📚 Learning Objectives
- Master data collection and cleaning
- Perform exploratory data analysis
- Understand statistical methods
- Build predictive models
- Create data visualizations
- Deploy analytics solutions
📹 Educational Videos
Data Science Basics
Python for Data Analysis
Data Visualization
Machine Learning Intro
Statistical Analysis
📊 Data Science Tools Comparison
| Tool | Use Case | Difficulty | Best For |
|---|---|---|---|
| Python + Pandas | Data manipulation | Medium | Beginners |
| R | Statistical analysis | Medium | Statisticians |
| Tableau | Visualization | Easy | Dashboards |
| SQL | Database queries | Easy | Data retrieval |
| Power BI | Business analytics | Medium | Enterprise |
🛠️ Implementation Steps
- Setup Environment: Install Python, Jupyter, libraries
- Load Data: Import dataset from CSV or database
- Clean Data: Handle missing values and outliers
- Explore: Generate summary statistics
- Visualize: Create charts and graphs
- Model: Build and train ML model
🎓 Knowledge Check - Module 5
Q1: What is the first step in data science?
Q2: Best Python library for data manipulation?
Q3: Which is used for creating dashboards?
🏆 Capstone: Build Analytics Dashboard
Phase 1: Collect sample dataset
Phase 2: Clean data in Python
Phase 3: Perform statistical analysis
Phase 4: Create 5 visualizations
Phase 5: Build predictive model
Phase 6: Create interactive dashboard
Phase 7: Document findings
Phase 8: Present insights to stakeholders