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unit 1/01_Introduction_to_Data_Mining.md

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+# Introduction to Data Mining
+
+## What is Data Mining?
+**Data Mining** is the process of digging through large amounts of raw data to find useful patterns, trends, and knowledge.
+- **Analogy**: Like mining gold from rocks. The rocks are the "raw data," and the gold is the "knowledge."
+
+### Key Definitions
+- **Data**: Raw facts and figures (e.g., sales logs, sensor readings).
+- **Mining**: Extracting something valuable.
+
+## The DIKW Pyramid
+The **DIKW** model shows how we move from raw data to wisdom.
+
+1. **Data (D)**: Raw, unprocessed facts.
+   - *Example*: Numbers like 42, 35, 50.
+2. **Information (I)**: Data that is organized and has meaning.
+   - *Example*: "These are the ages of employees."
+3. **Knowledge (K)**: Understanding gained from analysis.
+   - *Example*: "The team has a mix of young and experienced people."
+4. **Wisdom (W)**: Applying knowledge to make good decisions.
+   - *Example*: "Let's create a mentorship program to share skills."
+
+## Major Issues in Data Mining
+1. **Privacy and Security**: Mining can reveal sensitive personal info. We must protect it.
+2. **Scalability**: Can the system handle huge amounts of data (Big Data)?
+3. **Data Quality**: If data is dirty or missing, the results will be wrong ("Garbage In, Garbage Out").
+4. **Ethical Use**: Ensuring data isn't used for discrimination or bias.