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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

Applied Data Science: Techniques and Implementation

Prerequisites: Familiarity with Python programming, NumPy, Pandas, and fundamental machine learning concepts (e.g., train/test split, basic model types).

Level: Intermediate

What You'll Learn

Advanced Data Preparation
Implement sophisticated techniques for cleaning, transforming, and integrating diverse datasets.
Feature Engineering
Create and select informative features from raw data to improve model performance.
Model Building and Tuning
Apply and fine-tune various supervised and unsupervised learning algorithms.
Model Evaluation
Utilize appropriate metrics and validation strategies to assess and compare model performance.
Deployment Fundamentals
Understand the basics of preparing and deploying machine learning models using APIs and containers.

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