Learn in Public: Data Structures & Algorithms in Python

I am currently working on becoming more proficient in Data Structures and Algorithms - both for the sake of interviewing and to become a better engineer.

In the following weeks and months, I'll share with you what I've learned along my journey. The content will include:

• The theory and application of Data Structures & Algorithms.
• My interviewing experience, tips, and tricks.
• Fun problems you can solve using what we've learned.

❗️Please share any additional resources that you'd recommend, and topics that I forgot to add in the comments below!

Learning Method

I love learning, but I am not an efficient student. I am great at "consuming" material, like taking courses and going through tutorials. But if you ask me to explain what I've learned, I wouldn't be able to do so. I would say "it just slips my mind", but truth be told I never do the work to commit the material into my long term memory.

This time it is going to be different, and I am going to utilize the following two strategies to improve my retention of what I've learned:

• Use scientific methods for efficient learning.
• Following the idea of learning in public, I am going to summarize and share what I'm learning along the way. Learning by teaching makes sure you truly understand the material.

List of Resources

For now, let's start with a summary of resources and a list of topics we'll cover in the following weeks and months.

This is not an exhaustive list of all the resources that are available on the WWW, but rather a list of courses I find intriguing, and the books I own.

Legend to indicate where I'm at:
❗️: Currently working on
✅: Done
💤: Done, but needs a refresher

This is a living list, meaning I'll be coming back and updating it as I work through the material.

Data Structure & Algorithm Courses

• Coursera
  • Algorithms Specialization, Stanford
  • Data Structures & Algorithms Specialization, University of California San Diego
  • Algorithms, Part I, Princeton University 💤
  • Algorithms, Part II, Princeton University
• Udacity
  • Data Structures & Algorithms Nanodegree ✅

Books

• Theory
  • Introduction to Algorithms
  • Grokking Algorithms: An illustrated guide for programmers and other curious people
• Practice Problems
  • Cracking the Coding Interview
  • Daily Coding Problem
  • Elements of Programming Interviews
  • Programming Interviews Exposed

Websites

• CodeSignal ❗️
• Pramp, these are actual mock interviews with real people. You get to experience both sides: Being the interviewee as well as interviewing your peers.

Content

• Analyzing Algorithms
  • Asymptotic Notation
  • Amortized Analysis
  • Probabilistic Analysis & Randomized Algorithms
• Data Structures
  • Arrays & Linked Lists ❗️
  • Stacks & Queues
  • Hash Tables
  • Trees
  • Binary Search Trees
  • Red-Black Trees
  • B-Trees
  • Binomial Trees & Binomial Heaps
  • Fibonacci Heaps
  • Data Structures for Disjoint Sets
• Recursion
• Sorting & Order Algorithms
  • Heapsort
  • Quicksort
  • Sorting in Linear Time
• Graph Algorithms
  • Breadth-first search (BFS)
  • Depth-first search (DFS)
  • Topological Sort
  • Minimum Spanning Trees
  • Single-Source Shortest Paths
  • All-Pairs Shortest Paths
  • Maximum Flow
• Dynamic Programming
• Greedy Algorithms
• Linear Programming
• Sorting Networks
• Matrix Operations
• Polynomials & the FFT
• Number-Theoretic Algorithms
• String Matching
• Computational Geometry
• NP-Completeness
• Approximation Algorithms
• Maps & Hashing
• Tries
• Heaps
• Divide & Conquer
• A*