Top 12 Data Science Tools to Consider in 2026

The growing volume and complexity of organizational data and its critical role in decision-making inspire organizations to invest in people, processes, and technology to unlock value from data assets. 

Data science teams can choose from diverse tools and platforms to build their portfolios. Here’s a list of the 12 most widespread data science tools data teams can’t miss in 2026.

Key Takeaways

• lakeFS enables Git-like data versioning for data science workflows, allowing teams to manage data in object storage with isolated branches, ensuring safe experimentation and reproducibility without duplicating data.
• Data quality and reproducibility are central lakeFS use cases: Teams can create isolated branches for data preparation, track experiment results across branches, and revert datasets for reproducibility and compliance.
• CI/CD for data benefits from lakeFS integration: lakeFS supports hooks and automated workflows, making it easier to enforce testing, validation, and governance practices across machine learning and analytics pipelines.
• Tooling diversity demands flexible version control solutions: lakeFS complements commonly used tools like Jupyter, TensorFlow, and Apache Spark by adding essential capabilities for managing the data lifecycle that are missing in those platforms.
• Data security and governance remain key challenges: The article highlights growing concerns over compliance and licensing costs, reinforcing the value of open-source tools like lakeFS for secure, auditable, and cost-effective data management.

What are Data Science Tools?

Data science tools are specialized software solutions that enable data practitioners to efficiently collect, process, analyze, and visualize vast amounts of data. Such tools include features for data cleaning, statistical modeling, machine learning, and data visualization, allowing users to identify trends, develop insights, and make data-driven decisions. They also empower teams to transform raw data into actionable insights by combining advanced computing capabilities with intuitive interfaces, thereby fostering innovation and driving strategic growth.

Top 12 Data Science Tools and Their Features

1. Python

Python is the most extensively used data science and machine learning programming language. The Python open-source project’s website describes it as “an interpreted, object-oriented, high-level programming language with dynamic semantics,” built-in data structures, and dynamic typing and binding features. Another advantage is Python’s concise syntax, which makes the language easy to learn.

The multipurpose language has many applications, including data analysis, data visualization, artificial intelligence, natural language processing, and robotic process automation. Python enables developers to build web, mobile, and desktop applications. In addition to object-oriented programming, it supports procedural, functional, and other types and extensions written in C or C++.

2. Microsoft Power BI

Power BI is user-friendly software that provides non-technical teams with the tools to analyze, distribute, and visualize data on a business intelligence platform. It allows users to uncover patterns and insights in data, integrate disparate datasets, and transform raw data into an intelligible data model. 

As a Microsoft product, Power BI works seamlessly with Azure, Microsoft’s cloud computing service, which can help ease data administration. It naturally integrates with many other Microsoft products, making it a suitable choice for data scientists working within this ecosystem.

In addition to simple data visualization and interactive dashboards, Power BI can generate complicated data models that display relationships between data elements. 

3. TensorFlow

TensorFlow is an open-source machine learning technology that is particularly well-suited for developing deep neural networks. The platform accepts tensors, similar to NumPy multidimensional arrays, and then utilizes a graph structure to process the data through a series of computational operations specified by developers. 

TensorFlow also features an eager execution programming environment that executes operations without graphs, providing greater flexibility for research and debugging machine learning models.

Google published TensorFlow as an open-source project in 2015. Its core programming language is Python, and Keras serves as a high-level API for model development and training. 

The platform also contains a TensorFlow Extended (TFX) module that enables the end-to-end deployment of production machine learning pipelines. It also supports LiteRT, a runtime tool for mobile and IoT devices formerly known as TensorFlow Lite. TensorFlow models can be learned and executed on CPUs, GPUs, and Google’s specialized Tensor Processing Units.

4. SQL

SQL (Structured Query Language) is a programming language used to manage and manipulate relational databases. A relational database organizes data into tables made up of rows and columns. 

SQL has been around since the 1970s and is popular because it effortlessly integrates with various computer languages. Various sectors and industries use SQL, like Python, for database management, making SQL skills applicable to a wide range of roles and responsibilities.

SQL is commonly used to clean data, helping ensure your data is accurate, consistent, and contains unique values. You can also practice writing complicated queries to manage data more efficiently and integrate SQL with other apps to improve data manipulation.

5. R

R is an open-source programming language used for statistical computing and graphical applications, as well as data management, analysis, and data visualization. It’s an interpreted language similar to Python, known for its ease of use. It was developed in the 1990s as an alternative to S, a statistical programming language introduced in the 1970s.

The language is widely used by data scientists, university researchers, and statisticians to retrieve, cleanse, analyze, and present data. No wonder it’s one of the most widespread languages for data science and advanced analytics.

The R Foundation supports it, and the project includes thousands of user-created packages with code libraries that improve R’s capabilities. One example is ggplot2, a well-known graphics package. In addition, some software providers offer integrated development environments and commercial code libraries for R.

6. Jupyter Notebooks

Jupyter Notebook, an open-source web tool, facilitates interactive collaboration among data scientists, data engineers, and other users. It’s a computational notebook application that enables you to write, edit, and share code, as well as explanatory text, photos, and other data. 

For example, Jupyter users can combine software code, computations, comments, data visualization, and rich media representations of computation results into a single document known as a notebook, which team members can then share and change.

According to Jupyter, notebooks “can serve as a complete computational record” of interactive sessions among data science team members. The notebook papers are JSON files with versioning capabilities. Additionally, a Notebook Viewer service enables users to output notebooks as static webpages for those who do not have Jupyter installed on their systems.

Jupyter Notebook has its roots in the Python programming language. It was previously part of the open-source IPython interactive toolkit project before being separated in 2014. Jupyter’s name comes from a vague combination of Julia, Python, and R. In addition to those three languages, Jupyter supports dozens more through modular kernels. The open-source project also provides JupyterLab, a newer web-based UI that is more adaptable and expandable than the original.

7. KNIME

KNIME is an open-source data analytics platform that allows data scientists and analysts to develop and execute a variety of data workflows without having to code. Instead, KNIME users simply connect numerous data processing and analysis modules, known as nodes, to form the architecture required to interpret and analyze their datasets.

It provides almost all of the features teams need natively. And if a feature isn’t accessible, they can use R or Python directly in KNIME. 

KNIME is a straightforward tool for identifying hidden patterns in massive datasets. By linking nodes in KNIME, data scientists can quickly and easily automate processes for their daily applications. KNIME nodes enable customizable automated workflows for data pretreatment, cleaning, and transformation.

8. GitHub

Git is a version control system that tracks changes in source code over time.

When multiple data scientists work on the same project without a code version control system, it inevitably creates chaos. Resolving conflicts becomes impossible if you can’t keep track of their changes, and combining them into a single fundamental fact is extremely difficult. 

Git and higher-level services built on top of it (such as GitHub) provide tools to help solve this problem.

Typically, there is a single central repository that individual users clone to their local workstation. When users save relevant work, they transmit it back to the central repository by operations like “push” and “merge.”

GitHub is a web platform built on top of Git technology to make things easier. It also includes capabilities such as user management, pull requests, and automation. Other choices include GitLab and Sourcetree.

9. SAS

SAS is a comprehensive software suite for statistical analysis, advanced analytics, business intelligence, and data management. The platform, developed and supplied by SAS Institute Inc., enables users to combine, cleanse, prepare, and alter data. 

Teams can use SAS for various purposes, including fundamental business intelligence and data visualization, risk management, operational analytics, data mining, predictive analytics, and machine learning.

SAS Viya, a cloud-based version of the platform introduced in 2016 and redesigned to be cloud-native by 2020, is now the primary focus of development for the company behind SAS.

10. Tableau

Tableau is popular among data scientists because its drag-and-drop interface makes it simple to simultaneously sort, compare, and analyze data from multiple sources. Its data organization and visualization tools simplify the process of organizing disparate data into coherent, visual narratives.

Tableau’s built-in tools and integrated features help users manage, display, and organize data from multiple sources in a single, fully supported environment. It connects to various data sources, including spreadsheets, databases, cloud services, big data platforms, and programming languages like R and Python.

Tableau’s collaborative features allow data scientists to share their ideas and visualizations effortlessly. They can also publish interactive dashboards to Tableau Server or Tableau Online, providing a larger audience with real-time access to data and analysis.

11. Apache Spark

Apache Spark is an open-source data processing and analytics engine capable of handling massive volumes of data up to several petabytes. Spark’s capacity to analyze data quickly has driven tremendous growth in its use since its inception in 2009, making the Spark project one of the largest open-source communities in big data technology.

Spark’s speed makes it ideal for continuous intelligence applications requiring near-real-time stream data analysis. However, since Spark is a general tool for processing data, it works well for tasks like extracting, transforming, and loading data, as well as for running other SQL batch processes. 

Spark includes a machine learning library and support for key programming languages, enabling data scientists to easily utilize the platform.

12. Microsoft Excel

This list wouldn’t be complete without this classic. Excel is a spreadsheet tool in Microsoft’s Office suite, which lets users organize, calculate, and format data in spreadsheet format. 

Excel makes it easier for data analysts to convey information because it is organized in a simple, row-and-column format. Advanced users can utilize formulas to analyze, summarize, and visualize data. Excel may appear to be a simple tool, but its extensive capabilities make it a crucial component of a data scientist’s toolkit.

Excel’s powerful functions and formulas allow data scientists to examine data, generate interactive visualizations with pivot tables, and automate repetitive operations and analyses.

Top Data Science Tools: Comparison Table

Tool	Key features	Pros	Cons
Python	• High-level, interpreted, object-oriented language • Concise, readable syntax • Supports multiple paradigms (procedural, functional, object-oriented) • Massive ecosystem: data analysis, ML, NLP, automation, web/mobile/desktop apps • Extensions available in C/C++	• Easy to learn • Extremely versatile • Huge library and community support • Strong presence in data science and AI	• Slower than compiled languages • Can become inefficient for highly performance-critical tasks
Microsoft Power BI	• Business intelligence and data visualization platform • Integrates with Azure and other Microsoft products • Creates interactive dashboards and complex data models • Designed for non-technical users	• Very user-friendly • Strong Microsoft ecosystem integrations • Good for collaboration across business teams	• Less flexible than full programming languages • Advanced features may require Pro or Premium licensing
TensorFlow	• Open-source machine learning framework • Optimized for deep neural networks • Uses computational graphs and supports eager execution • Python-based with Keras as high-level API • Includes TFX for ML pipelines and LiteRT for mobile/IoT • Runs on CPUs, GPUs, and TPUs	• Highly scalable • Robust ecosystem for production ML • Strong community and enterprise backing	• Complex learning curve • Overkill for small or simple ML projects
SQL	• Language for managing relational databases • Used to query, clean, and manipulate structured data • Compatible with many languages and industries	• Essential for data work • Efficient for handling structured datasets • Stable and widely supported	• Limited for unstructured data • Complex queries can become difficult to maintain
R	• Open-source statistical programming language • Built for data analysis, statistical modeling, and visualization • Thousands of packages (e.g., ggplot2) • Supported by The R Foundation	• Excellent statistical and visualization capabilities • Strong academic and research adoption • Ideal for complex statistical workflows	• Slower than some languages • Less general-purpose than Python
Jupyter Notebooks	• Web-based interactive computational environment • Mixes code, text, visuals, and results in one document • Supports many languages via kernels • Includes JupyterLab and Notebook Viewer	• Great for collaboration and experimentation • Clear documentation of computational workflows • Easy sharing and versioning	• Not ideal for large-scale software engineering • Can produce messy, nonlinear workflows
KNIME	• Open-source, node-based data analytics platform • Code-optional workflows • Native support for data cleaning, transformation, automation • Integrates R and Python when needed	• Very accessible for non-programmers • Fast workflow creation • Strong built-in capabilities	• Less flexible than writing code • Complex workflows can become visually cumbersome
GitHub	• Cloud platform built on Git version control • Central repository management • Tools like pull requests, user management, automation	• Essential for collaborative coding • Tracks changes across teams • Robust ecosystem and integrations	• Requires learning Git fundamentals • Merge conflicts can be challenging for beginners
SAS	• Comprehensive statistical and analytics suite • Used for BI, visualization, data prep, ML, risk modeling • SAS Viya: modern, cloud-native version	• Enterprise-grade stability • Strong support and documentation • Trusted in industries like finance, pharma, government	• Expensive licensing • Smaller community than open-source tools
Tableau	• Drag-and-drop visualization and analytics tools • Connects to many data sources (databases, clouds, R, Python) • Supports interactive dashboards and collaboration	• Very intuitive UI • Powerful visual storytelling • Easy sharing across teams	• Limited for advanced statistical modeling • Can become expensive at scale
Apache Spark	• Distributed data processing engine • Handles massive datasets (petabyte scale) • Fast batch and stream processing • Includes MLlib and multi-language support	• Extremely fast for big data • Highly scalable across clusters • Suitable for streaming and real-time needs	• Requires cluster infrastructure • Steeper learning curve for beginners
Microsoft Excel	• Spreadsheet tool for organizing and analyzing data • Supports formulas, pivot tables, visualizations • Part of Microsoft Office suite	• Ubiquitous and easy to use • Great for quick analysis • Strong visualization and automation features	• Not suitable for large datasets • Limited reproducibility for complex workflows

Types of Data Science Tools

Data science tools are categorized into major groups with distinct functions and commonalities.

• Data Acquisition and Storage Tools – These data science tools are primarily focused on data collection, storage, and management, including databases (MySQL, PostgreSQL, MongoDB), data warehouses (Redshift, Snowflake), and data lakes (Hadoop, S3). Pandas, NumPy, and OpenRefine are data science tools used to clean, process, and prepare data for study.
• Data Exploration and Visualization Tools – Tools like Power BI, Tableau, Matplotlib, and Seaborn help you understand and communicate data using visual representations.
• Machine Learning and Modeling Tools – Scikit-learn, TensorFlow, PyTorch, and Keras are data science tools that provide methods and frameworks for developing and training machine learning models.
• Model Deployment and Management Tools – Data science tools like MLflow and Kubeflow assist with the deployment and management of machine learning models in production environments.
• Big Data Tools – Apache Spark and Apache Flink are data science tools built to handle large datasets.
• Natural Language Processing (NLP) Tools – NLTK, spaCy, and Gensim are three data science tools used for tasks requiring human language, such as text classification, sentiment analysis, and machine translation.
• Data Quality Tools – This category includes tooling for implementing CI/CD processes for data or solutions that focus on data version control, like lakeFS.

Role of Data Science Tools

Data science tools help data scientists and practitioners extract, process, analyze, and visualize data more efficiently, improving business capabilities. This is why such tools present a strategic need for modern organizations. 

Here are some good reasons why data science tools play such an important role across every business, no matter the industry:

Business Need	Role of Data Science Tool
Educated Decision-Making	Data science tools help teams make educated decisions by delivering insights based on data analysis. This improves strategic planning and resource allocation
Predictive Analytics	Businesses can use machine learning models to forecast future trends, customer behavior, and market dynamics, allowing for more proactive decision-making
Problem-Solving	Data science tools help extract useful insights by combining computer science, statistics, and predictive analytics. This is critical for addressing real-world business challenges
Workflow Efficiency & Automation	Such tools also automate repetitive procedures, allowing data scientists to concentrate on more valuable activities. Workflows are accelerated by smoothly integrating data from several sources and employing numerous algorithms and strategies. This simplifies the process of collecting and analyzing company information
Faster Decision Making	Data science tools can help businesses make faster decisions. These solutions’ real-time data monitoring and enhanced flexibility help to make faster and more informed decisions
Competitive Advantage	Organisations using data science technologies can respond faster to market developments, enhance operations, and provide personalized experiences
Customer Understanding	Data science technologies help firms better understand their customers. This knowledge aids in personalizing products and services to fit client needs, thus increasing customer happiness and loyalty

How to Choose the Right Data Science Tool

Picking the right solution for your project and team is no small feat. Here are the key aspects to consider when making your tooling choice:

1. Define the problem – Are you looking to explore data, build models, visualize insights, or implement data pipelines? Your use case will determine the tooling, not the other way around.
2. Match the tool to your technical skill level – Programming languages like Python and R provide flexibility, whereas visual platforms such as Power BI, KNIME, and Tableau help non-coders do tasks faster.
3. Consider ecosystem compatibility – Check how effectively the solution interfaces with your current stack (cloud provider, databases, languages, and business intelligence tools).
4. Don’t forget about scalability and performance requirements – Lightweight tools work well for small datasets, but distributed engines like Spark or TensorFlow come in handy for large-scale or real-time applications.
5. Evaluate community size and support – Robust ecosystems bring you more tutorials, plugins, technical help, and long-term stability.
6. Consider collaborative workflows – Platforms such as GitHub, Jupyter, and Tableau are great for sharing insights, code, and dashboards across teams.
7. Prioritize pricing and license models – Compare open-source flexibility to enterprise-grade functionality in commercial products such as SAS or Power BI Premium.
8. Consider deployment requirements – If the goal is to run production machine learning, technologies with pipeline and model-serving capabilities (TensorFlow, TFX, Spark) are key.
9. Plan for long-term maintainability – Use tools that make it simple to document work, version outcomes, and replicate tests.

Challenges in Using Data Science Tools

Data Security and Compliance Concerns

New privacy and regulatory issues have made it more challenging for data scientists to access critical data, particularly personal and sensitive information:

• Companies that provide third parties access to their datasets will face the extra difficulty of maintaining ongoing security and compliance with data protection rules such as GDPR. Neglecting either aspect may result in significant financial fines and costly, time-consuming audits by regulatory agencies.
• Organizations should establish robust data governance rules and practices to address data security concerns, including data access controls, encryption, and data anonymization techniques. 
• Data catalogs can also help administrators manage data access by allowing them to grant or restrict access to specific data sets depending on user roles and permissions. This ensures that data scientists can access only the data they need, while maintaining data security and compliance.

Cost and Licensing Challenges

However, proprietary data science tools frequently require license fees, which can be a substantial cost for enterprises with limited budgets. Using proprietary tools may result in vendor lock-in, making it more difficult to switch to alternative solutions in the future. Such tools may also be less flexible and customizable than open-source alternatives.

How lakeFS Works with Data Science Tools

lakeFS is an open-source data version control system that converts object storage into a Git-like repository. As a result, it lets data science teams manage and version control their data like they manage code. 

lakeFS brings data versioning capabilities that help to simplify and optimize any data science project with the following use cases:

Use Case	Application
Data Preparation In Isolation	Create isolated branches of your data to explore without damaging your primary dataset. This ensures data quality while increasing productivity in your data preparation procedures
Parallel Machine Learning Experimentation	Run multiple tests on different branches, compare experiment results, reduce data duplication, and improve resource management. Once you’ve identified the top-performing ML models, seamlessly integrate them into the main branch
Machine Learning Data Reproducibility	Maintain a complete history of your data revisions with lakeFS data version control, which is synced with your code version control. Revert to prior versions if necessary, and check that each experiment is reproducible
Fast Data Loading for Deep Learning Tasks	Enhance data load times and streamline large-scale data processing. Create branches that don’t duplicate data, use efficient reads and rapid data access, and use caching to accelerate data retrieval

Conclusion

Data science is poised to make substantial advances in artificial intelligence, machine learning, and quantum computing in the years to come. These technologies will transform how data is handled and utilized, making it crucial for future data scientists to stay ahead of the competition. 

This will compel teams to prioritize the quality of their data as soon as possible. Solid data quality management practices and tooling will be invaluable when handling massive datasets of both structured and unstructured data.