Reference and Selective Reading

You do not need to read Grokking Algorithms front-to-back for this module. Use the concept and practice pages first. Open these only when you want the original explanation, more examples, or a deeper look at the same idea.

Scope Note

This reference page maps only the Grokking Algorithms material that supports the current Semester 0 intuition module.

It does not attempt to cover every chapter in the book. Later topics such as Dijkstra, greedy algorithms, dynamic programming, k-nearest neighbors, and trees are intentionally deferred so Semester 0 stays focused on first-pass algorithm intuition rather than full algorithm coverage.

Read Only If Stuck

Optional Deep Dive

Source-Role Clarity

Primary teaching source: the concept and practice pages in this module
Selective reference: the linked Grokking Algorithms chunks from chapters 1-6
Exercise source: the drills and practice pages in this module
Official docs: not required here because this module is concept-heavy rather than tool- or API-heavy
Optional deep dive: hash-table use cases, graph extras, and the full chunk index

Concept-to-Source Map

Concept page	Best source if stuck	Why this source
Search by Eliminating Half the Possibilities	Binary search / A better way to search / Logarithms	Best first explanation of halving the search space and why sorted order matters
Growth Rate Beats Raw Timing	What Big O	Best for growth-rate intuition and common runtime families
Data Shape Controls What Feels Fast	Arrays and 5 Hash Tables	Best for storage tradeoffs and key-based lookup
Break Big Problems Into Smaller Ones	Recursion and 3 Recursion	Best for base case thinking and understanding how the call stack holds unfinished work
Sorting Reveals Algorithm Shape	Example Code Listing, 4 Quicksort, and Quicksort (Part 1)	Best for comparing repeated full scans with divide-and-conquer partitioning
Think in Graphs, Then Search Breadth-First	6 Breadth-first Search and Queues / Running time	Best for graph modeling, BFS waves, queue behavior, and shortest unweighted path reasoning

Direct Chunk Shortcuts By Need

If you need help with...	Best chunk
why binary search is dramatically faster	Running time / Big O notation
common runtime shapes	What Big O
array vs linked-list inserts and deletes	Terminology / Unordered Ordered / Deletions
why selection sort stays slow	Example Code Listing
how divide-and-conquer reduces a problem	4 Quicksort
how quicksort partitions around a pivot	Quicksort (Part 1)
why hash tables are fast on average but not magic	Collisions / Performance / Load factor
how recursion uses the call stack	3 Recursion
why BFS needs a queue and a visited set	Queues / Running time
task ordering from dependencies	6 Breadth-first Search

Source Book Chunks

For direct chunk access, use Grokking Algorithms Contents.

Deferred Topics From the Book

These source chunks are real parts of the book, but they are intentionally outside the boundary of this module:

Deferred topic	Source chunk	Why it is deferred
Dijkstra's algorithm	Working with Dijkstra's algorithm / terminology	Weighted shortest paths deserve a later algorithm pass after BFS is stable
Greedy algorithms	8 Greedy algorithms	Better placed with later tradeoff-heavy algorithm work
Dynamic programming	9 Dynamic programming part 1	Too heavy for orientation; belongs in a later problem-solving phase
K-nearest neighbors	10 K-nearest neighbors	Useful, but not required for Semester 0 algorithm intuition
Trees and advanced survey topics	11 Trees	Better after foundational data-structure reasoning is in place

If you want to study those topics later, treat this table as a forward map rather than a missing-content list.

Scope Note​

Read Only If Stuck​

Optional Deep Dive​

Source-Role Clarity​

Concept-to-Source Map​

Direct Chunk Shortcuts By Need​

Source Book Chunks​

Deferred Topics From the Book​