Data Analysis

Data Analysis

When to use this skill

• Data exploration: Understand a new dataset
• Report generation: Derive data-driven insights
• Quality validation: Check data consistency
• Decision support: Make data-driven recommendations

Instructions

Step 1: Load and explore data

Python (Pandas):

import pandas as pd
import numpy as np

# Load CSV
df = pd.read_csv('data.csv')

# Basic info
print(df.info())
print(df.describe())
print(df.head(10))

# Check missing values
print(df.isnull().sum())

# Data types
print(df.dtypes)

SQL:

-- Inspect table schema
DESCRIBE table_name;

-- Sample data