Скачать или смотреть Day 6 Python Data Science Essentials - Comprehensive Analysis

What python does to Data Science.
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Chapters
00:00 Introduction
00:51 🧭 6.1. What You'll Build Today
01:49 🧠 6.2. What You Will Learn
03:06 📊 6.4. Step 1: Data Loading and Initial Exploration
03:16 📁 6.4.1. Reading Data from CSV Files
03:56 🔍 6.4.2. Initial Data Exploration
04:53 🧹 6.5. Step 2: Data Cleaning and Preparation
05:04 🔄 6.5.1. Handling Duplicate Records
05:35 🕳️ 6.5.2. Managing Missing Values
06:32 📅 6.5.3. Data Type Conversion
07:06 📈 6.6. Step 3: Data Aggregation
07:15 🔢 6.6.1. Creating New Features or Aggregates
07:48 🧮 6.6.2. NumPy Array Operations
09:01 📊 6.7. Step 4: Data Visualization Mastery
09:10 📊 6.7.1. Matplotlib - Publication Quality Plots
10:12 🎨 6.7.2. Seaborn - Statistical Visualization
11:27 🌐 6.7.3. Plotly - Interactive Visualizations
12:35 📊 6.8. Step 5: Statistical Analysis and Hypothesis Testing
12:46 🔬 6.8.1. Independent T-Test
13:51 🤖 6.9. Step 6: Machine Learning - Predictive Modeling
14:00 🎯 6.9.1. Building a Regression Model
14:41 🏋️ 6.9.2. Training and Evaluating the Model
15:48 📊 6.9.3. Results Summary Dashboard
16:47 🔄 6.10. Complete Workflow Summary
17:13 🎯 6.10.1. Key Takeaways
17:44 🚀 6.10.2. Next Steps and Advanced Topics
18:17 💡 6.11. Practical Tips for Data Science Success
18:23 🔧 6.11.1. Best Practices
18:52 🐛 6.11.2. Common Pitfalls to Avoid
19:22 🎉 Congratulations!