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Data Analysis | Skills Pool

技能檔案

Data Analysis

Analyze data, create visualizations, and generate insights. Use when working with data manipulation, statistical analysis, data visualization, or data reporting.

hallucinaut0 星標2026年2月8日

職業
分類: 數據分析

技能內容

Data Analysis Skill

Analyze data, create visualizations, and generate actionable insights.

When to Use

Use this skill when the user wants to:

Analyze data from datasets
Create data visualizations
Perform statistical analysis
Clean and transform data
Generate data reports
Find patterns and trends

Data Analysis Workflow

1. Data Understanding

Examine data structure and content
Identify data types and formats
Understand relationships between variables

2. Data Cleaning

Handle missing values
Remove duplicates
Standardize formats
Fix data quality issues

3. Data Exploration

相關技能

快速安裝

Data Analysis

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作者: hallucinaut
星標: 0
更新時間: 2026年2月8日
職業

本頁內容

01Data Analysis Skill 02

Calculate summary statistics
Find correlations
Detect outliers
Identify patterns

4. Data Analysis

Perform statistical tests
Group and aggregate data
Create derived metrics
Test hypotheses

5. Visualization

Create charts and graphs
Build interactive dashboards
Design meaningful visualizations
Present findings

Tools & Libraries

Python

Pandas: Data manipulation
NumPy: Numerical computing
Matplotlib: Static plotting
Seaborn: Statistical visualization
Plotly: Interactive plots
Scikit-learn: ML preprocessing

JavaScript

D3.js: Data visualization
Chart.js: Simple charts
Plotly.js: Interactive plots
Apache ECharts: Powerful charts

R

ggplot2: Visualization
dplyr: Data manipulation
tidyr: Tidying data

Analysis Types

Descriptive Analysis

Summarize data with statistics
Create frequency distributions
Calculate central tendency

Exploratory Analysis

Identify patterns
Find correlations
Visualize distributions

Predictive Analysis

Forecast trends
Build predictive models
Estimate values

Diagnostic Analysis

Understand cause and effect
Root cause analysis
Anomaly detection

Visualization Guidelines

Clarity over aesthetics: Make data easy to understand
Tell a story: Each visualization should have a purpose
Avoid chart junk: Don't overcomplicate
Color appropriately: Use colors to highlight, not decorate
Add context: Labels, titles, and annotations

Deliverables

Analysis report with findings
Data visualizations
Data cleaning pipeline
Statistical analysis results
Insights and recommendations

Quality Checklist

Data is properly cleaned and validated
Analysis is statistically sound
Visualizations are clear and accurate
Storyline is logical and compelling
Code is reproducible and documented
Findings are actionable

When to Use

03Data Analysis Workflow

041. Data Understanding

052. Data Cleaning

063. Data Exploration

074. Data Analysis

085. Visualization

09Tools & Libraries

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