Chapter 1: Introduction to Data Science and Data Mining
- Lesson 1: What is Data Science?
- Lesson 2: What is Data Mining?
- Lesson 3: Importance and Applications
- Lesson 4: The Data Science Lifecycle
- Lesson 5: Overview of the Course
Chapter 2: Understanding Data
- Lesson 1: Types of Data (Structured, Unstructured, Semi-Structured)
- Lesson 2: Data Sources and Data Collection Methods
- Lesson 3: Data Storage and Databases
- Lesson 4: Metadata and Data Provenance
- Lesson 5: Web Scraping Basics
- Lesson 6: APIs for Data Extraction
- Lesson 7: Introduction to Big Data
- Lesson 8: Data Warehouses and Data Lakes
Chapter 3: Data Preprocessing
- Lesson 1: Data Cleaning (Handling Missing Values, Outliers)
- Lesson 2: Data Transformation (Normalization, Standardization)
- Lesson 3: Data Integration and Reduction
- Lesson 4: Feature Selection and Engineering
- Lesson 5: Handling Categorical and Numerical Data
Chapter 4: Data Exploration and Visualization
- Lesson 1: Exploratory Data Analysis (EDA)
- Lesson 2: Data Visualization Techniques
- Lesson 3: Using Python Libraries (Matplotlib, Seaborn)
- Lesson 4: Interactive Data Visualization with Plotly
- Lesson 5: Storytelling with Data
Chapter 5: Introduction to Statistical Analysis
- Lesson 1: Descriptive vs Inferential Statistics
- Lesson 2: Measures of Central Tendency and Variability
- Lesson 3: Probability Distributions and Sampling
- Lesson 4: Correlation vs Causation
- Lesson 5: Hypothesis Testing Basics
Chapter 6: Introduction to Data Mining Techniques
- Lesson 1: What is Data Mining?
- Lesson 2: Data Mining Process
- Lesson 3: Predictive vs Descriptive Data Mining
- Lesson 4: Ethical Considerations in Data Mining
- Lesson 5: Common Data Mining Tools
Chapter 7: Association Rule Mining
- Lesson 1: Market Basket Analysis
- Lesson 2: Apriori Algorithm
- Lesson 3: FP-Growth Algorithm
- Lesson 4: Evaluation Metrics (Support, Confidence, Lift)
- Lesson 5: Applications in Business
Chapter 8: Clustering Techniques
- Lesson 1: Introduction to Clustering
- Lesson 2: K-Means Clustering
- Lesson 3: Hierarchical Clustering
- Lesson 4: DBSCAN Clustering
- Lesson 5: Evaluating Clustering Performance
Chapter 9: Introduction to Classification Methods
- Lesson 1: What is Classification?
- Lesson 2: Decision Trees
- Lesson 3: K-Nearest Neighbors (KNN)
- Lesson 4: Support Vector Machines (SVM)
- Lesson 5: Evaluating Classification Models
Chapter 10: Regression Techniques
- Lesson 1: Introduction to Regression
- Lesson 2: Simple Linear Regression
- Lesson 3: Multiple Regression
- Lesson 4: Polynomial Regression
- Lesson 5: Evaluating Regression Models
Chapter 11: Anomaly Detection
- Lesson 1: Importance of Anomaly Detection
- Lesson 2: Statistical Approaches
- Lesson 3: Clustering-Based Anomaly Detection
- Lesson 4: Isolation Forests
- Lesson 5: Applications in Fraud Detection
Chapter 12: Introduction to Feature Selection Techniques
- Lesson 1: What is Feature Selection?
- Lesson 2: Filter Methods (Chi-Square, Mutual Information)
- Lesson 3: Wrapper Methods (Forward, Backward Selection)
- Lesson 4: Embedded Methods (Lasso, Ridge)
- Lesson 5: Principal Component Analysis (PCA)
Chapter 13: Introduction to Time Series Analysis
- Lesson 1: Time Series Data and Its Characteristics
- Lesson 2: Moving Averages and Smoothing Techniques
- Lesson 3: Trend and Seasonality Detection
- Lesson 4: ARIMA Model Basics
- Lesson 5: Time Series Applications
Chapter 14: Data Science with Python
- Lesson 1: Introduction to Python for Data Science
- Lesson 2: Data Manipulation with Pandas
- Lesson 3: Numerical Computation with NumPy
- Lesson 4: Data Visualization with Matplotlib and Seaborn
- Lesson 5: Hands-On Data Science Project
Chapter 15: Data Science with R
- Lesson 1: Introduction to R for Data Science
- Lesson 2: Data Manipulation with dplyr
- Lesson 3: Data Visualization with ggplot2
- Lesson 4: Statistical Analysis in R
- Lesson 5: Hands-On Data Science Project in R
Chapter 16: Introduction to Business Intelligence and Analytics
- Lesson 1: What is Business Intelligence?
- Lesson 2: Business Analytics vs Data Science
- Lesson 3: BI Tools (Tableau, Power BI)
- Lesson 4: Creating Dashboards and Reports
- Lesson 5: Case Study
Chapter 17: Introduction to Cloud and Big Data Technologies
- Lesson 1: Cloud Computing in Data Science
- Lesson 2: Introduction to Hadoop and Spark
- Lesson 3: NoSQL Databases (MongoDB, Cassandra)
- Lesson 4: Cloud Platforms (AWS, GCP, Azure)
- Lesson 5: Data Lakes vs Data Warehouses
Chapter 18: Introduction to Recommender Systems
- Lesson 1: What is a Recommender System?
- Lesson 2: Content-Based Filtering
- Lesson 3: Collaborative Filtering
- Lesson 4: Hybrid Methods
- Lesson 5: Evaluation Metrics
Chapter 19: Ethics and Legal Aspects in Data Science
- Lesson 1: Data Privacy and Security
- Lesson 2: GDPR and Data Protection Laws
- Lesson 3: Bias in Data Science
- Lesson 4: Responsible AI and Fairness
- Lesson 5: Case Studies in Ethical Data Science
Chapter 20: Hands-on Data Science Projects
- Lesson 1: Real-World Data Science Projects
- Lesson 2: Exploratory Data Analysis Project
- Lesson 3: Data Cleaning and Preprocessing Project
- Lesson 4: Predictive Modeling Mini-Project
- Lesson 5: Final Project Guidelines
Chapter 21: Future of Data Science
- Lesson 1: Emerging Trends in Data Science
- Lesson 2: Explainable AI and Model Interpretability
- Lesson 3: Data Science for Social Good
- Lesson 4: Career Paths in Data Science
- Lesson 5: Course Recap and Next Steps
Chapter 1: Advanced Feature Engineering
- Lesson 1: Handling High-Dimensional Data
- Lesson 2: Feature Encoding Techniques
- Lesson 3: Feature Selection using Mutual Information
- Lesson 4: Feature Construction
- Lesson 5: Auto-Feature Engineering
Chapter 2: Advanced Dimensionality Reduction
- Lesson 1: Kernel PCA
- Lesson 2: Autoencoders for Feature Reduction
- Lesson 3: Independent Component Analysis
- Lesson 4: Feature Clustering
- Lesson 5: UMAP
Chapter 3: Advanced Data Mining Techniques
- Lesson 1: Ensemble Learning Methods
- Lesson 2: Outlier Detection in Large Datasets
- Lesson 3: Graph-Based Data Mining
- Lesson 4: Frequent Pattern Mining
- Lesson 5: Anomaly Detection Techniques
Chapter 4: Deep Data Visualization
- Lesson 1: Interactive Dashboards
- Lesson 2: 3D Visualization
- Lesson 3: Geospatial Data Visualization
- Lesson 4: Advanced Plotting Libraries (Plotly, Bokeh)
- Lesson 5: Real-Time Data Visualization
Chapter 5: Web Scraping and Data Harvesting
- Lesson 1: Advanced Web Scraping with Selenium
- Lesson 2: API Integration for Data Collection
- Lesson 3: Web Data Storage Techniques
- Lesson 4: Ethical Considerations in Web Scraping
- Lesson 5: Automating Data Pipelines
Chapter 6: Data Science in Production
- Lesson 1: Model Deployment Strategies
- Lesson 2: MLOps for Data Science
- Lesson 3: Continuous Integration in Data Pipelines
- Lesson 4: Serverless Data Processing
- Lesson 5: Real-Time Analytics
Chapter 7: Advanced Recommender Systems
- Lesson 1: Bayesian Personalized Ranking
- Lesson 2: Deep Learning-based Recommenders
- Lesson 3: Reinforcement Learning in Recommendations
- Lesson 4: Scalability Challenges
- Lesson 5: Case Studies
Chapter 8: Graph Analytics and Network Science
- Lesson 1: Graph Theory Basics
- Lesson 2: Social Network Analysis
- Lesson 3: Graph Databases (Neo4j)
- Lesson 4: Community Detection
- Lesson 5: Applications in Cybersecurity
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