data-analyst

Name: data-analyst
Availability: InStock
Author: Nirbhay

Analyze, impute, and visualize CSV datasets with Plotly Dash dashboards.

19|5|Updated Nov 23, 2025

Search Tags:#dashboard #data analysis #visualization #csv #missing values #imputation

Ready to run

npx skills add https://github.com/Nir-Bhay/markups --skill data-analyst-nir-bhay

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/Nir-Bhay/markups/tree/main/.agents/skills/data-analyst
Command: npx skills add https://github.com/Nir-Bhay/markups --skill data-analyst-nir-bhay

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scikit-learn, plotly, dash, dash-bootstrap-components, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the entire data analysis process, from identifying and handling missing values to generating interactive visualizations, making complex data exploration accessible.

Core Features & Use Cases

Missing Value Analysis: Automatically detects and reports on missing data patterns.
Intelligent Imputation: Fills missing values using methods like mean, median, mode, or KNN.
Interactive Dashboard Creation: Generates Plotly Dash dashboards for visualizing trends.
Use Case: Upload a messy CSV file, and this Skill will clean it, impute missing data intelligently, and provide an interactive dashboard to explore relationships and trends.

Quick Start

Use the data-analyst skill to analyze the missing values in the file 'sales_data.csv'.

Frequently Asked Questions about data-analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze and handle missing values in a CSV dataset?▼

To handle missing values in a CSV dataset, this Skill automatically detects data patterns and fills gaps using intelligent imputation methods like mean, median, mode, or KNN imputation.

How do I create an interactive dashboard for exploratory data analysis?▼

You can create an interactive dashboard for exploratory data analysis by using this Skill to generate Plotly Dash visualizations that map out relationships and trends directly from your cleaned dataset.

What is the best way to clean a messy CSV file for statistical analysis?▼

The best way to clean a messy CSV file for statistical analysis is to automate data quality assessment and impute missing values, ensuring your dataset is complete and ready for exploration.

Does this data analysis workflow support KNN imputation for missing data?▼

Yes, this data analysis workflow supports KNN imputation for missing data, alongside mean, median, and mode methods, to intelligently fill gaps based on dataset patterns.

Can I use pandas and scikit-learn to automate data visualization?▼

Yes, this Skill uses pandas and scikit-learn for data processing and intelligent imputation, then leverages Plotly and dash-bootstrap-components to automate the creation of interactive data visualizations.

When should I use KNN imputation instead of mean or median for missing values?▼

You should use KNN imputation instead of mean or median when your dataset contains complex missing value patterns that require intelligent, similarity-based filling to maintain statistical integrity.