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Data Structures and Algorithms Practice

A tracked route back to fluent data structures and algorithms: a problem log, complexity analysis, each core structure built from scratch, graphs, trees and dynamic programming by pattern, spaced review and timed checkpoints.

Free · Mac (Apple silicon) and Windows · v1.0.0 · Other platforms

Then open the Marketplace in the app, search for Data Structures and Algorithms Practice, and click Install. It goes into your own files, and works offline from then on.

What it installs.

1

Pillar

1

Area

50

Projects

195

Starter tasks

🏅 Tech & Engineering Certifications

Data Structures and Algorithms Practice

Rebuilding data structures and algorithms knowledge for interviews: arrays, graphs, trees, dynamic programming and complexity analysis, tracked problem by problem.

  1. Problem log with tags, outcomes and review dates
  2. Topic baseline across the core syllabus
  3. Big-O and complexity analysis refresher
  4. One reference text and one problem bank
  5. Tested implementation repository
  6. Study timetable sized to real hours
  7. Standard library containers in your language
  8. Twenty-problem diagnostic set
  9. Depth target and finish date
  10. Spaced review of solved problems
  11. Weekly topic block rotation
  12. One problem a day habit
  13. Monthly mixed-topic checkpoint
  14. Weekly error review and mistake taxonomy
  15. Weekly contest and upsolving
  16. Monthly progress report from the log
  17. Quarterly re-baseline and diagnostic repeat
  18. Flashcards for invariants and templates
  19. Arrays, strings, two pointers and sliding windows
  20. Hashing, counting and lookup problems
  21. Linked lists built from scratch
  22. Stacks, queues and monotonic stacks
  23. Binary search, including search on the answer
  24. Sorting algorithms implemented and compared
  25. Binary trees and binary search trees
  26. Heaps and priority queues
  27. Graph representation, BFS and DFS
  28. Shortest paths and minimum spanning trees
  29. Union-find and topological ordering
  30. Recursion and backtracking
  31. Dynamic programming by pattern
  32. Greedy algorithms and interval problems
  33. Bit manipulation essentials
  34. Rewriting your slowest accepted solutions
  35. Stress testing against a brute force
  36. Proving correctness with loop invariants
  37. Weak topic remediation plan
  38. Curated problem list or topic-first practice
  39. Timed graphs and trees exam
  40. First rated contest result
  41. Study group problem marathon day
  42. Final two-week refresh before the date
  43. Student plan alongside an algorithms module
  44. Working engineer plan in 45-minute slots
  45. Career changer route without a CS degree
  46. Practice plan for parents with short windows
  47. Running a weekly peer problem group
  48. Segment trees and Fenwick trees
  49. String algorithms and tries
  50. Advanced graphs, flows and strongly connected components

Where it starts.

Each project arrives with its purpose, milestones and first tasks written out. Here is how the first three read.

Problem log with tags, outcomes and review dates

Solving two hundred problems and remembering forty of them is the usual result of practising without records. A single log, one row per attempt, with the topic, pattern, time taken, whether you needed a hint and when to look at it again, turns scattered practice into evidence you can sort and review. Every other project in this area reads from it.

First tasks

  • Create the problem log with the ten columns listed in the milestones
  • Write the four outcome codes at the top of the sheet
  • Enter the last ten problems you can remember solving
  • Build a view filtered to rows due for review today or earlier
  • Export a backup copy of the problem log @recurring(monthly:3)

Topic baseline across the core syllabus

Before choosing what to study, rate yourself honestly on the fifteen or so topics that make up a standard algorithms syllabus, from arrays and hashing to graphs and dynamic programming. A two-hour baseline, one easy and one medium problem per weak-looking topic, replaces vague unease with a ranked list of gaps. Repeat it each quarter and the change is visible.

First tasks

  • Copy the fifteen core topics into a skills matrix
  • Rate recall, implementation and problem solving for each topic
  • Attempt one easy problem in every topic you rated 2 or below
  • Mark the three weakest topics as first priorities

Big-O and complexity analysis refresher

Stating complexity correctly is half the credit on most algorithm questions, and it is the skill people bluff most often. Working through time and space analysis for loops, recursion, amortised operations and the common recurrences gives you a method instead of a guess. It also makes every later topic faster, because you can tell early whether an approach is fast enough.

First tasks

  • Write the definitions of Big-O, Big-Omega and Big-Theta from memory
  • Analyse twenty short snippets for time and space, then check each
  • Apply the master theorem to three recurrences
  • Work out the amortised cost of dynamic array appends by hand
  • Draw up the input size against affordable complexity table

Make it yours.

Install Data Structures and Algorithms Practice, then tell Aurum the date you're aiming for. It checks whether the plan fits the weeks you actually have.

Free · Mac (Apple silicon) and Windows · v1.0.0 · Other platforms

Blueprints are general information to help you organise your own work. They are not medical, financial, legal or other professional advice. See the Terms.