Hey everyone! I’m really interested to hear about how people are setting up their data science workflows nowadays. Things seem to be changing so fast with all the new AI tools coming out.
I’d love to know what development environments you’re using. Are you sticking with traditional setups, or have you switched to something new? What about notebooks versus regular IDEs?
Also curious about AI coding assistants. Has anyone tried the newer ones? I’m wondering which ones actually help with data analysis tasks and which ones are just hype.
How do you actually use these tools in your daily work? Do you have specific ways you ask them for help? What kind of tasks do they handle well and where do they fall short?
Would really appreciate hearing about your experiences, both good and bad. Any recommendations or things to avoid?
My setup blends tools effectively. I primarily operate from a Linux workstation utilizing Docker to manage different projects seamlessly. For development, I alternate between PyCharm for larger applications and JupyterLab for quick data analysis and exploratory tasks.
I’ve found Claude to be quite helpful, especially for clarifying complex statistical concepts and assisting with pandas debugging. It’s also beneficial for optimizing SQL queries, although I advise caution; always verify the math outputs it provides.
Lately, I leverage AI more for code reviews rather than initial coding. I provide functions and seek feedback on potential issues or improvements, which has led to identifying several edge cases and enhancing my coding practices. Clarity in prompts is key, as it sets the foundation for more useful responses.
Been running a pretty stable setup for the last couple years that works well for our team.
We standardized on conda environments with jupyter lab for exploration and VSCode for production code. Keeps things consistent across different machines and makes onboarding new people way easier.
For AI assistants, I actually use a mix depending on the task. GitHub Copilot handles the routine pandas operations and data cleaning scripts pretty well. But for anything involving statistical modeling or complex analysis, I switch to ChatGPT or Claude because they give better explanations of the approach.
One thing I learned the hard way - don’t let AI write your entire analysis pipeline from scratch. Use it more like a rubber duck that talks back. I’ll paste a function and ask “what edge cases am I missing here” or “is there a more efficient way to do this transformation”.
The biggest win has been using AI to generate unit tests for data validation. Give it a function that processes messy real world data and it comes up with test cases you wouldn’t think of. Saved us from several bad deployments.
Avoid using AI for anything involving domain specific business logic though. It loves to make assumptions that sound reasonable but are completely wrong for your use case.
DataSpider + R Studio + Python has been a solid combo for environment management. Everyone sleeps on R, but it crushes statistical modeling while Python libraries make you want to pull your hair out debugging.
I skip the popular AI tools and stick with Tabnine. It actually learns from YOUR code instead of random Stack Overflow snippets, so you get suggestions that match your data structures and naming style. Gets smarter as you use it.
Here’s my workflow: I write the analysis logic myself, then get AI to document what each section does in normal English. Creates way better handoff docs for stakeholders and helps me spot logic bugs.
Big warning though - AI tools are terrible with missing data. They’ll throw standard imputation methods at you without thinking about your business context or how the data was collected. Always double-check their missing data suggestions against what you know about the domain.
i mostly use vscode wit Jupyter extensions for my projects. tried cloud tools but latency is a pain with big datasets. GitHub copilot’s cool for boilerplate stuff, but id double-check its ML tips - sometimes it gives odd pandas syntax.
I recently ditched my traditional Anaconda setup for Databricks Community Edition. Even working solo, the collaborative features are surprisingly handy, and MLflow integration has made experiment tracking way easier. As for AI assistants - results are all over the place. Cursor’s been surprisingly good for data science work, better than the popular alternatives. It actually gets context when I’m working with dataframes and stats operations. But here’s the thing - every AI tool I’ve tried falls flat on domain-specific feature engineering decisions. What works well is using AI for code translation between libraries. Moving from scikit-learn to XGBoost or converting matplotlib plots to plotly? AI nails the syntax conversion while I handle parameter tuning and validation. Time series analysis is where AI really struggles though. It keeps suggesting cookie-cutter approaches that completely miss domain stuff like seasonality patterns or data leakage. For that work, I’m still doing manual implementation and sticking with traditional docs.
I’ve been trying something completely different lately. Built an automated pipeline that handles most of my data science work instead of juggling all these tools manually.
The real game changer? Automated data ingestion, preprocessing, and model deployment. New data comes in, everything kicks off automatically - cleaning, feature engineering, training, report generation.
I integrated multiple AI APIs into the workflow too. My system automatically sends code for review, generates docs, and creates visualizations from analysis results. No more manual prompting.
I can trigger different workflows based on data patterns or schedule them for specific times. Done with manual notebook runs and forgotten dashboard updates.
This saves me 15-20 hours weekly. Way less time on repetitive stuff, more on actual analysis and strategy.
Latenode’s been perfect for building these automated data science workflows. Connects all your tools and APIs without writing tons of integration code.