I Audited My Workflow: These Are The 7 AI Tools I Actually Use Daily As A Data Scientist

You probably have 50 browser tabs open right now. Half of them are new AI tools promising to “revolutionize” your workflow. The other half are StackOverflow threads from 2018.

I get it. The noise is deafening.

I didn’t want to write another generic “Top 10 AI Tools” list. Instead, I audited my actual browser history and terminal logs from the last month. I cut the fluff and kept only the tools that actually handle the grunt work, fix my broken Python scripts, and help me explain complex models to stakeholders.

Here is the honest breakdown of the stack that keeps me sane.

⚡ Quick Look: My “Daily Drivers”

• Best for Coding & Refactoring: Cursor (Editor)
• Best for “Dumb” Questions & Research: Perplexity AI
• Best for Quick EDA & Visualization: ChatGPT Team (Advanced Data Analysis)
• Best for Private/Sensitive Data: Ollama (Local LLMs)
• Best for Reading Academic Papers: SciSpace
• The “Secret Weapon”: Claude 3.5 Sonnet (for Logic)
• My One Crucial Tip: Never paste customer PII (Personally Identifiable Information) into any cloud tool. Use Ollama for that.

1. The Code Editor: Cursor (VS Code on Steroids)

I resisted switching from VS Code for a long time. I was wrong. Cursor is essentially a fork of VS Code, so all your extensions work, but AI is baked into the core, not glued on as a plugin.

Why I can’t live without it:
It predicts my next logic step, not just the syntax. If I’m writing a plotting function in Matplotlib, I can hit Cmd+K and type “Change this to a sub-plot layout and make the color scheme colorblind friendly,” and it just does it.

My workflow:
I use the “Codebase Context” feature. You can chat with your entire repository. I’ll ask, “Where in data_loader.py are we handling the missing timestamp values?” and it points me right there.

2. The Research Assistant: Perplexity AI

Google search has become a mess of SEO-spam. When I forget the syntax for a specific PyTorch tensor manipulation, I don’t Google it. I ask Perplexity.

Why it wins:
It cites sources. As a Data Scientist, hallucination is my enemy. Perplexity gives me the code snippet and links directly to the documentation or the GitHub discussion where the solution was found.

Pro Tip:
Use the “Focus” mode and select “Writing” or “Academic” if you are researching algorithms, or “Wolfram|Alpha” if you need to double-check a mathematical concept.

3. The “Junior Analyst”: ChatGPT (Advanced Data Analysis)

I don’t use ChatGPT to write production code. I use it to prototype. Specifically, the Advanced Data Analysis (formerly Code Interpreter) feature is a lifesaver for the boring initial phase of Explanatory Data Analysis (EDA).

How I use it:
I upload a sanitized CSV (no PII!) and prompt: “Run a basic EDA. Check for nulls, distributions of numerical columns, and give me 5 interesting correlations.”

It writes and executes the Python code in a sandbox. It’s like having a junior analyst sitting next to me doing the grunt work so I can focus on the modeling.

4. The Logic Engine: Claude 3.5 Sonnet

Here is a controversial opinion: Claude 3.5 Sonnet is currently better at coding logic than GPT-4o.

When I have a complex system design problem—like figuring out how to architect a data pipeline or refactor a messy class hierarchy—I go to Claude. It tends to be less “chatty” and more precise with architectural decisions. It holds context incredibly well for long conversations.

The “Artifacts” Feature:
Claude’s “Artifacts” allow it to render React components or diagrams in a side window. I use this to quickly visualize how a dashboard might look before I ask the frontend team to build it.

5. The Privacy Shield: Ollama (Local LLMs)

This is the most important tool on this list for professionals. You cannot send sensitive company data to OpenAI or Anthropic. Period.

Ollama allows me to run powerful models (like Llama 3 or Mistral) locally on my MacBook.

My Setup:

1. Download Ollama.
2. Run ollama run llama3 in the terminal.
3. I use this to summarize confidential meeting notes, clean sensitive datasets using Regex, or debug proprietary code. The data never leaves my machine.

6. The Academic Accelerator: SciSpace (Typeset.io)

Data Science moves fast. New papers drop on ArXiv every day. SciSpace is a dedicated AI for reading research papers.

Why it’s better than a standard PDF reader:
I can highlight a confusing math formula in a paper, and the AI explains it to me in plain English. I can also ask, “What are the limitations of this methodology?” and it scans the whole paper to find the answer. It saves me hours of reading irrelevant papers.

7. The Storyteller: Hex

Hex is a modern notebook interface (like Jupyter) but with AI built-in. It bridges the gap between “code” and “stakeholder presentation.”

The Killer Feature:
I write my SQL and Python to pull data. Then, I use the “Magic” button to say, “Explain this chart to a non-technical marketing manager.” It generates a text description that I can put right next to the chart. It turns my analysis into a report automatically.

So, what’s the bottom line?

If you are just starting, don’t try to master all seven. Start with Cursor for your code and Perplexity for your search. Those two changes alone recovered about 30% of my work week.

Remember: These tools are force multipliers, not replacements. They make a good Data Scientist great, but they won’t turn bad logic into good science.

Which tool is saving your sanity right now? Drop a comment below—I’m always looking to test something new.