[
  {
    "slug": "python-programming-fundamentals",
    "title": "Python Programming, Fundamentals",
    "description": "Learn Python from scratch — master variables, data types, control structures, and basic collections with hands-on examples.",
    "difficulty": "Easy",
    "tags": ["Computer Science"],
    "chapterCount": 8
  },
  {
    "slug": "whirlwind-tour-python",
    "title": "A Whirlwind Tour of Python",
    "description": "Python fundamentals covering syntax, variables, operators, built-in types, data structures, and control flow.",
    "difficulty": "Easy",
    "tags": ["Computer Science"],
    "chapterCount": 14
  },
  {
    "slug": "python-programming-intermediate",
    "title": "Python Programming, Intermediate Concepts",
    "description": "Master intermediate Python concepts including functions, data structures, error handling, OOP, and file operations with practical examples and real-world applications.",
    "difficulty": "Medium",
    "tags": ["Computer Science"],
    "chapterCount": 7
  },
  {
    "slug": "unit-testing-csharp",
    "title": "Unit Testing with C#",
    "description": "Write trustworthy, maintainable tests, work with isolation frameworks, handle legacy code in C#.",
    "difficulty": "Medium",
    "tags": ["Computer Science"],
    "chapterCount": 11
  },
  {
    "slug": "statistical-inference-with-r",
    "title": "Statistical Inference with R",
    "description": "A practical introduction to data analysis that covers visualization, wrangling, and linear modeling using R and the tidyverse.",
    "difficulty": "Medium",
    "tags": ["Computer Science", "Statistics"],
    "chapterCount": 11
  },
  {
    "slug": "bayesian-statistics-python",
    "title": "Bayesian Statistics with Python",
    "description": "Practical Python programming and real-world case studies guide the transition from basic probability rules to multi-dimensional parameter estimation.",
    "difficulty": "Medium",
    "tags": ["Computer Science", "Statistics"],
    "chapterCount": 15
  },
  {
    "slug": "econometrics-with-r",
    "title": "Econometrics with R",
    "description": "Bridge the gap between mathematical theory and practical code by applying rigorous econometric models to real-world data in R.",
    "difficulty": "Medium",
    "tags": ["Computer Science", "Economics"],
    "chapterCount": 16
  },
  {
    "slug": "econometrics-with-python",
    "title": "Econometrics with Python",
    "description": "The intersection of economic theory and modern data science. Handle real-world datasets and perform rigorous analysis, from regression techniques to time-series forecasting using Python.",
    "difficulty": "Hard",
    "tags": ["Computer Science", "Economics"],
    "chapterCount": 16
  },
  {
    "slug": "data-science-python",
    "title": "Data Science with Python",
    "description": "An introduction to data science in Python, covering the foundations of NumPy, data manipulation with Pandas, visualization using Matplotlib and Seaborn, and machine learning workflows with Scikit-Learn.",
    "difficulty": "Easy",
    "tags": ["Computer Science", "Data Science"],
    "chapterCount": 18
  },
  {
    "slug": "computational-cognitive-neuroscience",
    "title": "Computational Cognitive Neuroscience",
    "description": "An exploration of the computational mechanisms of the brain, from biological neurons to artificial neural networks. Learn how distributed processing, learning algorithms, and functional brain architecture combine to create perception, memory, and intelligent behavior.",
    "difficulty": "Hard",
    "tags": ["Computer Science", "Psychology"],
    "chapterCount": 10
  },
  {
    "slug": "computational-physics",
    "title": "Computational Physics",
    "description": "A hands-on guide to computational physics using Python and the Scipy stack, covering numerical linear algebra, differential equations, sparse matrices, and Markov Chain Monte Carlo.",
    "difficulty": "Medium",
    "tags": ["Computer Science", "Physics"],
    "chapterCount": 13
  },
  {
    "slug": "data-science-r",
    "title": "Data Science with R",
    "description": "An introduction to the R programming language, focusing on the fundamental data structures, control flow, and functional programming concepts needed for data analysis.",
    "difficulty": "Easy",
    "tags": ["Computer Science", "Data Science"],
    "chapterCount": 10
  },
  {
    "slug": "categorical-data-analysis-r",
    "title": "Categorical Data Analysis with R",
    "description": "Practical statistical methods for analyzing categorical and count data, focusing on logistic and Poisson regression models using R.",
    "difficulty": "Hard",
    "tags": ["Computer Science", "Data Science"],
    "chapterCount": 12
  },
  {
    "slug": "advanced-r",
    "title": "Advanced R",
    "description": "Exploration of R's internal mechanics covering data structures, scoping environments, functional programming, and object-oriented systems.",
    "difficulty": "Medium",
    "tags": ["Computer Science"],
    "chapterCount": 20
  },
  {
    "slug": "advanced-linear-models-r",
    "title": "Advanced Linear Models with R",
    "description": "Classical regression diagnostics, Generalized Linear Models (GLMs) for count and categorical data, and mixed-effects models for correlated observations. Practical data analysis in R is presented alongside non-parametric smoothing, decision trees, and neural networks.",
    "difficulty": "Medium",
    "tags": ["Computer Science", "Statistics"],
    "chapterCount": 15
  }
]