7 AI Tools I Can’t Live Without as a Professional Data Scientist – KDnuggets

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# Introduction

 
I have been immersed in artificial intelligence (AI) tools, not just writing about them but using them every day in my work as a data scientist. They have completely changed how I get things done, helping me write cleaner code, improve my writing, speed up data analysis, and deliver projects much faster.

In this article, I share the seven AI tools that have become permanent parts of my workflow. No replacements, no substitutes — just the essentials that power everything from machine learning projects to content automation.

# 1. Grammarly AI

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Grammarly is a tool I have been using for almost a decade. It started as a simple grammar and spell-check assistant for my assignments and thesis, but it has evolved into a full AI-powered writing companion.

Now, I can highlight any text and ask Grammarly to improve it, rewrite it, adjust the tone, or make it clearer, and it consistently delivers high-quality results.

After running my content through Grammarly, everything feels sharper, more polished, and ready to publish. I use it for my LinkedIn posts, articles, tutorials, project documentation, and emails. It’s one of the few tools I truly can’t live without.

# 2. You.com

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I have been using You.com for two years, and honestly, even with the recent subscription price increase, it is still worth every penny.

I rely on it for research, planning, and learning new topics. Its deep-research mode is one of the best features; it explores subjects thoroughly and gives detailed reports that I haven’t seen from ChatGPT or any other AI assistant.

One of the biggest advantages of You.com is access to top models from Anthropic, OpenAI, Google, and a range of open-source models, all in one place. You can test them, compare them, and even integrate them into your workflow. On top of that, You.com offers a free Model Context Protocol (MCP) server, which makes it incredibly easy to connect your local AI tools and pull web results in milliseconds.

For research-heavy work or exploring new ideas, You.com is easily one of my most reliable tools.

# 3. Cursor

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I have been a fan of Cursor long before it became popular. It is lightweight, intuitive, and one of the first editors to offer native support for agentic AI workflows.

Today, I use Cursor with Claude Code and several key extensions to test, debug, and ship code much faster, and I am loving every bit of it.

I use Cursor for machine learning model training, web development, API building, data analysis, and even assembling full projects from scratch. Features like inline AI suggestions, multi-file reasoning, instant refactoring, and context-aware planning make it feel like a true AI pair-programmer.

# 4. Deepnote

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Deepnote is my go-to tool for fast prototyping and testing code. I have been using it for five years, and it has grown into a fully capable data science platform. It is a cloud-based notebook powered by AI, which means you can simply ask it to analyze your data and it will generate code step-by-step, run it, fix errors, and produce a clean, structured notebook report for you.

It comes with smart autocomplete, debugging support, and fast environment loading, which makes experimentation effortless. I use it for my tutorials, demos, and quick experiments, and it has drastically reduced my time to build and test ideas.

I have become so used to the Deepnote workflow that I rarely touch local notebooks anymore. Everything stays online, organized, and synced. For the kind of work I do, nothing beats it.

# 5. Claude Code

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Honestly, I was skeptical about Claude Code at first. It felt too expensive, and it did not perform well on some of my early data science tests. But everything changed when I discovered I could integrate the GLM Coding Plan with it. Since then, I have been using Claude Code every single day for both personal projects and work.

Using it feels seamless. I have tried Open Code, Gemini, Codex, and even Droid, but I keep coming back to Claude Code.

Its simplicity, the way it follows instructions, and its ability to handle complex tasks automatically make it incredibly reliable. For fast development, clean reasoning, and handling multi-step coding workflows, nothing else comes close.

# 6. ChatGPT

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Where do I even start with ChatGPT? It has been part of my daily life since the day it launched. I use it for everything — coding, research, debugging issues, troubleshooting my system, writing, and streamlining my workflow.

Whenever I am stuck on a complex problem, ChatGPT is the first place I turn for a fast, reliable answer. I ask it personal questions, work-related questions, and anything in between, and it consistently gives useful, context-aware responses thanks to its ability to remember past conversations.

What makes ChatGPT so powerful for me is the combination of conversational memory, flexible inputs, and custom instructions. It adapts to how I work, understands my patterns, and can switch effortlessly between tasks.

Whether I am generating code, reviewing notebooks, drafting content, or analyzing data, it’s the closest thing to having a full-time AI partner sitting next to me for my workflow.

# 7. llama.cpp

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llama.cpp is the backbone of the local AI ecosystem. It is fully open-source and lets you run large language models locally on regular consumer hardware, even without a GPU. It is lightweight, fast, and incredibly efficient, delivering true bare-metal performance. Recently, the developers even added a clean UI, which makes it feel almost like a local replacement for ChatGPT.

I use llama.cpp for offline projects and any work that involves sensitive code or private data. It integrates easily with local coding agents, chatbots, and custom tools, and the setup has become so simple that even Windows users can install it without hassle. Whenever I want to test new open-source models, I run them directly on my laptop through llama.cpp and share my experience. I also use it for code generation, writing, and quick question-answering.

It is not at ChatGPT’s level, but if you care about privacy, security, and experimenting with new models for free, llama.cpp is the tool you want in your stack.

# Final Thoughts

 
My core tools stay the same: Grammarly, You.com, Cursor, and ChatGPT. The rest change depending on my workflow or when better alternatives show up.

As someone with dyslexia, having AI support at my fingertips has been a real advantage. These tools help me understand complex text, review my writing, and even handle research that would normally take me hours to get through. Discovering Grammarly, ChatGPT, and Cursor turned what felt like a challenge into one of my strengths.

I don’t believe AI is here to replace us. It is here to support us and shape a new generation of workflows where AI becomes a natural part of how we build, write, learn, and create. When used well, it doesn’t take away your capabilities; it amplifies them.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.