Applied Data Science: Techniques and Implementation
Chapter 1: Advanced Data Acquisition and Preparation
Connecting to Databases and Data Warehouses
Working with Web APIs for Data Retrieval
Techniques for Web Scraping Structured Data
Advanced Data Cleaning Methods
Strategies for Handling Missing Values
Data Transformation and Normalization Techniques
Merging and Joining Diverse Datasets
Hands-on: Data Acquisition and Wrangling Practice
Quiz for Chapter 1
Chapter 2: Practical Feature Engineering
Generating Features from Numerical Data
Encoding Categorical Variables Effectively
Creating Features from Text Data
Interaction Terms and Polynomial Features
Dimensionality Reduction with PCA
Selecting Features using Statistical Methods
Hands-on: Feature Creation and Selection
Quiz for Chapter 2
Chapter 3: Building and Tuning Predictive Models
Review of Common Supervised Learning Algorithms
Implementing Linear and Logistic Regression
Applying Tree-Based Models
Introduction to Gradient Boosting Machines
Hyperparameter Tuning using Grid Search and Randomized Search
Model Evaluation of Accuracy
Cross-Validation Strategies
Hands-on: Model Training and Hyperparameter Optimization
Quiz for Chapter 3
Chapter 4: Applied Unsupervised Learning
Understanding Clustering Concepts
Implementing K-Means Clustering
Applying DBSCAN for Density-Based Clustering
Introduction to Anomaly Detection Methods
Dimensionality Reduction for Visualization
Hands-on: Clustering and Anomaly Detection Practice
Quiz for Chapter 4
Chapter 5: Model Deployment Fundamentals
Saving and Loading Trained Models
Introduction to Model Serving Frameworks
Building a REST API for Model Prediction
Containerizing Applications with Docker
Basic Model Monitoring Concepts
Hands-on: Creating and Containerizing a Model API
Quiz for Chapter 5
Advanced Data Cleaning Methods
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- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney, 2022 (O'Reilly Media) - An authoritative guide covering data manipulation, cleaning, and preparation using Pandas, including techniques for handling missing data, inconsistent formats, and data transformation.
- Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection, Peter Christen, 2012 (Springer) DOI: 10.1007/978-3-642-28266-9 - A foundational academic book detailing advanced techniques for identifying and linking records, entity resolution, and detecting subtle duplicates using various matching algorithms.
- Outlier Detection: A Survey, Varun Chandola, Arindam Banerjee, and Vipin Kumar, 2009 ACM Computing Surveys, Vol. 41 (Association for Computing Machinery (ACM)) DOI: 10.1145/1541880.1541882 - A highly cited survey paper offering a comprehensive overview and taxonomy of outlier detection methods, including statistical, proximity-based, and model-based approaches.
- Data Cleaning, Ihab F. Ilyas, Xu Chu, 2019 Vol. 28 (Association for Computing Machinery and Morgan & Claypool Publishers) DOI: 10.1145/3342502 - An academic book providing a systematic treatment of modern data cleaning challenges and techniques, covering various data quality problems and their solutions.
- Pandas User Guide: Working with Text Data, The Pandas Development Team, 2024 - Official documentation detailing Pandas' powerful string methods and regular expression capabilities for cleaning, standardizing, and transforming text data.