Algorithms and Data Structures

The basic ideas that explain why code works, why it becomes slow, and how to pick a better solution. Not competitive-programming puzzles — the shapes that show up in ordinary product work: searching a list, grouping records, walking a tree of categories, deduplicating what came back from an API.

Why it matters. Code that is fine with fifty items and unusable with fifty thousand is the most common performance bug in product development, and it never appears in testing. It appears on the largest customer.

What to understand

• What data is stored, and how often it changes versus how often it is read
• What the code does repeatedly, and whether it needs to
• How the solution behaves when the input grows tenfold
• Which tradeoff is acceptable for this task — speed, memory, or simplicity
• When a slower but obvious solution is the right one

Core topics

Algorithms

Repeatable steps for solving a problem.

• Searching and filtering
• Sorting and ordering
• Grouping and aggregation
• Traversal of nested structures
• Deduplication

Data structures

How data is organised determines what is cheap and what is expensive.

• List — ordered, cheap to append, expensive to search
• Map — near-instant lookup by key
• Set — membership and uniqueness
• Queue and stack — order of processing
• Tree and graph — nested and connected data

Complexity

A way to talk about growth without measuring.

• Constant, linear, and quadratic growth
• Nested loops over the same data
• Repeated work that could be computed once
• Memory as a cost, not only time

Levels

Level	What it looks like
Junior	Uses lists and maps correctly. Can write a search or a grouping loop that works.
Middle	Notices a nested loop over a growing collection and reaches for a map or set instead. Can explain why a screen got slow.
Senior	Chooses structures from the access pattern before writing code. Knows when to leave a slow solution alone because the data is bounded.

Practice

Starting out

• Find one item Look an item up by id, name, or status, and handle "not found" properly.

• Group a list Group records by category, date, or owner, and render the groups.

• Remove duplicates Clean a list while keeping a predictable order.

Going deeper

• Kill repeated work Find a calculation running on every rebuild and compute it once instead.

• Change the structure Replace a linear search inside a loop with a map lookup, and measure both.

• Walk a tree Process nested data — comments, categories, a menu, a route table — without recursion getting away from you.

Check yourself

• How do you decide a simple loop is good enough?
• When does a list stop being convenient and a map or set become better?
• What tells you code is doing the same work more than once?
• How do you check a solution still holds with a hundred times more data?
• When is a slower but simpler solution the right call?
• How do you explain an algorithmic tradeoff to someone who does not want to hear it?

Resources

• Grokking Algorithms — the one book to read if this material never stuck. Illustrated, short, and aimed at working developers rather than exam candidates.
• Big-O Cheat Sheet — the complexity of every common structure and sorting algorithm on one page. Worth keeping open while you decide.
• dart:collection — what Dart actually gives you beyond List and Map: linked lists, queues, splay trees, unmodifiable views.
• Iterable collections codelab — the official walkthrough of Dart's collection operations, including the lazy ones that quietly avoid a second pass. Runnable in the browser.
• Data Structure Visualizations — step-by-step animations from the University of San Francisco. The fastest way to build intuition for how a tree or a hash table behaves.