Learning Paths

Curated study guides across all textbooks

46 paths available — click a path to view its steps.

  1. 1 Python Fundamentals for Sports NFL Analytics Set up your Python environment and learn pandas, numpy, and data wrangling for sports analysis.
  2. 2 Statistical Foundations for Sports Soccer Analytics Ground yourself in hypothesis testing, regression, and statistical inference applied to soccer data.
  3. 3 Data Visualization Fundamentals College Football Analytics Create publication-quality charts and plots to communicate analytical findings effectively.
  4. 4 Expected Goals (xG) Modeling Soccer Analytics Build expected goals models from shot-level data, the foundational metric of modern soccer analytics.
  5. 5 Shot Quality Models in Basketball Basketball Analytics Develop shot quality models using spatial data, defender proximity, and game context features.
  6. 6 Machine Learning Prediction NFL Analytics Apply random forests, gradient boosting, and other ML algorithms to predict NFL game outcomes.
  7. 7 Neural Networks for Sports Betting Sports Betting Train neural networks for point spread prediction, player prop modeling, and market forecasting.
  8. 8 Computer Vision in Soccer Soccer Analytics Apply computer vision techniques to video data for player tracking, event detection, and tactical analysis.
  9. 9 Advanced ML in Basketball Basketball Analytics Use advanced machine learning for lineup optimization, draft modeling, and season projection systems.
  10. 10 Vision Transformers AI Engineering Study Vision Transformers (ViT) and learn how transformer architectures are applied to sports video and image analysis.
  1. 1 The Transformer Architecture AI Engineering Understand the transformer architecture that powers every modern AI coding assistant.
  2. 2 Scaling Laws & Large Language Models AI Engineering Study how scaling laws govern LLM capabilities and why bigger models produce better code.
  3. 3 Prompt Engineering (AI Theory) AI Engineering Learn the theoretical foundations of prompt engineering — few-shot learning, chain-of-thought, and instruction following.
  4. 4 AI Agents & Tool Use AI Engineering Understand agent architectures, tool use, and how AI systems interact with external environments.
  5. 5 Prompt Engineering for Code Vibe Coding Apply prompt engineering theory to practical code generation with AI coding assistants.
  6. 6 Advanced Prompting Techniques Vibe Coding Master advanced prompting strategies including chain-of-thought coding, constraint specification, and meta-prompting.
  7. 7 AI Coding Agents Vibe Coding Work with autonomous AI coding agents that plan, execute, and iterate on complex software tasks.
  8. 8 Custom Tools & MCP Servers Vibe Coding Build custom tools and MCP servers to extend AI coding agents with domain-specific capabilities.
  9. 9 Multi-Agent Systems Vibe Coding Orchestrate multiple AI agents working together on complex software development projects.
  10. 10 Building AI-Powered Apps Vibe Coding Build applications that integrate LLMs as core features — chatbots, RAG systems, and AI-native software.
  11. 11 Emerging Frontiers Vibe Coding Explore the cutting edge of AI-assisted development — what's coming next and how to stay ahead.
  1. 1 Introduction to Cross-Domain Thinking Pattern Recognition Understand why the same deep structures keep appearing across wildly different fields.
  2. 2 Feedback Loops Pattern Recognition Recognize positive and negative feedback loops and how they drive growth, stability, and collapse.
  3. 3 Emergence Pattern Recognition See how simple rules produce complex, unpredictable behavior — from ant colonies to stock markets.
  4. 4 Power Laws Pattern Recognition Learn why extreme events are far more common than normal distributions predict, and what that means for planning.
  5. 5 Phase Transitions Pattern Recognition Understand how systems flip suddenly from one state to another — and why gradual change can mask approaching tipping points.
  6. 6 Distributed vs. Centralized Systems Pattern Recognition Compare the tradeoffs between centralized control and distributed networks in organizations, ecosystems, and software.
  7. 7 Redundancy and Resilience Pattern Recognition Discover why apparent inefficiency — redundant systems, slack resources — is often the key to surviving shocks.
  8. 8 Cascading Failures Pattern Recognition Trace how small failures propagate through tightly coupled systems to produce catastrophic outcomes.
  9. 9 Scaling Laws Pattern Recognition Explore the mathematical relationships that govern how systems change as they grow — from cities to organisms to companies.
  10. 10 The S-Curve Pattern Recognition Recognize the ubiquitous S-shaped growth pattern and learn to identify where you are on the curve.
  11. 11 How to Think Across Domains Pattern Recognition Build a personal practice for spotting patterns, transferring mental models, and thinking in systems.
  1. 1 Calibration: Knowing What You Know Metacognition Learn to align your confidence with your actual accuracy — the single most important metacognitive skill for decision-making.
  2. 2 Metacognitive Monitoring Metacognition Develop the habit of stepping back to evaluate your own thinking processes in real time.
  3. 3 Motivation and Self-Regulation Metacognition Understand how motivation biases your reasoning and learn strategies for staying intellectually honest.
  4. 4 Goodhart's Law Pattern Recognition Discover why optimizing for a measure inevitably corrupts it — and how this trap undermines organizations everywhere.
  5. 5 Survivorship Bias Pattern Recognition Learn to see the data that is missing — the failures, the silent evidence, the stories that never get told.
  6. 6 Narrative Capture Pattern Recognition Recognize how compelling stories hijack your reasoning and lead you to ignore contradictory evidence.
  7. 7 Chesterton's Fence Pattern Recognition Before tearing something down, understand why it was built — a principle for avoiding unintended consequences.
  8. 8 The Map Is Not the Territory Pattern Recognition Understand the gap between models and reality, and learn when to trust (and distrust) abstractions.
  9. 9 How Brains Misread Luck Science of Luck Explore the cognitive biases that cause us to see patterns in randomness and misattribute outcomes to skill.
  10. 10 Emotional Intelligence in Decisions Applied Psychology Learn how emotions shape your judgments and how emotional intelligence improves decision quality.
  1. 1 The World of COBOL Learning COBOL Discover why COBOL still powers the global economy and begin your journey into mainframe programming.
  2. 2 COBOL Program Structure Learning COBOL Learn the four divisions of a COBOL program and write your first working program.
  3. 3 Conditional Logic Learning COBOL Master IF/ELSE, EVALUATE, and condition handling to build decision-making programs.
  4. 4 Sequential File Processing Learning COBOL Learn to read and write sequential files, the backbone of batch processing on mainframes.
  5. 5 COBOL Program Structure Deep Dive Intermediate COBOL Deepen your understanding of COBOL program architecture and advanced division features.
  6. 6 CALL and Subprogram Linkage Intermediate COBOL Build modular COBOL applications using CALL statements and subprogram linkage conventions.
  7. 7 Embedded SQL Fundamentals Intermediate COBOL Connect COBOL programs to DB2 databases using embedded SQL for enterprise data access.
  8. 8 The z/OS Ecosystem Advanced COBOL Understand the z/OS mainframe ecosystem and how COBOL fits into enterprise architecture.
  9. 9 CICS Architecture Advanced COBOL Master CICS transaction server architecture for building online mainframe applications.
  10. 10 DB2 Optimizer Deep Dive Advanced COBOL Learn how the DB2 optimizer works to write high-performance COBOL-DB2 programs.
  11. 11 Mainframe Security Advanced COBOL Understand RACF, security models, and compliance requirements for enterprise mainframe systems.
  12. 12 Modernization Strategy Advanced COBOL Explore strategies for modernizing legacy COBOL systems while preserving business logic.
  1. 1 What is Data Science? Intro to Data Science Explore the data science landscape, career paths, and the tools used by data scientists.
  2. 2 Python Fundamentals for Data Science Intro to Data Science Learn core Python programming concepts tailored for data analysis workflows.
  3. 3 Introduction to pandas Intro to Data Science Master the pandas library for loading, exploring, and manipulating tabular data.
  4. 4 Cleaning Messy Data Intro to Data Science Learn techniques for handling missing values, duplicates, and inconsistent data.
  5. 5 Matplotlib Foundations Intro to Data Science Create informative charts and graphs to visualize patterns in your data.
  6. 6 Graphs and Descriptive Statistics Introductory Statistics Build a statistical foundation with histograms, box plots, and summary measures.
  7. 7 Probability Foundations Introductory Statistics Learn probability rules and how they underpin statistical inference and prediction.
  8. 8 Descriptive Statistics in Python Intro to Data Science Compute and interpret descriptive statistics using Python for real-world datasets.
  9. 9 Hypothesis Testing Intro to Data Science Apply hypothesis testing to make data-driven decisions with statistical confidence.
  10. 10 Linear Regression Intro to Data Science Build your first predictive model using linear regression and interpret its results.
  11. 11 Decision Trees and Random Forests Intro to Data Science Learn tree-based models for classification and regression tasks.
  12. 12 Evaluating Models Intro to Data Science Master metrics and validation techniques to assess how well your models perform.
  1. 1 The Archaeology of Error How Humans Get Stuck Discover how false beliefs take root and why some errors persist for centuries.
  2. 2 The Authority Cascade How Humans Get Stuck Understand how deference to authority propagates errors through institutions and cultures.
  3. 3 Survivorship Bias at Scale How Humans Get Stuck Learn why we systematically overlook failures and how this distorts our understanding of success.
  4. 4 The Sunk Cost of Consensus How Humans Get Stuck Explore how group consensus creates inertia that prevents correction of known errors.
  5. 5 The Einstellung Effect How Humans Get Stuck Understand how expertise can become a trap when familiar solutions block better alternatives.
  6. 6 Cognitive Load and Its Limits Metacognition Learn how cognitive load affects judgment and how to manage your mental resources.
  7. 7 Calibration: Knowing What You Know Metacognition Develop the critical skill of accurately judging your own knowledge and confidence.
  8. 8 Desirable Difficulties Metacognition Discover why the things that feel hardest often produce the deepest learning.
  9. 9 Red Flags for Bad Thinking How Humans Get Stuck Build a practical checklist for spotting unreliable claims and flawed reasoning.
  10. 10 How to Disagree Productively How Humans Get Stuck Learn frameworks for constructive disagreement that advance understanding rather than entrench positions.
  11. 11 Cognitive Biases in Everyday Life Applied Psychology Apply psychological research on cognitive biases to recognize and counteract them in daily decisions.
  1. 1 Descriptive Statistics Introductory Statistics Build a foundation with graphical displays, distributions, and summary statistics.
  2. 2 Probability Distributions Introductory Statistics Understand the probability distributions that underpin statistical modeling and ML.
  3. 3 Hypothesis Testing Introductory Statistics Master the logic of statistical inference that distinguishes signal from noise.
  4. 4 Regression Foundations Introductory Statistics Learn correlation and simple linear regression as the gateway to predictive modeling.
  5. 5 Pandas for Data Wrangling Intro to Data Science Prepare data for modeling using pandas data manipulation capabilities.
  6. 6 Linear Regression in Python Intro to Data Science Implement linear regression with scikit-learn and interpret model outputs.
  7. 7 Logistic Regression Intro to Data Science Extend regression to classification problems using logistic regression.
  8. 8 Decision Trees and Random Forests Intro to Data Science Build tree-based models that handle nonlinear patterns and feature interactions.
  9. 9 Evaluating Models Intro to Data Science Learn cross-validation, ROC curves, and other techniques to assess model quality.
  10. 10 AI and ML for Business Strategy AI & ML for Business Understand how organizations deploy AI and ML to create competitive advantage.
  11. 11 Supervised Learning in Practice AI & ML for Business Apply supervised learning techniques to real business problems and datasets.
  12. 12 AI Implementation and Ethics AI & ML for Business Navigate the practical and ethical challenges of deploying AI in business contexts.