What Should I Know? Give Me a Checklist!

The biggest challenge with technical interviews is that almost anything under the sun can fall under the term "technical". If we're specifically talking about software engineering jobs, then the scope shrinks considerably, but the world of programming is still huge.

Here's an attempt at all the topics to cover for technical interviews. Be sure to go to the previous section for a guide on how to study these topics.

These lists are prioritized from most important to least important. Ultimately, it's a pretty minor subset of all the algorithms and data structures we know of. The great thing is that this is pretty fixed over time, as the academic underpinnings of Computer Science don't change too much.

Big O notation

You must get this part, as it forms the foundation for understanding algorithmic performance and how to choose a data structure.

At least the very least, know the underlying theory and why it's important (hint: scaling, good decision making, trade-offs, etc.)-- a good starting point is this guide.

The point here of this is that interviewers want to know you can avoid poor-performing code. Imagine if Facebook took an hour to find your friends, or if Google took a day to display search results..

Data structures

• Hashtables - Arguably the single most important data structure known to mankind. Make sure you can implement one from scratch.
• Stacks / Queues are essential, know what FILO and FIFO are.
• Linked Lists - Know about singly linked lists, doubly linked lists, circular.
• Trees - Get to know basic tree/node construction, traversal and manipulation algorithms. Learn about the subsets-- binary trees, n-ary trees, and trie-trees. Lower-level or senior programmers should know about balanced binary trees and their implementation.
• Graphs - Get to know all implementations (objects and pointers, matrix, and adjacency list) and their pros and cons.

Algorithms

• Sorting - get to know the details of at least two n*log(n) sorting algorithm, I recommend Quicksort and Mergesort.
• Binary Search - it's surprisingly common to end using some form of binary search in a final problem solution. Check our guide out for a lovely introduction.
• Tree/Graph traversal algorithms: Breadth-first Search and Depth-first Search are musts. Also know inorder, postorder, preorder.
• Advanced - for the most part optional, but if you wanted to go beyond the basics, I'd recommend Dijkstra, A*, Traveling Salesman, Knapsack Problem.

Math (rare)

• Basic discrete math (logic, set theory, etc.)
• Counting problems
• Probability (permutations vs. combinations)

Programming Languages

• Know the ins-and-outs of your language (I'm fond of JS). The language you choose should be one that you have mastered, or know best. The interview is not the time to be figuring out how to write a for-loop or what is truthy.
• Don't worry about choice of language. There is some controversy around this point-- if you're going for a Frontend role, perhaps Javascript is a better language than Python (and vice-versa for a backend role). But for the most part, the interviewer is conducting the interview because they are looking for your algorithmic reasoning skills and thought process. I've had interviewers not know the ins-and-outs of ES6 JS, and I just kindly explained parts that were unintuitive to them (like why you don't need return statements in certain arrow functions).
• Helps a lot to know one of Python, C++, Java

OOP Design

• Polymorphism
• Abstraction
• Encapsulation/Inheritance

Systems Design

This tutorial to systems design for new engineers is a great starting point on how best to practice for such interviews.

• Scoping/User Cases/Constraints
• Component Design
• OOP Design
• Database Schema Design
• Vertical Scaling
• Horizontal Scaling
• Caching
• Load Balancing
• Database Replication
• Database Partitioning
• Map-Reduce
• Microservices
• Concurrency
• Networking
• Abstraction
• Estimation

Note: Systems Design is a huge interview topic in itself. I highly recommend the System Design Cheatsheet and reading Designing Data-Intensive Applications.