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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

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

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 Beyond Accuracy

Cross-Validation Strategies

Hands-on: Model Training and Hyperparameter Optimization

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

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

Advanced Data Acquisition and Preparation

Effective data science work starts with obtaining and preparing the data. This chapter focuses on the practical steps required to gather information from various origins and refine it into a usable format for analysis.

You will learn techniques for accessing data stored in SQL databases and data warehouses, retrieving information from web APIs, and extracting structured content from websites using web scraping methods. We will also cover advanced strategies for data cleaning, handling missing values beyond simple imputation, applying necessary transformations like scaling $x_{scaled} = (x - \min(x)) / (\max(x) - \min(x))$ and normalization, and integrating data from multiple sources through merging and joining operations. By the end of this chapter, you will have practiced applying these methods to prepare datasets for the subsequent stages of feature engineering and model building.

Sections

1.1 Connecting to Databases and Data Warehouses
1.2 Working with Web APIs for Data Retrieval
1.3 Techniques for Web Scraping Structured Data
1.4 Advanced Data Cleaning Methods
1.5 Strategies for Handling Missing Values
1.6 Data Transformation and Normalization Techniques
1.7 Merging and Joining Diverse Datasets
1.8 Hands-on: Data Acquisition and Wrangling Practice

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