Skip to content

README.md

Latest commit

 

History

History
86 lines (64 loc) · 4.04 KB

README.md

File metadata and controls

86 lines (64 loc) · 4.04 KB
Raw

Data-algo-python

Useful data structure study and algorithms implemented with python, practing for technical interviews, live coding and leet problems Also, leet code preparation road map, around 8-12 weeks long.

  • Two-pointer technique
  • Sliding window
  • Fast and slow pointers
  • Tree traversal
  • Depth-first search (DFS)
  • Binary search
  • Dynamic programming

Phase 1: Foundations (1-2 weeks)

Goal: Familiarize yourself with common data structures and algorithms, and solve easy-level questions to build confidence.

1- Review Core Concepts:

  • Arrays & Strings: Basics of iteration, manipulation, and string operations.
  • Hash Maps & Sets: Learn how to solve problems using these for faster lookups.
  • Sorting: Learn basic sorting algorithms and when to use them.
  • Time Complexity: Understand Big O notation and analyze solutions.

2- Practice Topics:

  • Arrays & Strings: Easy questions (e.g., Two Sum, Best Time to Buy and Sell Stock)
  • Hash Maps: Practice frequent problems (e.g., Contains Duplicate, Valid Anagram)
  • Sorting: Learn and apply basic algorithms like quicksort or mergesort.
  • Goal: Solve at least 10-15 easy questions per topic.

Phase 2: Intermediate Mastery (2-3 weeks)

Goal: Move to intermediate-level questions focusing on the most common algorithms asked in interviews.

1- Core Topics to Focus On:

  • Binary Search: Learn how to apply it in sorted data (e.g., Search Insert Position).
  • Linked Lists: Practice operations on singly and doubly linked lists (e.g., Reverse Linked List).
  • Stacks & Queues: Use these data structures for questions involving sequence (e.g., Valid Parentheses, Min Stack).
  • Binary Trees & BST: Familiarize yourself with traversals, depth-first and breadth-first searches.
  • Recursion & Backtracking: Solve problems like permutations and combinations (e.g., Subsets, Combination Sum).

2- Practice Strategy:

  • Start by learning the theory, then solve 5-10 questions on each topic.
  • Review solutions and understand different approaches (e.g., iterative vs. recursive).

Phase 3: Advanced Topics (3-4 weeks)

Goal: Focus on harder and more complex problem-solving skills, including optimization.

1- Key Advanced Topics:

  • Dynamic Programming: Solve questions that involve memoization (e.g., Climbing Stairs, Coin Change).
  • Graphs: Practice BFS/DFS and shortest path algorithms (e.g., Course Schedule, Number of Islands).
  • Heap & Priority Queues: Understand heap operations and use them in questions (e.g., Kth Largest Element).
  • Advanced Trees & Tries: Practice problems involving more complex tree structures.
  • Greedy Algorithms: Solve questions where locally optimal choices lead to the global optimum (e.g., Jump Game, Gas Station).

2- Practice Strategy:

  • Spend extra time reviewing dynamic programming problems, as these are common in interviews.
  • Solve at least 5-10 questions per advanced topic.

Phase 4: Mock Interviews & Timed Practice (2-3 weeks)

Goal: Simulate interview conditions by solving problems in a timed environment.

1- Simulate Interviews:

  • Pick random medium and hard problems and try to solve them within 30-45 minutes.
  • Focus on verbalizing your thought process, as you would in an actual interview.
  • Use mock interview platforms or ask a friend to conduct mock interviews with you.

2- Topic Revision:

  • Target Weak Areas: Revisit the questions or topics where you struggled the most.
  • Mixed Practice: Do random LeetCode questions to simulate real interview randomness.
  • LeetCode Contests: Participate in LeetCode Weekly Contests for more variety and pressure handling.

Phase 5: Final Week (1 week)

Goal: Strengthen your confidence and optimize speed before the interview.

1- Daily Problem Solving:

  • Solve a mixture of easy, medium, and hard problems.
  • Focus on efficiency and correctness.
  • Do some pair programming to get used to coding with someone watching.

2- System Design & Behavioral Practice:

  • If applying for senior roles, start reviewing system design concepts.
  • Also, practice answering behavioral questions and explaining the thought process for algorithmic problems.

DSA and leer code peoblems