Skip to main content

Algorithms And Data Structures

Algorithms and data structures are the basic ideas that help a developer understand why code works, why it becomes slow, and how to choose a better solution.

The goal is not to solve abstract puzzles for their own sake. The goal is to recognize common problem shapes in real product work and make reasonable engineering decisions.

How To Think​

It is important not only to remember definitions, but to understand:

  • what data needs to be stored
  • how often the data changes
  • how often the data is searched or transformed
  • how the solution behaves when the input grows
  • what tradeoff is acceptable for the current product task

Core Topics​

Algorithms​

Algorithms are repeatable steps for solving a problem.

Examples:

  • search
  • sorting
  • filtering
  • grouping
  • traversal
  • validation

Data Structures​

Data structures define how data is organized and accessed.

Examples:

  • array / list
  • map / dictionary
  • set
  • queue
  • stack
  • tree
  • graph

Complexity​

Complexity helps estimate how a solution grows when there is more data.

Examples:

  • constant time
  • linear growth
  • nested loops
  • memory usage
  • expensive repeated work

Practice​

Basic Level​

  • Search in a list
    Find an item by id, name, or status and handle the case when nothing is found.

  • Group data
    Group a list of items by category, date, owner, or status.

  • Remove duplicates
    Clean a list while keeping predictable order.

Advanced Level​

  • Optimize repeated work
    Find a place where the same calculation runs too often and cache or restructure it.

  • Choose a data structure
    Replace a slow list search with a map or set when lookup speed matters.

  • Process a tree
    Work with nested data such as comments, categories, menus, or routes.

Reflection Questions​

  • How do you usually decide whether a simple loop is enough?
  • When does a list stop being convenient and a map or set become better?
  • What signs tell you that code is doing the same work too many times?
  • How do you check whether a solution will still work with much more data?
  • When is it better to keep a slower but simpler solution?
  • How do you explain an algorithmic tradeoff to another developer?