plan-data.js

window.__PLAN_DATA__ = {
  "months": [
    {
      "name": "Month 1: Foundations",
      "focus": "Math-first with light DSA: arrays, strings, hash maps, two pointers",
      "dsaTopics": [
        "ARRAYS",
        "STRINGS",
        "SLIDING WINDOW"
      ],
      "math": [
        "Arithmetic: order of operations",
        "Arithmetic: fractions and decimals",
        "Arithmetic: negative numbers",
        "Pre-algebra: variables",
        "Pre-algebra: exponents",
        "Pre-algebra: ratios and proportions",
        "Algebra: solving one-step equations",
        "Algebra: solving two-step equations",
        "Algebra: linear equations",
        "Algebra: slope and intercept",
        "Algebra: functions (input/output)",
        "Algebra: graph a line",
        "Algebra: inequalities",
        "Algebra: absolute value",
        "Algebra: word problems",
        "Algebra: systems of equations (intro)",
        "Algebra: simplifying expressions",
        "Algebra: factoring basics",
        "Algebra: rearranging formulas",
        "Algebra: review and recap",
        "Algebra: quick quiz day"
      ],
      "companies": [
        "Amazon",
        "Google",
        "Meta"
      ]
    },
    {
      "name": "Month 2: Data Structures and Linear Algebra",
      "focus": "Alternate DSA and math daily",
      "dsaTopics": [
        "LINKED LIST",
        "STACK",
        "BINARY SEARCH",
        "BINARY TREE"
      ],
      "math": [
        "Vectors: magnitude and direction",
        "Vectors: addition and subtraction",
        "Vectors: dot product",
        "Matrices: add and multiply",
        "Matrices: identity and inverse idea",
        "Matrices: transpose",
        "Systems of equations (matrix form)",
        "Linear transformations (intuition)",
        "Basis and span (idea)",
        "Eigenvalues (intuition only)",
        "Matrix rank (intuition)",
        "Vector norms (L1, L2)",
        "Projections and similarity",
        "Orthogonality",
        "Data as vectors (features)",
        "Cosine similarity (idea)",
        "Matrix multiplication in ML",
        "Linear regression math overview",
        "Loss functions (MSE)",
        "Gradient intuition without calculus",
        "Review: vectors and matrices"
      ],
      "companies": [
        "Amazon",
        "Google",
        "Meta"
      ]
    },
    {
      "name": "Month 3: Graphs and Dynamic Programming",
      "focus": "Graphs + DP, probability basics",
      "dsaTopics": [
        "DP",
        "RECURSION",
        "BINARY TREE"
      ],
      "math": [
        "Probability: sample space",
        "Probability: independent events",
        "Probability: conditional probability",
        "Probability: Bayes rule",
        "Random variables",
        "Expected value",
        "Variance",
        "Standard deviation",
        "Common distributions (Bernoulli)",
        "Binomial distribution",
        "Normal distribution idea",
        "Law of large numbers (intuition)",
        "Entropy (idea)",
        "KL divergence (idea)",
        "Information gain (idea)",
        "Stats: mean vs median",
        "Stats: bias and variance",
        "Stats: correlation",
        "Stats: covariance",
        "Review: probability"
      ],
      "companies": [
        "Amazon",
        "Google",
        "Meta"
      ]
    },
    {
      "name": "Month 4: Calculus and Optimization",
      "focus": "Derivatives, gradients, optimization",
      "dsaTopics": [
        "DP",
        "BINARY SEARCH",
        "BINARY TREE"
      ],
      "math": [
        "Derivatives: slope of a curve",
        "Derivative rules (power rule)",
        "Chain rule concept",
        "Partial derivatives",
        "Gradients and direction",
        "Tangent line idea",
        "Local minima and maxima",
        "Convex vs non-convex",
        "Gradient descent steps",
        "Learning rate intuition",
        "Loss surfaces (intuition)",
        "Overfitting vs underfitting",
        "Regularization (L2)",
        "Regularization (L1)",
        "Log loss intuition",
        "Softmax idea",
        "Backprop overview",
        "Vectorized gradients",
        "Numerical stability",
        "Optimization recap",
        "Review day"
      ],
      "companies": [
        "Amazon",
        "Google",
        "Meta"
      ]
    },
    {
      "name": "Month 5: Neural Nets and Transformers",
      "focus": "NN components, attention, LLM basics",
      "dsaTopics": [
        "DP",
        "BINARY TREE",
        "RECURSION",
        "STACK"
      ],
      "math": [
        "Neuron idea: weighted sum",
        "Activation functions (ReLU, sigmoid)",
        "Loss functions for classification",
        "Backprop in a 2-layer net",
        "Gradient checking (idea)",
        "Overfitting and dropout",
        "Batch norm intuition",
        "Embeddings idea",
        "Sequence modeling basics",
        "RNN vs LSTM (idea)",
        "CNN basics (filters)",
        "Attention intuition",
        "Scaled dot-product attention",
        "Multi-head attention",
        "Positional encoding",
        "Transformer blocks",
        "Residual connections",
        "Layer norm",
        "Tokenization basics",
        "Training loop overview",
        "Evaluation metrics (perplexity)",
        "Inference basics",
        "Prompting basics",
        "RLHF overview (concept)",
        "Safety and alignment basics",
        "Review and recap"
      ],
      "companies": [
        "Amazon",
        "Google",
        "Meta"
      ]
    },
    {
      "name": "Month 6: Company Mix and Interview Polish",
      "focus": "Timed sets, system design, mock loops",
      "dsa": [
        "Timed set: 2 easy + 1 medium",
        "Timed set: 3 mediums",
        "Timed set: 1 hard + 1 medium",
        "Company mix: Amazon tag set",
        "Company mix: Google tag set",
        "Company mix: Meta tag set",
        "Mock interview: full 45 min",
        "System design drill: API + data model",
        "System design drill: scaling",
        "Behavioral story: conflict",
        "Behavioral story: leadership",
        "Behavioral story: failure",
        "Behavioral story: impact",
        "Design review: tradeoffs",
        "Complexity analysis review",
        "Re-do 3 weakest problems",
        "Re-do 3 hardest problems",
        "Re-do 3 graph problems",
        "Re-do 3 DP problems",
        "One-page resume review",
        "Mock interview: full 45 min",
        "System design: caching",
        "System design: queues",
        "System design: sharding",
        "System design: observability",
        "Behavioral: teamwork",
        "Behavioral: ownership",
        "Behavioral: ambiguity",
        "Full loop simulation",
        "Rest and reset"
      ],
      "math": [
        "Review: vectors and matrices",
        "Review: probability and stats",
        "Review: gradients and optimization",
        "LLM inference basics (batching, cache)",
        "RL recap: rewards and value",
        "LLM systems: latency vs throughput",
        "Paper reading: summarize one abstract",
        "Paper reading: outline sections",
        "Paper reading: rewrite methods",
        "Paper reading: note assumptions",
        "Paper reading: list limitations",
        "Paper reading: find baselines",
        "Paper reading: explain result table",
        "Paper reading: explain ablation",
        "Paper reading: reproduce a figure",
        "Research write-up: 1 page",
        "Explain a system design",
        "Explain a DP recurrence",
        "Explain a graph approach",
        "Explain a transformer",
        "Explain a loss function",
        "Explain a metric",
        "Explain a tradeoff",
        "Explain a failure case",
        "Explain a fix",
        "Explain a result",
        "Explain a paper in 5 lines",
        "Explain a model in 5 lines",
        "Write 10 flashcards",
        "Rest and recap"
      ],
      "companies": [
        "Amazon",
        "Google",
        "Meta",
        "Apple",
        "Netflix"
      ]
    }
  ]
};