Chapter 1: What is Data Science
- Data Science
- Data science best practices
Chapter 2: Mathematics and statistical methods
- Calculus
- Probability Distributions
- Inferential Statistics
- Linear Algebra
Chapter 3: Data Collection and Storage
- Common Data Sources
- Data Ingestion
- Data Storage
- Managing the Data Lifecycle
Chapter 4: Data Exploration and Analysis
- Exploratory Data Analysis
- Common Data Quality Issues
Chapter 5: Data Processing and Preparation
- Data Transformation
- Data Enrichment and Augmentation
- Data Cleaning
- Handling Class Imbalance
Chapter 6: Modeling and Evaluation
- Types of Models
- Model Design Concepts
- Model Evaluation
Chapter 7: Model Validation and Deployment
- Model Validation
- Communicating Results
- Model Deployment
- Machine Learning Operations (MLOps)
Chapter 8: Unsupervised Machine Learning
- Association Rules
- Clustering
- Dimensionality Reduction
- Recommender Systems
Chapter 9: Supervised Machine Learning
- Linear Regression
- Logistic Regression
- Discriminant Analysis
- Na ve Bayes
- Decision Trees
- Ensemble Methods
Chapter 10: Neural Networks and Deep Learning
- Artificial Neural Networks
- Deep Neural Networks
Chapter 11: Natural Language Processing
- Natural Language3 Processing
- Text Preparation
- Text Representation
Chapter 12: Specialized Applications of Data Science
- Optimization
- Computer Vision