Principles of Data Science Course

Lesson 1 • What Data Science Is

Defines data science, distinguishes it from related fields, and maps its core components. Establishes shared vocabulary used throughout the course.

Lesson 2 • Types of Data and Problems

Categorizes structured, unstructured, and semi-structured data and maps them to problem types. Helps practitioners choose appropriate methods early in a project.

Lesson 3 • Ethics and Responsible Practice

Covers bias, fairness, privacy, and transparency as foundational professional obligations. Frames ethical reasoning as integral to every stage of the workflow.

Lesson 1 • Probability Theory Fundamentals

Establishes probability rules, distributions, and conditional reasoning essential for modeling uncertainty. Directly supports statistical inference and model evaluation.

Lesson 2 • Descriptive Statistics and Distributions

Covers measures of central tendency, spread, and shape to summarize datasets. Builds the analytical foundation for exploratory data analysis in the next chapter.

Lesson 3 • Calculus Concepts for Optimization

Introduces derivatives and gradients as tools for minimizing loss functions. Provides the intuition behind how models learn from data.

Lesson 4 • Statistical Inference and Hypothesis Testing

Teaches estimation, confidence intervals, and significance testing for drawing conclusions from samples. Equips practitioners to validate findings rigorously.

Lesson 1 • Handling Missing and Noisy Data

Identifies patterns of missingness and applies imputation and filtering strategies. Prevents data quality issues from corrupting downstream models.

Lesson 2 • Data Acquisition and Ingestion

Covers reading data from files, databases, and APIs into a working environment. Sets the starting point for every subsequent preparation step.

Lesson 3 • Data Validation and Quality Checks

Establishes systematic checks to verify schema, ranges, and consistency before modeling. Instills a quality-first mindset critical for production pipelines.

Lesson 1 • Generating and Testing Hypotheses

Translates visual patterns into testable hypotheses using statistical tests introduced earlier. Connects EDA findings to formal analytical conclusions.

Lesson 2 • Bivariate and Multivariate Analysis

Explores relationships between two or more variables using correlation and cross-tabulation. Reveals feature interactions that inform model selection.

Lesson 3 • Effective Data Visualization

Applies design principles to create clear, accurate, and audience-appropriate charts. Ensures that analytical findings are communicated without distortion.

Lesson 1 • Model Evaluation and Selection

Applies cross-validation, performance metrics, and learning curves to compare models fairly. Prevents overfitting and ensures generalization to unseen data.

Lesson 2 • Ensemble Methods

Combines multiple models through bagging and boosting to improve accuracy and robustness. Represents the state of practice for many tabular data competitions.

Lesson 3 • Regression Algorithms

Covers linear, polynomial, and regularized regression for predicting continuous outcomes. Provides the simplest entry point into supervised modeling.

Lesson 4 • Hyperparameter Tuning

Optimizes model configuration using grid search, random search, and Bayesian methods. Maximizes model performance within computational constraints.

Lesson 1 • Dimensionality Reduction

Compresses high-dimensional data while preserving structure using PCA and manifold methods. Supports visualization, noise reduction, and faster modeling.

Lesson 2 • Association Rule Learning

Extracts co-occurrence patterns from transactional data using support, confidence, and lift metrics. Commonly applied in recommendation and market basket analysis.

Lesson 3 • Clustering Algorithms

Groups observations by similarity using k-means, hierarchical, and density-based methods. Enables segmentation and pattern discovery without labeled targets.

Lesson 1 • Productionizing Machine Learning Models

Covers serialization, API wrapping, and containerization to serve models reliably. Bridges the gap between experimentation and production engineering.

Lesson 2 • Building Reproducible Pipelines

Automates data preprocessing and model training steps into versioned, repeatable workflows. Ensures consistent results across environments and team members.

Lesson 3 • CI/CD for Machine Learning

Applies continuous integration and delivery principles to automate model testing and release. Reduces manual errors and accelerates safe deployment cycles.

Lesson 1 • Data Science Strategy and Roadmapping

Frames how to prioritize projects, measure ROI, and build team capabilities over time. Prepares practitioners to lead data science functions strategically.

Lesson 2 • Communicating Results to Stakeholders

Structures data-driven narratives and translates technical findings into business recommendations. Ensures analytical work drives decisions rather than sitting unused.

Lesson 3 • Natural Language Processing Basics

Applies text preprocessing, vectorization, and classification to language data. Enables practitioners to extract value from unstructured text sources.

Lesson 4 • Deep Learning Fundamentals

Introduces neural network architecture, training, and regularization for complex pattern recognition. Extends supervised learning skills to high-dimensional and unstructured data.