find an answer to any question or query that you may encounter while working with R.
To use this magical language and play around with it, you need RStudio. It is open-source software that helps you handle, clean, and manipulate data, and then analyze the same. RStudio provides a user-friendly interface for using R effectively.
The following are the key features and usages of R:
- Provides a large and coherent collection of tools for data analysis
- Offers effective data handling and storage facilities
- Perfect for statistical computing, design, and analyses
- Provides graphical functionalities for data analysis and displaying the output either on a computer screen or on paper
DataRobot is an AI-driven development and automation platform that helps in building accurate and automotive predictive models. DataRobot assists in the easy implementation of a wide range of machine learning algorithms, including regression, classification, and clustering models.
The following are the key features and usages of DataRobot:
- Provides parallel programming by directing thousands of servers to perform multitasking on data analysis, data validation, data modeling, and so on
- Offers lightning-fast speed when it comes to building, training, and testing machine learning models
- Helps in scaling up the entire machine learning process
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15. D3.js – Data Visualization Library
D3.js is a JavaScript library that allows you to make automated visualizations on web browsers. It provides several APIs, using which you can access numerous functions to create interactive data visualizations and do meaningful data analysis on your browser. Another significant feature of D3.js is that it creates dynamic documents by allowing updates on the client side and reflects the changes in visualizations with respect to the changes made in the data on the browser.
The following are some of the important features of D3.js:
- Emphasizes the usage of web standards to utilize the full potential of modern browsers
- Merges powerful visualization modules and a data-driven process into document object model (DOM) manipulation
- Helps to apply data-driven transformations to documents after binding data to DOM
Azure HDInsight is a full-fledged cloud platform created by Microsoft to aid processes such as data processing, data storage, and data analytics. Big enterprises, including Jet, Adobe, Milliman, etc., use this tool to store, process, manage and extract valuable insights from a large amount of data.
The followings are the features and usages of Microsoft HDInsight:
- Provides support for integrating with different tools, such as Apache Spark and Apache Hadoop, for data processing
- Uses Windows Azure Blob as the default storage system to effectively manage sensitive data across thousands of nodes
- Provides Microsoft R Server as a function that supports R for performing statistical analysis and helps in creating robust machine learning models
Jupyter is an open-source data science tool that is predominantly used for coding Python programs but also supports other languages such as Julia, R, and Fortran. Jupyter works as a computational notebook that consists of different components including code, visualizations, equations, and text.
One of the most prominent features of Jupyter is that you can easily share your code files or your work with your peers in the form of an executable notebook and have interactive output in the form of mind-blowing plots, images, etc. You can easily integrate this tool with other tools, such as Apache Spark, that are used extensively in the process of data analysis.
The following are the important characteristics of Jupyter:
- Supports more than 40 programming languages
- Offers a user-friendly interface for executing code files
- Provides interactive features with the help of computational kernels
- Establishes connections with other data-driven solutions such as Apache Spark
18. Matplotlib – Renowned Visualization and Plotting Library
Matplotlib is a visualization and plotting library developed for Python. Matplotlib is one of the most powerful tools for generating interactive graphs with analyzed data. It is mainly used for plotting much-needed and complex graphs by using simple Python code. By working with these Data Science tools, you can create different types of graphs, such as histograms, bar plots, scatter plots, etc., by using Pyplot, which is considered an essential module of Matplotlib.
The following are the major features and usages of Matplotlib:
- Builds diverse plots, histograms, power spectra, bar charts, scatterplots, error charts, and more with simple lines of code
- Helps create compelling visualizations
- Provides certain formatting function line styles, axes properties, font properties, etc., which can be used to increase the readability of plots
- Offers several export options to extract the plot or visualization and put it on the platform of your choice
19. MATLAB – Multi-Paradigm Programming Language
Matrix Laboratory (MATLAB) is a multi-paradigm programming language that helps in providing a numerical computing environment for processing mathematical expressions. The most significant feature of this language is that it helps users with algorithmic implementation, matrix functions, and statistical modeling of data; it is extensively used in different scientific disciplines.
MATLAB is used in the field of data science for simulating fuzzy logic and neural networks and for creating powerful visualizations.
The following are the usages of MATLAB:
- Helps develop algorithms and models
- Merges the desktop environment with a programming language for iterative analysis and design processes
- Provides an interface consisting of interactive apps to test how different algorithms work when applied to the data at hand
- Helps automate and reproduce work by automatically generating a MATLAB program
- Scales up the process of analysis to run on clusters, cloud, or GPUs
QlikView is a leading business intelligence and analytics tool used for conversational analytics, data integration, and converting raw data into informative insights. It facilitates in-memory data processing and helps store the processed data in self-created reports.
QlikView is also considered one of the most powerful data science tools for visually analyzing data to derive useful business insights. It is used by more than 24,000 organizations globally.
The following are the features and usages of QlikView:
- Provides tools to create powerful dashboards and detailed reports
- Offers in-memory data processing for the efficient and fast creation of reports for end-users.
- Generates and automates the associations and relations in data by using the data association feature
- Helps to maximize the performance of enterprises, big or small, with the help of features such as collaboration and sharing, data security provisions, integrated framework, and guided analytics for the efficient working of an organization
21. PyTorch
PyTorch is an open-source machine learning library facilitating the expedited development of deep learning models with a robust foundation in data science. It offers a dynamic computational graph that renders a high level of flexibility and enables the performance of complex computational tasks.
The key features of PyTorch include:
- PyTorch has a user-friendly interface, which simplifies the learning curve for beginners.
- PyTorch’s dynamic computation graph eases the debugging process.
- PyTorch provides a vast array of libraries and tools for data science, making it a comprehensive suite for machine learning and AI projects.
- It supports the majority of the machine learning algorithms and models that cater to different data science needs.
- The deep integration into Python allows developers to create complex architectures, while the strong GPU acceleration enhances the performance of computation-heavy tasks.
22. Pandas
Pandas, a high-level data manipulation tool developed by Wes McKinney, is essential in the domain of data science and analysis. The Pandas data science tool is engineered for cleaning, aggregating, transforming, visualizing, and more, providing a one-stop solution for various data handling tasks. It has a broad range of operations, including from academics to commercial purposes, which make sure the task delivery is efficient.
Primary features of Pandas a data science tool:
- Pandas ability to work with a variety of data formats such as CSV, Excel, SQL, and more.
- It provides two fundamental data structures, Pandas Series and Pandas DataFrame, which are immensely flexible and allow for efficient data manipulation and analysis.
- The tool is equipped with robust functions for time series analysis, making it a compelling choice for financial applications.
- It enables users to carry out statistical analysis, data cleaning, data transformation, and machine learning tasks with ease and efficiency.
23. Scikit-Learn
Scikit-learn is a versatile and widely acclaimed data science tool, it is developed with the precision of Python programming language. This library, renowned for its ease of use and efficiency, encapsulates a large set of algorithms facilitative for supervised and unsupervised learning.
Key features and usage of Scikit-learn:
- One of the key features of Scikit-learn is its provision for a myriad of machine learning algorithms, making it a formidable choice for tasks ranging from regression, classification, clustering, to dimensionality reduction.
- Its compatibility with Python numerical and scientific libraries like NumPy and SciPy further augments its utility.
- Scikit-learn finds extensive application in natural language processing tasks.
- Scikit-learn natural language processing is exhibited through feature extraction from text and image data, alongside providing tools for feature selection.
- From predictive analytics to statistical modeling, the Scikit-learn Python library is an ideal tool that transforms raw data into insightful foresight.
- It’s also employed in predictive modeling, which is imperative for decision-making in businesses.
24. WEKA(Waikato Environment for Knowledge Analysis)
It is a prominent data science tool originating from the University of Waikato, New Zealand. This data science tool harbors a collection of machine learning algorithms tailored for data mining tasks. WEKA’s suite of algorithms, streamlined data preprocessing tools, and adeptness for various statistical modeling tasks render it an indispensable asset in the data science domain.
WEKA a data science tool’s key features:
- Its open-source nature, providing an avenue for customization and scrutiny of the underlying WEKA algorithms.
- WEKA’s graphical user interface(GUI) facilitates ease of interaction, even for those new to data science.
- WEKA supports various platforms, showcasing its flexibility and inclusivity.
WEKA’s usages:
- Its prowess in WEKA data mining finds applications in market research, where discerning patterns is crucial.
- The WEKA machine learning capability significantly contributes to predictive modeling, aiding businesses in decision-making processes.
- WEKA serves as a teaching aid, bridging the theoretical underpinnings of machine learning and real-world dataset applications.
25. Minitab
Minitab, a leading data science tool, manifests as a powerful asset for individuals and organizations keen on diving into data analysis. The Minitab data science tool is renowned for its user-friendly interface which facilitates easy navigation and operation even for data science beginners. It’s an ideal data science tool where simplicity meets robust analytical capabilities.
Usage and Key features of Minitab include:
- Its ability to execute on a wide range of statistical analyses, robust data visualization tools, and predictive modeling.
- This data science tool provides a platform for performing essential tasks such as hypothesis testing, regression analysis, and variance analysis with remarkable ease and precision.
- Utilizing Minitab for data science goes beyond mere data analysis, it’s about making data interpretation a smooth task.
- The interactive nature of Minitab aids in elucidating complex data insights, making it a desirable choice for professionals across various sectors like manufacturing, finance, and healthcare.
- The usages of Minitab extend to quality control and Six Sigma projects, where data-driven decisions are crucial.
- With Minitab, extracting actionable insights from a sea of data becomes less daunting, making it an essential companion for those striving for excellence in data analysis.
Conclusion
In today’s data-driven world, data plays a crucial role in the survival of any organization in this competitive era. By utilizing data, data scientists provide impactful insights to the key decision-makers of organizations. This is almost impossible to imagine without the use of the above-listed powerful data science tools.
It provides a way for analyzing data, creating interactive visualizations with aesthetics, and developing powerful and automated predictive models using machine learning algorithms, which ease the process of extracting and delivering valuable insights from raw and seemingly useless data.
After going through the entire blog, you might have understood that one of the most prominent features of all these tools is that they provide a user-friendly interface with built-in functions for conducting computing on data, increasing efficiency, and reducing the amount of code needed to extract value from the given data resources for fulfilling the needs of end-users. Therefore, selecting one tool from among many should depend on the specific requirements of different use cases.