Learning Paths
Curated study guides across all textbooks
9 paths available — click a path to view its steps.
Getting Started with Sports Analytics
A beginner-friendly journey from Python basics through statistics to building your first sports prediction model.
- 1 Introduction to NFL Analytics NFL Analytics Understand what sports analytics is, its history, and why data-driven analysis matters in professional football.
- 2 Python Fundamentals for Sports Data NFL Analytics Learn Python programming essentials tailored for working with sports datasets and APIs.
- 3 Exploratory Data Analysis NFL Analytics Explore real NFL data using pandas, learn to clean datasets, and discover patterns through exploration.
- 4 Statistical Foundations NFL Analytics Master the core statistical concepts needed to analyze sports data and draw meaningful conclusions.
- 5 Descriptive Statistics in Basketball Basketball Analytics Apply descriptive statistics to NBA data, including distributions, correlations, and summary measures.
- 6 Traditional Sports Statistics College Football Analytics Understand traditional box-score statistics and how they form the foundation of modern analytics.
- 7 Introduction to Prediction Models NFL Analytics Build your first predictive model using regression and classification techniques on football data.
- 8 Elo Ratings and Power Rankings NFL Analytics Implement Elo rating systems and power rankings to quantify team strength over time.
Sports Betting Masterclass
From probability fundamentals through expected value, bankroll management, and machine learning-driven betting models.
- 1 Introduction to Sports Betting Sports Betting Learn the landscape of sports betting, market types, and the analytical mindset needed to succeed.
- 2 Probability and Odds Sports Betting Master the relationship between probability and odds formats, including implied probability and the vig.
- 3 Expected Value Sports Betting Understand expected value as the cornerstone of profitable betting and learn to identify positive-EV opportunities.
- 4 Bankroll Management Sports Betting Apply Kelly Criterion and fixed-fraction strategies to manage risk and optimize long-term growth.
- 5 Understanding Betting Markets Sports Betting Explore market efficiency, line movement, steam moves, and how sportsbooks set their lines.
- 6 Value Betting Strategies Sports Betting Learn systematic approaches to identifying and exploiting value in sports betting markets.
- 7 Modeling NFL Games Sports Betting Build quantitative models for predicting NFL game outcomes, point spreads, and totals.
- 8 Feature Engineering for Betting Sports Betting Design and build predictive features from raw sports data to power machine learning betting models.
- 9 ML Betting Pipeline Sports Betting Assemble an end-to-end machine learning pipeline from data ingestion to automated bet selection.
- 10 Psychology of Betting Sports Betting Recognize cognitive biases, manage tilt, and develop the mental discipline required for long-term success.
AI & Deep Learning Track
A rigorous path through mathematical foundations, machine learning, neural networks, NLP, and large language models.
- 1 Linear Algebra for AI AI Engineering Build fluency in vectors, matrices, eigendecomposition, and other linear algebra essentials for AI.
- 2 Probability, Statistics & Information Theory AI Engineering Study probability distributions, Bayes theorem, entropy, and KL-divergence as used in modern AI.
- 3 Supervised Learning AI Engineering Master regression, classification, SVMs, decision trees, and ensemble methods for supervised tasks.
- 4 Feature Engineering AI Engineering Learn feature selection, transformation, encoding, and dimensionality reduction techniques.
- 5 Neural Networks from Scratch AI Engineering Implement feedforward neural networks from the ground up, including backpropagation and gradient descent.
- 6 Convolutional Neural Networks AI Engineering Understand CNN architectures for image recognition, object detection, and visual feature extraction.
- 7 The Attention Mechanism AI Engineering Explore self-attention, multi-head attention, and how attention revolutionized sequence modeling.
- 8 The Transformer Architecture AI Engineering Dive deep into the encoder-decoder transformer, positional encodings, and why transformers dominate NLP.
- 9 Scaling Laws and Large Language Models AI Engineering Study neural scaling laws, emergent abilities, and the engineering behind training frontier LLMs.
- 10 Fine-Tuning LLMs AI Engineering Learn LoRA, QLoRA, instruction tuning, and RLHF techniques for adapting large language models.
Data Visualization Journey
From statistical foundations through matplotlib, spatial charts, and interactive dashboards using real sports data.
- 1 Descriptive Statistics College Football Analytics Learn means, medians, standard deviations, and distributions as the foundation for effective visualization.
- 2 Exploratory Data Analysis Basketball Analytics Use exploratory techniques to uncover patterns, outliers, and relationships in NBA datasets.
- 3 Visualization Fundamentals College Football Analytics Master matplotlib and seaborn basics including bar charts, scatter plots, histograms, and best practices.
- 4 Play-by-Play Visualization College Football Analytics Visualize play-by-play data with drive charts, EPA plots, and game-flow diagrams.
- 5 Comparison Charts College Football Analytics Build radar charts, heat maps, and side-by-side comparisons to evaluate players and teams.
- 6 Pitch Coordinates and Spatial Data Soccer Analytics Work with spatial coordinate systems to plot events on soccer pitch visualizations.
- 7 Spatial Analysis and Field Maps College Football Analytics Create field-based spatial visualizations including pass maps, rush tendency charts, and heat maps.
- 8 Interactive Dashboards College Football Analytics Build interactive web dashboards with Plotly and Dash to create shareable analytics tools.
Prediction Markets & Forecasting
Explore how prediction markets aggregate information, from probability fundamentals through trading strategies to ML-powered forecasting.
- 1 What Are Prediction Markets? Prediction Markets Understand how prediction markets work, why they produce accurate forecasts, and their key design principles.
- 2 Probability Fundamentals Prediction Markets Review probability axioms, conditional probability, and Bayes theorem as applied to market forecasting.
- 3 Contracts, Payoffs, and Mechanics Prediction Markets Learn the structure of binary contracts, multi-outcome markets, and how payoffs are calculated.
- 4 Order Books and Market Microstructure Prediction Markets Study how order books work, bid-ask spreads, and the mechanics of price discovery.
- 5 Calibration and Accuracy Prediction Markets Measure forecasting accuracy using Brier scores, calibration curves, and resolution decomposition.
- 6 Finding Your Edge Prediction Markets Identify systematic edges in prediction markets through domain expertise, models, and information advantages.
- 7 Portfolio and Risk Management Prediction Markets Apply portfolio theory to prediction market positions, managing correlation, drawdown, and position sizing.
- 8 Machine Learning for Prediction Markets Prediction Markets Build ML models to generate probability estimates and identify mispriced contracts in prediction markets.
- 9 Backtesting Strategies Prediction Markets Design rigorous backtests to evaluate strategy performance while avoiding overfitting and lookahead bias.
Full-Stack Sports Data Scientist
A comprehensive advanced track spanning Python, statistics, visualization, machine learning, deep learning, and computer vision across multiple sports.
- 1 Python Fundamentals for Sports NFL Analytics Set up your Python environment and learn pandas, numpy, and data wrangling for sports analysis.
- 2 Statistical Foundations for Sports Soccer Analytics Ground yourself in hypothesis testing, regression, and statistical inference applied to soccer data.
- 3 Data Visualization Fundamentals College Football Analytics Create publication-quality charts and plots to communicate analytical findings effectively.
- 4 Expected Goals (xG) Modeling Soccer Analytics Build expected goals models from shot-level data, the foundational metric of modern soccer analytics.
- 5 Shot Quality Models in Basketball Basketball Analytics Develop shot quality models using spatial data, defender proximity, and game context features.
- 6 Machine Learning Prediction NFL Analytics Apply random forests, gradient boosting, and other ML algorithms to predict NFL game outcomes.
- 7 Neural Networks for Sports Betting Sports Betting Train neural networks for point spread prediction, player prop modeling, and market forecasting.
- 8 Computer Vision in Soccer Soccer Analytics Apply computer vision techniques to video data for player tracking, event detection, and tactical analysis.
- 9 Advanced ML in Basketball Basketball Analytics Use advanced machine learning for lineup optimization, draft modeling, and season projection systems.
- 10 Vision Transformers AI Engineering Study Vision Transformers (ViT) and learn how transformer architectures are applied to sports video and image analysis.
Vibe Coding: From Zero to AI-Powered Developer
Start from scratch and learn to build real software using AI coding assistants — from your first prompt to deploying a web application.
- 1 The Vibe Coding Revolution Vibe Coding Discover what vibe coding is, why it's changing software development, and what you'll learn in this journey.
- 2 How AI Coding Assistants Work Vibe Coding Understand the LLMs behind AI coding tools — how they generate code, their strengths, and their limitations.
- 3 Setting Up Your Environment Vibe Coding Install and configure your IDE, AI coding assistant, and development tools for vibe coding.
- 4 Python Essentials Vibe Coding Learn the Python fundamentals you need to read, understand, and guide AI-generated code effectively.
- 5 Your First Vibe Coding Session Vibe Coding Follow a hands-on walkthrough of your first complete vibe coding session, from prompt to working code.
- 6 Prompt Engineering Fundamentals Vibe Coding Master the core techniques for writing effective prompts that produce high-quality code from AI assistants.
- 7 Iterative Refinement Vibe Coding Learn to iteratively improve AI-generated code through follow-up prompts, feedback loops, and guided corrections.
- 8 CLI Tools and Scripts Vibe Coding Build your first real project — command-line tools and automation scripts using AI-assisted development.
- 9 Web Frontend Development Vibe Coding Create web frontends with AI assistance, covering HTML, CSS, JavaScript, and modern frameworks.
AI-Powered Full-Stack Development
Build production-ready full-stack applications using AI coding assistants — from specification-driven prompting through backend APIs, databases, testing, security, and deployment.
- 1 Specification-Driven Prompting Vibe Coding Learn to write detailed specifications that guide AI to produce well-structured, production-quality code.
- 2 Multiple Files & Large Codebases Vibe Coding Manage multi-file projects and navigate large codebases effectively with AI assistance.
- 3 Backend Development & REST APIs Vibe Coding Build REST APIs and backend services with AI, covering routing, middleware, and API design.
- 4 Database Design & Data Modeling Vibe Coding Design database schemas and data models with AI assistance, covering SQL, ORMs, and migrations.
- 5 Full-Stack Development Vibe Coding Combine frontend and backend into complete full-stack applications using AI-powered workflows.
- 6 External APIs & Integrations Vibe Coding Integrate third-party APIs, webhooks, and external services into your AI-built applications.
- 7 AI-Assisted Testing Vibe Coding Write comprehensive test suites with AI — unit tests, integration tests, and test-driven development.
- 8 Security-First Development Vibe Coding Build secure applications by identifying vulnerabilities, applying security best practices, and using AI for security review.
- 9 DevOps & Deployment Vibe Coding Deploy your applications using CI/CD pipelines, containerization, and cloud platforms with AI assistance.
- 10 Version Control & Workflows Vibe Coding Master Git workflows, branching strategies, and collaborative development practices with AI tools.
AI Engineering to Vibe Coding
Bridge the gap from AI theory to practice — understand transformers, scaling laws, and prompt engineering, then apply that knowledge to build with AI coding agents and multi-agent systems.
- 1 The Transformer Architecture AI Engineering Understand the transformer architecture that powers every modern AI coding assistant.
- 2 Scaling Laws & Large Language Models AI Engineering Study how scaling laws govern LLM capabilities and why bigger models produce better code.
- 3 Prompt Engineering (AI Theory) AI Engineering Learn the theoretical foundations of prompt engineering — few-shot learning, chain-of-thought, and instruction following.
- 4 AI Agents & Tool Use AI Engineering Understand agent architectures, tool use, and how AI systems interact with external environments.
- 5 Prompt Engineering for Code Vibe Coding Apply prompt engineering theory to practical code generation with AI coding assistants.
- 6 Advanced Prompting Techniques Vibe Coding Master advanced prompting strategies including chain-of-thought coding, constraint specification, and meta-prompting.
- 7 AI Coding Agents Vibe Coding Work with autonomous AI coding agents that plan, execute, and iterate on complex software tasks.
- 8 Custom Tools & MCP Servers Vibe Coding Build custom tools and MCP servers to extend AI coding agents with domain-specific capabilities.
- 9 Multi-Agent Systems Vibe Coding Orchestrate multiple AI agents working together on complex software development projects.
- 10 Building AI-Powered Apps Vibe Coding Build applications that integrate LLMs as core features — chatbots, RAG systems, and AI-native software.
- 11 Emerging Frontiers Vibe Coding Explore the cutting edge of AI-assisted development — what's coming next and how to stay ahead.