Checklist

Data Structures study checklist

A data structures checklist for CS students implementing lists, trees, and graphs while taming debugging and runtime analysis.

Built for Data Structures · CS students implementing lists, trees, and graphs.

Progress

0 of 13 tasks complete

Master linear structures

Lists, stacks, and queues first.

Implement a dynamic array and linked listCode both from scratch and compare insertion and access costs.~60 minBuild stacks and queuesImplement with both arrays and linked lists; note tradeoffs.~45 minAnalyze operation complexityWrite the Big-O of each operation and verify with reasoning, not guessing.~40 min

Build hierarchical and hash structures

Trees and hash tables.

Implement a binary search treeCode insert, search, and delete; trace deletion of a two-child node carefully.~60 minStudy balanced trees and heapsUnderstand why balancing matters for worst-case height.~50 minImplement a hash tableHandle collisions with chaining or open addressing and analyze load factor.~55 minTrace operations on paperHand-trace a tree rotation or rehash to confirm the mental model.~40 min

Work with graphs

Representation and traversal.

Represent graphs two waysAdjacency list and matrix; compare space and lookup cost.~45 minImplement BFS and DFSCode both traversals and use a visited set to avoid infinite loops.~55 minDebug with print and assertionsAdd invariant checks so structure bugs surface early.~40 min

Debug and self-test

Make correctness reliable.

Write unit tests for each structureCover empty, single-element, and boundary cases that hide bugs.~50 minKeep a bug logRecord each defect, its root cause, and the fix for pattern recognition.~30 minRe-derive complexity from codeRead your implementation and state Big-O without looking it up.~35 min

Common mistakes

Guessing Big-O from memory instead of deriving it from the code.
Skipping the two-child deletion case in a BST and shipping a subtle bug.
Forgetting the visited set in graph traversal, causing infinite loops.
Testing only the happy path and missing empty or single-element edge cases.
Debugging by random edits instead of adding assertions to check invariants.

Pro tips

Derive Big-O directly from your loops and recursion, not from memory.
Hand-trace tree rotations and rehashing before trusting the code.
Always use a visited set in graph traversal to prevent cycles from looping.
Write tests for empty, single-element, and boundary inputs first.
Keep a bug log mapping each defect to its root cause to spot patterns.

FAQ

How should I start the Data Structures study checklist?

Start with the first phase, then run one timed Study Spaces sprint before adding more tasks. The goal is execution, not a perfect plan.

What should I do if I fall behind?

Copy the remaining tasks, pick the highest-score or highest-deadline item, and restart with one focused block.

How often should I review progress?

Review after each sprint and once at the end of the week so the next session starts with a clear first task.

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Turn this page into a live sprint

Start the matching room for Data Structures, then use the sprint plan as the first task and recap script.

Data Structures study checklist
Focus target: Data Structures
Block 1 (25 min): closed-book recall or one timed practice set.
Break (5 min): mark confusing items without opening a new task.
Block 2 (25 min): correct misses and write the next first step.
Done: one score/error note plus one queued task for tomorrow.

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