What is Data Analysis? #
Data Analysis is the process of examining, transforming, and interpreting data to discover useful patterns, trends, and insights.
Importance of Data Analysis #
- Helps in understanding data
- Supports better decision-making
- Identifies patterns and trends
- Converts raw data into meaningful insights
Types of Data Analysis #
Descriptive Analysis: What happened? (summary, averages)
Diagnostic Analysis: Why did it happen? (finding causes)
Predictive Analysis: What will happen? (forecasting)
Prescriptive Analysis: What should be done? (recommendations)
Steps in Data Analysis #
- Collect data
- Clean data
- Explore data (EDA)
- Analyze patterns
- Visualize results
Basic Python Example #
Step 1: Load Data #
import pandas as pd
data = pd.read_csv("data.csv")
print(data.head())Step 2: Basic Analysis
# Summary statistics
print(data.describe())
# Average value
print("Average Sales:", data["sales"].mean())Step 3: Group Analysis
# Group by category
grouped = data.groupby("product")["sales"].sum()
print(grouped)Step 4: Simple Visualization
import matplotlib.pyplot as plt data["sales"].plot() plt.show()
