Month 21: Data Science and Machine Learning Fundamentals
Week 1: Introduction to Data Science
- Day 1: Understanding Data Science
- Day 2: Role of a Data Scientist
- Day 3: Essential Skills for Data Science
- Day 4: Data Science Tools and Libraries
- Day 5: Data Science Applications and Case Studies
Week 2: Introduction to Machine Learning
- Day 1: Understanding Machine Learning
- Day 2: Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Day 3: Introduction to Regression and Classification
- Day 4: Introduction to Clustering and Dimensionality Reduction
- Day 5: Introduction to Ensemble Methods
Week 3: Preprocessing and Exploratory Data Analysis
- Day 1: Data Preprocessing: Cleaning, Transformation, Feature Scaling
- Day 2: Exploratory Data Analysis: Understanding the Data
- Day 3: Visualization Techniques for Data Analysis
- Day 4: Handling Missing Values
- Day 5: Feature Engineering and Selection
Week 4: Model Evaluation and Selection
- Day 1: Understanding Overfitting and Underfitting
- Day 2: Train/Test Split and Cross-Validation
- Day 3: Performance Metrics for Classification
- Day 4: Performance Metrics for Regression
- Day 5: Model Selection Techniques
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