18 data science tools to consider using in 2026

1. Apache Spark

Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has made it a widely used platform since it was created in 2009, resulting in the Spark project being one of the largest open source communities among big data technologies.

Due to its speed, Spark is a good fit for continuous intelligence applications driven by near-real-time processing of streaming data. However, it's a general-purpose distributed processing engine that's equally suited for SQL batch jobs, such as extract, transform and load processes. In fact, Spark initially was touted as a faster alternative to the MapReduce engine for batch processing in Hadoop clusters.

Spark is still often used with Hadoop, but it also runs standalone on top of other file systems and data stores. It features an extensive set of developer libraries and APIs, including a machine learning library and support for Python, Scala, Java, and R in addition to SQL. These capabilities make it easier for data scientists and analysts to develop Spark applications.

2. D3

Another open source tool, D3 is a JavaScript library for creating custom data visualizations in a web browser. Short for data-driven documents, D3 uses web standards such as HTML, Scalable Vector Graphics and CSS rather than its own graphical vocabulary. D3's developers describe it as a flexible tool that enables users to design dynamic, interactive visualizations.

First released in 2011 and originally known as D3.js, the tool lets visualization designers use the Document Object Model API to bind data to documents representing the contents of a graphic; DOM manipulation methods can then be applied to make data-driven transformations to the documents. Animations, annotation capabilities and user-interaction features such as panning and zooming can be built into visualizations.

D3 includes more than 30 modules and 1,000 visualization methods, making it complicated to learn. In addition, even basic charts might require significant coding -- and many data scientists don't have JavaScript skills. As a result, they might be more comfortable with Tableau, Power BI or another commercial data visualization tool, while D3 is used by data visualization developers and specialists who are also members of data science teams.

3. IBM SPSS

IBM SPSS is a family of software for managing and analyzing complex statistical data and creating predictive models. It includes two primary products, IBM SPSS Statistics and IBM SPSS Modeler, plus several others that work with or incorporate them. IBM acquired the technologies when it bought SPSS Inc. in 2009.

SPSS Statistics is a statistical analysis tool that helps users identify complex relationships, patterns and trends in data. It also supports data preparation, predictive modeling and forecasting. The tool includes a menu-driven UI, its own command syntax and sets of Python and R extension commands that add analytics capabilities beyond its built-in ones. AI Output Assistant, a feature added in 2025, interprets tables, charts and statistical outputs, generates data visualizations and summarizes analytics results.

SPSS Modeler is a data science and machine learning tool that focuses on data mining and predictive modeling. It's designed for ad hoc analytics applications that combine data from multiple sources, while SPSS Statistics is geared toward regular reporting on specific data sets. SPSS Modeler includes a drag-and-drop UI and supports various types of machine learning algorithms. It also provides model management and deployment capabilities and can run R extensions and Python scripts for Spark.

Users can export prepared data from SPSS Statistics to SPSS Modeler and run predictive models created in SPSS Modeler in the statistical analysis tool.

4. Julia

Julia is an open source programming language used for numerical computing and data science applications, such as machine learning. In a 2012 blog post announcing Julia's initial release, its four creators said they set out to design a single language that met all their needs. A key goal was to avoid the need to write programs in one language and then convert them to another for execution.

To that end, Julia combines the convenience of using a high-level dynamic language with performance that's comparable to statically typed languages, such as C and Java. Users don't have to define data types in programs, but an option allows them to do so. A multiple dispatch approach used at runtime also helps boost execution speed.

The documentation for Julia notes that because its compiler differs from the interpreters in data science languages like Python and R, new users "may find that Julia's performance is unintuitive at first." But, it claims, "once you understand how Julia works, it is easy to write code that is nearly as fast as C."

5. Jupyter Notebook/JupyterLab

Jupyter Notebook and JupyterLab are open source web applications that enable interactive collaboration among data scientists, data engineers, mathematicians, researchers and other users. They're computational notebook tools used to create, edit and share software code, as well as explanatory text, images and other information. For example, Jupyter users can add code, computations, comments and data visualizations to a single notebook document, which can then be shared with and revised by colleagues.

As a result, notebooks "can serve as a complete computational record" of interactive sessions involving various data science team members, according to Jupyter Notebook's documentation. The notebook documents are JSON files with built-in version control capabilities. In addition, users can render notebooks as static webpages for viewing by people who don't have Jupyter installed on their systems.

Jupyter Notebook was the original tool -- it was initially part of the open source IPython interactive toolkit project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name, but in addition to supporting those three languages, Jupyter provides modular kernels for dozens of others. JupyterLab is a web-based UI added in 2018 that's more flexible and extensible than Jupyter Notebook.

6. Keras

Keras is a programming interface that simplifies the use of several popular machine learning platforms by data scientists. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow, PyTorch and JAX. Keras initially supported multiple back ends, then was tied exclusively to TensorFlow starting with its 2.4.0 release in 2020. However, multiplatform support was restored in Keras 3.0, a full rewrite released in late 2023.

As a high-level API, Keras was designed to accelerate implementation of machine learning models -- in particular, deep learning neural networks -- through a "quick and easy" development process, as the technology's documentation puts it. Keras enables data scientists to experiment during the model development process with less coding than other deep learning options require. Models can also be run on all the supported back-end platforms without any code changes.

The Keras framework includes a sequential interface for creating relatively simple linear stacks of neural-network building blocks called layers with inputs and outputs, as well as a functional API for building more complex graphs of layers and writing deep learning models from scratch.

7. Matlab

Offered by software vendor MathWorks since 1984, Matlab is a high-level programming language and analytics platform for numerical computing, mathematical modeling and data visualization. It's primarily used by conventional engineers and scientists to analyze data, design algorithms and develop embedded systems for wireless communications, industrial control, signal processing and other applications. Users often pair it with a companion Simulink tool that offers model-based design and simulation capabilities.

While Matlab isn't as widely used in data science applications as languages such as Python, R and Julia, it does support machine learning and deep learning, predictive modeling, big data analytics, computer vision and other work done by data scientists. Data types and high-level functions built into the platform are designed to speed up exploratory data analysis and data preparation in analytics applications.

Matlab -- short for matrix laboratory -- is considered relatively easy to learn and use. The platform includes prebuilt applications but also lets users build their own. It also provides a library of add-on toolboxes with discipline-specific software and hundreds of built-in functions, including the ability to visualize data in 2D and 3D plots.

8. Matplotlib

Matplotlib is an open source Python plotting library that's used to visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib. It works in Python scripts, the Python and IPython shells, Jupyter Notebook, JupyterLab, web application servers and various GUI toolkits.

The library's large codebase can be challenging to master, but it's organized in a hierarchical structure that enables users to build visualizations primarily with high-level commands. The top component in the hierarchy is pyplot, a module that provides a state-machine environment and a set of simple plotting functions like those in Matlab.

First released in 2003, Matplotlib also includes an object-oriented interface that supports low-level commands for more complex data plotting and can be used with pyplot or on its own. The library is primarily focused on creating 2D visualizations but offers an add-on toolkit with 3D plotting features.

9. NumPy

Short for Numerical Python, NumPy is an open source Python library that's used widely in scientific computing as well as data science and machine learning applications. The library consists of multidimensional array objects and processing routines that enable various mathematical and logic functions. It also supports linear algebra, random number generation and other operations.

One of NumPy's core components is the N-dimensional array, or ndarray, which represents a collection of items that are the same type and size. An associated data-type object describes the format of the data elements in an array. The same data can be shared by multiple ndarrays, and data changes made in one can be viewed in another.

NumPy was created in 2005 by combining and modifying elements of two earlier libraries. It's generally considered one of the most useful Python libraries due to its numerous built-in functions. NumPy is also known for its speed, which partly results from the use of optimized C code at its core. In addition, various other Python libraries are built on top of NumPy.

10. Pandas

Another popular open source Python library, pandas is used to manipulate and analyze data. Built on top of NumPy, it features two primary data structures: Series, a one-dimensional array that holds data of any type, and DataFrame, a two-dimensional structure that can contain columns of different data types and supports data manipulation with integrated indexing. Both accept data from NumPy ndarrays and other inputs. A DataFrame can also incorporate multiple Series objects.

Created in 2008, pandas provides built-in data visualization capabilities and exploratory data analysis functions. It supports file formats and languages such as CSV, SQL, HTML and JSON. Additional features include data aggregation and transformation, integrated handling of missing data and the ability to quickly merge and join data sets.

To optimize its performance, key code paths in pandas are written in C or Cython, a superset of Python designed to provide C-like performance. The library can be used with various kinds of analytical and statistical data, including tabular, time series and text data sets.

11. Python

Python is the most widely used programming language for data science applications and scientific and numeric computing, and one of the most popular languages overall. The Python open source project's website describes it as a high-level interpreted, interactive, object-oriented language with a simple syntax, built-in data structures, and dynamic typing and binding capabilities. Python also supports both procedural and functional programming, as well as extensions written in C or C++.

The multipurpose language is used for a wide range of data-driven tasks, including data analysis, data visualization, AI, natural language processing and robotic process automation. Python includes an extensive library of functions and modules that can streamline application development, and thousands of third-party modules are available in the Python Package Index repository.

Python 3.x is the recommended version for production use. Older Python 2.x releases can still also be downloaded from the Python website, but maintenance and technical support for the 2.x line ended in 2020.

12. PyTorch

PyTorch is an open source Python library used to build and train deep learning models based on neural networks. It was designed to be easier to use than Torch, a precursor machine learning framework written primarily in the Lua programming language. PyTorch also provides more flexibility and speed than Torch, according to its creators.

First released in 2017, PyTorch uses array-like tensors to encode model inputs, outputs and parameters. Its tensors are similar to NumPy's multidimensional arrays, which can be converted into tensors for processing in PyTorch, and vice versa. By default, PyTorch runs in an "eager mode" that executes computational operations immediately, an approach suited to model development. But operations can also be combined into computational graphs to deliver higher performance in production deployments.

Other PyTorch components include an automatic differentiation package; a module for building neural networks; and ExecuTorch, a tool for deploying models on mobile phones and edge devices. In addition to the main Python API, PyTorch provides a C++ one that can be used as a separate front-end interface or to create extensions for Python applications. Users can run models built in PyTorch on CPUs, GPUs and custom hardware accelerators.

13. R

The R programming language is an open source environment designed for statistical computing and graphics applications as well as data manipulation, analysis and visualization. Many data scientists, academic researchers and statisticians use R to retrieve, cleanse, analyze and present data, making it one of the most popular languages for data science and advanced analytics.

Thousands of user-created packages with libraries of code that enhance R's functionality are also available. One example is ggplot2, a well-known package for creating graphics that's part of the tidyverse collection of R-based data science tools. In addition, multiple vendors offer integrated development environments and commercial code libraries for R.

R is an interpreted language, like Python, and it has a reputation for being relatively intuitive. It was created in the 1990s as an alternative version of S, a statistical programming language developed in the 1970s. R's name is both a play on S and a reference to the first letter of the names of its two creators.

14. SAS

SAS is an integrated software suite for statistical analysis, advanced analytics, AI, BI and data management. Developed and sold by software vendor SAS Institute Inc., the platform helps users integrate, cleanse, prepare and manipulate data, then analyze it using different statistical and data science techniques. SAS supports a range of analytics tasks, from basic BI and data visualization to risk management, operational analytics, data mining, predictive analytics and machine learning.

SAS development began in 1966 at North Carolina State University. Its use began to grow in the early 1970s, and SAS Institute was founded in 1976 as an independent company. The software was initially built for use by statisticians -- SAS was short for Statistical Analysis System. But over time, the SAS platform expanded to include a broad set of functionality.

Development and marketing are now focused primarily on SAS Viya, a cloud-based version of the platform that was launched in 2016 and redesigned to be cloud-native in 2020. Viya supports Python, R, Java, Lua and REST APIs for programming. It also includes built-in AI governance features and SAS Viya Copilot, a conversational AI assistant that uses Microsoft Foundry services to help users generate SAS code and build AI and analytics models.

15. Scikit-learn

Scikit-learn is an open source Python machine learning library that's built on the SciPy and NumPy scientific computing libraries and Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. It also provides functionality for model fitting, selection and evaluation, as well as data preprocessing and transformation.

Initially called scikits.learn, the library began as a Google Summer of Code project in 2007 and was publicly released in 2010. The first part of its name is short for SciPy toolkit and is also used by other SciPy add-on packages. Scikit-learn primarily works on numeric data that's stored in NumPy arrays or SciPy sparse matrices.

The library's suite of tools also enables other tasks, such as loading data sets and creating workflow pipelines that combine data transformer objects and estimators. But scikit-learn has some limits due to design constraints. For example, it doesn't support deep learning or reinforcement learning, and GPUs aren't supported by default. The library's website also says its developers "only consider well-established algorithms for inclusion."

16. SciPy

SciPy is another open source Python library that supports scientific computing. Short for Scientific Python, it features a set of mathematical algorithms and high-level commands and classes for data manipulation and visualization. The library is organized into more than a dozen subpackages that contain algorithms and functions for different scientific computing domains. That includes areas such as data optimization, integration and interpolation, as well as clustering, image processing and statistics.

SciPy is built on top of NumPy and can operate on NumPy arrays. But it extends beyond NumPy's capabilities by providing additional array computing tools and specialized data structures, including sparse matrices and K-dimensional trees.

SciPy also predates NumPy: It was created in 2001 by combining multiple add-on modules from the Numeric library, one of NumPy's two predecessors. Like NumPy, SciPy uses compiled code to optimize performance. In its case, most of the performance-critical parts of the library are written in C, C++ or Fortran.

17. TensorFlow

TensorFlow is an open source machine learning platform developed by Google that's particularly popular for building deep learning neural networks. Like PyTorch, TensorFlow structures data inputs as tensors akin to NumPy multidimensional arrays. It supports the same two processing methods as PyTorch, but in reverse: By default, TensorFlow creates computational graphs to flow data through a set of operations specified by developers, while also offering an eager execution programming environment that runs operations individually.

Google made TensorFlow open source in 2015, and Release 1.0.0 became available in 2017. TensorFlow uses Python as its core programming language and incorporates Keras as a high-level API for building and training models. Alternatively, a TensorFlow.js library enables model development in JavaScript, and custom operations -- ops, for short -- can be built in C++.

The platform also includes TFX, a module initially called TensorFlow that automates the deployment of production machine learning pipelines. In addition, it supports LiteRT, a runtime tool for mobile and IoT devices. TensorFlow models can run on CPUs, GPUs and Google's special-purpose Tensor Processing Units.

18. Weka

Weka is an open source workbench that provides a collection of machine learning algorithms for use in data mining tasks. Weka's algorithms, called classifiers, can be applied directly to data sets without any programming via a GUI or a command-line interface that offers additional functionality. They can also be implemented through a Java API.

The workbench can be used for classification, clustering, regression, and association rule mining applications. It also includes a set of data preprocessing and visualization tools. Weka supports integration with R, Python, Spark and other libraries, such as scikit-learn. For deep learning uses, an add-on package combines it with the Eclipse Deeplearning4j library.

Weka is free software licensed under the GNU General Public License. It was developed at the University of Waikato in New Zealand starting in 1992. An initial version was rewritten in Java to create the current workbench, which was first released in 1999. Weka stands for the Waikato Environment for Knowledge Analysis. It is also the name of a flightless bird native to New Zealand that the technology's developers say has "an inquisitive nature."

Data science and machine learning platforms

Numerous software vendors offer commercially licensed platforms that provide integrated functionality for machine learning, AI and other data science applications. These product offerings are diverse: They include machine learning operations hubs, automated machine learning platforms and full-function analytics suites, with some products combining MLOps, AutoML and analytics capabilities. Many of the platforms incorporate some of the data science tools listed above.

IBM SPSS Modeler, Matlab and SAS can also be counted among the data science platforms. Other prominent platform options for data science teams include the following technologies:

• Altair RapidMiner.
• Alteryx One.
• Amazon SageMaker.
• Anaconda.
• Azure Machine Learning.
• BigML.
• Databricks Data Intelligence Platform.
• Dataiku.
• DataRobot.
• Domino Enterprise AI Platform.
• Google Cloud Vertex AI Platform.
• H2O AI Cloud.
• IBM Watson Studio.
• Knime.
• Qubole.
• Saturn Cloud.

Some platforms, such as Dataiku and H2O, are also available in free open source or community editions. Knime combines an underlying open source analytics platform with a commercial Knime Business Hub software package that supports team-based collaboration and analytics workflow automation, deployment and management capabilities.

Editor's note: TechTarget editors updated this article in February 2026 for timeliness and to add new information.

Mary K. Pratt is an award-winning freelance journalist with a focus on covering enterprise IT and cybersecurity management.