Best AI Tools for Data Scientists: 11 Tools to Try

AI won't replace the data scientist who knows which question to ask. It will, though, take the boilerplate code, the first chart and the tedious data cleaning off your plate.

The eleven tools below are listed alphabetically and in no particular order, and every description comes from the tool's own website, read this week. For quick wins, my collection of AI prompts for SQL works with most of these tools.

AI tools for data scientists compared

In the free plan column, Yes means the tool's own site names a free plan, a free version, a free account, a free allowance or open source software. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to start for free.

Tool	Type	Good for	Free plan
Databricks Genie Code	AI agent for data teams on Databricks	Teams already on Databricks	Not stated
Dataiku	Platform for AI and analytics projects	Enterprise data science teams	Not stated
DataRobot	Agent workforce platform for enterprise	Deploying and monitoring models	Not stated
Deepnote	Collaborative analytics and data science notebook	Shared notebooks with AI help	Not stated
GitHub Copilot	AI coding agent from GitHub	Writing Python and SQL faster	Not stated
Google Colab	Hosted notebooks from Google	Quick experiments in the browser	Not stated
H2O.ai	Sovereign AI platform	Private, on-premise AI	Not stated
Hex	Analytics platform for humans and agents	Notebooks that become apps	Not stated
Jupyter AI	Open source AI extension for Jupyter	AI inside JupyterLab	Yes
PandasAI	AI dashboard for data analysis with an open source library	Asking questions of dataframes	Yes
Positron	Free data science IDE for Python and R	Python and R in one IDE	Yes

Eleven AI tools for data scientists

Databricks Genie Code

Best for: Teams working in Databricks who want an AI agent that writes code and queries inside their workspace.

Dataiku

Best for: Large organisations that want data science, analytics and AI projects governed in one shared platform.

DataRobot

Best for: Enterprise teams that want to build, deploy and observe AI models and agents at scale.

Deepnote

Best for: Analysts who want collaborative notebooks with AI assistance that colleagues can read, comment on and reuse.

GitHub Copilot

Best for: Anyone who writes a lot of Python and SQL and wants an AI coding agent in their editor.

Google Colab

Best for: Students and researchers who want hosted notebooks in the browser for quick experiments with no local setup.

H2O.ai

Best for: Regulated organisations that want AI and machine learning they can run in their own environment.

Hex

Best for: Analytics teams that want notebooks, AI agents and shareable data apps in one workspace.

Jupyter AI

Best for: Data scientists who live in JupyterLab and want an open source extension connecting AI agents to notebooks.

PandasAI

Best for: Python users who want to ask plain-language questions of their data, starting from the open source library.

Positron

Best for: Analysts who switch between Python and R and want a free IDE built for data science.

A framework for trustworthy AI work

If your team builds or uses AI, NIST's AI Risk Management Framework is worth knowing. NIST says it's intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use and evaluation of AI products, services and systems.

You don't need to adopt the whole thing, but its ideas are a useful checklist: know where your data came from, test models before you trust them and keep a record of decisions. AI assistants make work faster, and the checks are still yours.

Assistant in your editor or full platform?

AI assistants live where you already work, in notebooks and code editors, and help you write Python, SQL and R faster. Full platforms add data pipelines, model training, deployment and governance, with AI agents spread across the workflow.

Individual data scientists usually get the most from an assistant in the environment they already use. Larger teams with models in production often need a platform that everyone shares.

Using AI without losing rigour

Treat AI-written code like a junior colleague's first draft of code: read it, test it and question it. Generated code often runs perfectly and still does the wrong thing quietly.

Keep sensitive data out of tools that aren't approved by your organisation, and check where prompts and data are stored. My look at remote and office data science roles covers the working side of the job too.

Questions to ask before you adopt an AI data tool

1. Does it work in the notebooks and editors I already use?
2. Where are my data and prompts stored?
3. Can I review and test what it generates?
4. Does it support Python, R and SQL?
5. Can my team share and reuse its work?
6. Is it approved for the data I handle?

Signs your data team should adopt AI tools

• Boilerplate eats your day. Let AI draft it.
• Charts take hours. Generate a first version.
• Code reviews pile up. Use AI to explain changes.
• Nobody checks AI output. Add a review step.

Do you make an AI tool for data science? Tool makers can ask to be considered for a featured place on this list.

My plain advice on choosing AI tools as a data scientist

Start with an assistant in the place you already work and measure what it saves you over a few weeks. If it saves an hour a day on boilerplate, it's earned its keep.

And never let speed replace checking. The value you bring is judgement about data and results, and that's exactly what the tools can't do for you.

Most of this work starts in an editor, and our free Python IDE guide compares free ones.

Frequently asked questions

What are the best AI tools for data scientists?

It depends on whether you want an assistant in your editor or a full platform. The list above is in no particular order.

Are there free AI tools for data science?

Some tools are free or open source. The free plan column shows what each tool's own site says.

What is the NIST AI Risk Management Framework?

NIST says it's a voluntary framework to help build trustworthiness into the design, development, use and evaluation of AI systems.

Can AI write my Python and SQL?

It can draft code quickly, but you should always read, test and question what it produces.

Is it safe to put company data into AI tools?

Only into tools approved by your organisation, and check where data and prompts are stored.

Will AI replace data scientists?

It takes over routine tasks, but judgement about questions, data and results stays with people.