10 Weeks, 20 Lessons, Data Science for All!
100
Quality
100
Trust
75
Fit
$ npx skills add microsoft/Data-Science-For-BeginnersData agent skills
Compare skills for CSV analysis, spreadsheet profiling, charts, metrics, business analysis, notebooks, and structured-data workflows.
Built for users searching for agent skills that analyze CSVs, spreadsheets, datasets, charts, and metrics.
Matched
16
Stars
422K
Input
CSV
Output
Insights
Agent jobs
These pages are built for high-intent search and for agents that need a structured shortlist before installing third-party code.
01
Profile CSV files and explain suspicious columns
02
Generate charts and plain-language metric summaries
03
Validate spreadsheet outputs before sharing
04
Turn raw data into a reproducible analysis plan
Task routes
Ranked shortlist
10 Weeks, 20 Lessons, Data Science for All!
100
Quality
100
Trust
75
Fit
$ npx skills add microsoft/Data-Science-For-Beginners#02
Visualizer for pandas data structures
100
Quality
100
Trust
68
Fit
$ npx skills add man-group/dtale#03
PyGWalker: Turn your dataframe into an interactive UI for visual analysis
100
Quality
100
Trust
73
Fit
$ npx skills add Kanaries/pygwalker#04
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
100
Quality
100
Trust
76
Fit
$ npx skills add pandas-dev/pandas#05
Streamlit — A faster way to build and share data apps.
100
Quality
100
Trust
76
Fit
$ npx skills add streamlit/streamlit#06
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
100
Quality
100
Trust
76
Fit
$ npx skills add gradio-app/gradioEvaluation
Produces reproducible steps and code where possible
Flags missing data, outliers, and schema assumptions
Uses readable charts and concise conclusions
Keeps sensitive data handling explicit
Questions
Yes. Many data analysis skills support CSV or spreadsheet-style workflows, but sensitive business data should be handled with explicit approval.
A good skill should produce reproducible steps, data-quality notes, charts or tables when useful, and a concise explanation of the result.