interview study sheet

A quick study sheet I use as a refresher 😄

• Data Structures
• Algorithms
• Other Concepts
• Math
• Common Problems
• Just Python Things
• Java Cheatsheet
• Programming Languages
• Problem-solving Strategies

Also, there's much more to computer science than these simple topics! There are a multitude of online resources for broadening and deepening your core CS knowledge; https://teachyourselfcs.com/ is one such site.

Data Structures

Array

• An array is a collection with specified size
  • Dynamic array: some languages' implementations automatically expand as you add elements
• Access elements directly by index
• Time complexity:
  • Access by index: O(1)
  • Search by value: O(n)
  • Insert: O(n) (need to shift values)
  • Delete: O(n) (need to shift values)

Linked List

• A linked list is a collection of nodes where each node has a value and a reference
• Singly linked list: nodes have pointers to the next node
• Doubly linked list: nodes have pointers to next and previous nodes
• Time complexity:
  • Access by index: O(n)
  • Search by value: O(n)
  • Insert: O(1)
  • Delete: O(1)

Stacks & Queues

• Stack: last in, first out (LIFO)
  • Adding an element and popping the most recently added element are O(1) operations
• Queue: first in, first out (FIFO)
  • Adding an element and popping the oldest element are O(1) operations
• Double-ended queue: stack + queue combined
• push adds elements & pop extracts elements

Trees

• A tree is an undirected, connected, acyclic graph
  • Has v vertices and v-1 edges
  • Any two vertices are connected by a unique path
  • A leaf is a vertex of degree 1
  • One node is designated as the root
  • Each node has parent and/or children pointers
  • A node's height is the length of its path to the root
• A forest has multiple distinct trees (a disjoint union)
• An n-ary tree has at most n children per node

Binary Tree

• A binary tree has nodes with at most 2 children (designated left & right)
• Full: every node has 0 or 2 children
  • Number of nodes is at most 2^(h+1)-1
• Complete: every level, except possibly the last, is filled, and the last level's nodes are as far left as possible
  • Number of internal nodes: floor(n/2)
• Balanced: has the minimum possible maximum depth
  • Height is ceil(lg(n+1))
• Traversals:
  • Pre-order: open current, visit left subtree, visit right subtree
  • In-order: visit left subtree, open current, visit right subtree (returns sorted list)
  • Post-order: visit left subtree, visit right subtree, open current
  • Level-order: breadth-first traversal, level by level

Binary Search Tree

• A binary search tree is an ordered binary tree
• Satisfies the BST property: each node's value is greater than all keys stored in the left subtree and less than all keys stored in the right subtree
• Designed to make searching faster--each comparison allows operations to skip about half the tree
• Search: recursively search subtrees; takes O(h)
• Insertion: like search, but insert node when a leaf is reached; takes O(h)
• Deletion: more complicated; takes O(h)
  • If deleting a node with no children, just do it
  • If deleting a node with a single child, replace the node with its subtree
  • If deleting a node with two children, swap with minimum value in right subtree or maximum value in left subtree, then delete the node (which should now be a leaf)

AVL Tree

• An AVL tree is a self-balancing binary search tree
• Pairs of subtrees differ in height by at most 1
• Lookup, insertion, and deletion all take O(log n), since height is at most O(log n)
• Rotation balances the tree on update
• Implement by adding a balance factor on each node (difference between subtree heights)

Trie

• A trie is a special tree that stores subsequences of values, also known as a prefix tree
• Each node's descendants share a common prefix given by the node
• Useful for autocomplete

Hashing

• A hash function is a function mapping an object to an integer such that if a==b, H(a)==H(b)
• Universal hashing: a randomized way of drawing a hash function from some set of functions so that performance is good in expectation
• Perfect hashing: has no collisions; usually only practical when the set of keys is roughly constant

Hash Tables

• A hash table is an array whose indices correspond to results from a hash function (implemented as a dictionary in Python)
• Provides O(1) lookup, assuming load factor is small enough
• Load factor: n/k, where n is number of entries and k is number of buckets
• Collision resolution
  • Chaining (e.g. with linked lists)
  • Open addressing (e.g. with linear probing, quadratic probing, or double hashing)
• Table doubling: choose a new hash function to map to the new size and insert elements from old table into new table
• Simple uniform hashing assumption (SUHA): a hash function maps to any slot with equal probability

Heap

• A heap is a special tree where nodes have higher (in the case of a min-heap) values than their parents
• Binary heap:
  • Heapify in O(n)
  • Find min in O(1)
  • Extract min, increase key, insert, delete in O(log n)
  • Can implement as a list where a node at index i has children at 2i+1 and 2i+2 (0-indexed)

Graph

• A graph is a collection of nodes and edges and can be directed or undirected
• Cycle: path that loops onto itself
• Topological sort: linear ordering of vertices such that directional constraints are preserved in a directed acyclic graph (DAG)
  • Generate using DFS by prepending to output list
• Spanning tree: a tree that includes all nodes in the graph
  • Minimum spanning tree: a spanning tree with minimum total edge weights
• Complete graph: fully connected; every pair of nodes has an edge
• Bipartite graph: split into two groups A and B where there are no edges within each groups
• Clique: a complete subgraph

Algorithms

Binary Search

• Given a sorted list, start at the midpoint and divide and conquer
• Exponential search is like binary search but in one direction (e.g. can be used in infinite sequence)
• O(log n)

Sorting

Insertion

• Maintain a sorted sublist and insert new elements in it appropriately
• Sorts in-place; stable
• Best-case O(n), average O(n^2), worst O(n^2)

Bubble

• On each pass through the array, compare adjacent pairs of elements and swap if necessary
• Sorts in-place; stable
• Best-case O(n), average O(n^2), worst O(n^2)

Selection

• Exchange current element with smallest element to the right of the current element
• Sorts in-place; unstable
• Best-case O(n^2), average O(n^2), worst O(n^2)

Merge

• Recursively divide until sublists are size 1, then recursively merge the sublists
• Requires O(n) space; stable
• Best-case O(n log n), average O(n log n), worst O(n log n)

Quick

• Set some pivot element in the array; move elements smaller than pivot to its left and elements larger to the right
  • Recursively sort left and right sublists
• Requires O(log n) space; stable
• Best-case O(n log n), average O(n log n), worst O(n^2)

Counting/Bucket

• For lists whose elements' values are in a bounded, constant range
• Not a comparison sort so best & average is O(n+k) and worst is O(n^2) (not bounded to O(n log n))
• Iterate through list and place items in buckets; can be stable

Radix

• Apply a stable counting sort to every place value in a number
• Sort places from least to most significant
• Requires O(n+k) space; O(d(n+k)) time
• Also not a comparison sort

Graph Search

• Given a graph, find a path from a start node to an end node
• General strategy: expand a node, check to see if it's the goal node, add its children to the search agenda
• In the case of weighted graphs, a heuristic may help find the shortest path faster
  • Admissible: heuristic's value for a node is less than actual distance from node to goal (H(n,G) ≤ dist(n,G) for all nodes n)
  • Consistent: heuristic follows triangle inequality (|H(A)-H(B)| ≤ dist(A,B) for all nodes A,B)

Depth-first

• Implement with a stack (add new paths to the front of the agenda)
• Can use for cycle detection

Breadth-first

• Implement with a queue (add new paths to the end of the agenda)
• In an unweighted graph, guaranteed to find shortest path

Hill-climbing

• Add new paths to the front of the agenda
• Sort new paths by terminal node's heuristic

Best-first

• Add new paths to the front of the agenda
• Sort all paths in agenda by terminal node's heuristic

Branch and bound

• Add new paths to the front of the agenda
• Sort agenda by path length so far
• Can also add a heuristic or extended set (or both)

A*

• Branch and bound with heuristic and extended set
• Heuristic must be consistent

Dijkstra's

• Find shortest path between two nodes (branch and bound with extended set and without heuristic)
• Can't handle negative edge weights
• Using a Fibonacci heap, runtime is O(|E|+|V|log|V|)

Bellman-Ford

• Compute shortest paths from a single source to all other nodes in the graph
• Can handle negative edge weights & detect negative-weight cycles
• Worst-case runtime is O(|V||E|)

Floyd-Warshall

• Dynamic programming all-pairs shortest paths algorithm
• dp(i,j,k+1)=min(dp(i,j,k),dp(i,k+1,k)+dp(k+1,j,k))

Other Graph Algorithms

Min Cut & Max Flow

• The min cut problem asks for the minimum number of edges you can remove from a graph to disconnect a given source and sink
• The max flow problem asks for the maximum flow from a given source to sink
• Karger's randomized min-cut algorithm
• Ford-Fulkerson computes max flow
• Example of linear duality

Minimum Spanning Tree

• Prim's adds the smallest-weight connected edge that doesn't create a cycle
  • O(|E|+|V|log|V|) so use in dense graphs
• Kruskal's adds the globally smallest edge and keeps a forest (
  • O(|E|log|V|)

Greedy Algorithms

• Locally optimal choices lead to globally optimal solution
• Be careful--this is usually rare!
• Prim's, Kruskal's, interval scheduling, Huffman codes, Dijkstra's

Dynamic Programming

• A general method for solving a problem with optimal substructure by breaking it down into overlapping subproblems
• Top-down: memoize (store) solutions to subproblems and solve problem recursively
• Bottom-up: build up subproblems from base case up and avoid recursive overhead
  • Order subproblems by topologically sorting DAG of dependencies
• Knapsack problem, longest common subsequence, coin change, edit distance, minimum number of jumps, longest palindrome substring, balanced partition

Other Concepts

General

• Static/dynamic checking
• Strongly/weakly typed
• Compiled/interpreted
• Shallow/deep copying
• Immutable/mutable
• Defensive copying
• Pseudo-polynomial runtime

Asymptotic Notation

• Look here for formal definitions
• O - asymptotic upper bound
  • o - asymptotic upper bound, excluding same rate
• Ω - asymptotic lower bound
  • ω - asymptotic lower bound, excluding same rate
• Θ - same asymptotic growth
• Exponential > polynomial > logarithmic > constant
• Can ask for worst, best, or average case

Object-oriented Programming

Inspiration from here

• Abstract data type: defined logically by set of values and set of operations
• Class: basic concept in OOP, bundles data type information with actions
• Object: runtime value which belongs to a class
• Encapsulation: information hiding to ensure data integrity
• Hierarchy: classes can have super- and subclasses
• Inheritance: a subclass inherits data and methods from its parent classes
• Overriding: a subclass inherits parent methods but may override them
• Polymorphism: different classes in a program can respond to the same message in different ways; useful when an object's class can't be determined at compile time
• Identity: checks whether two objects are the same location in memory
• Equality: checks whether two objects are behaviorally equivalent

Concurrency

• Starting with a single-threaded program, threads can spawn new threads
• Data races: bugs in concurrent programs resulting from concurrent access to shared objects
• Ways to prevent data races: protect objects with locks so that only one thread can access an object at once, or use a special hyperobject

Design Patterns

• Model-view-controller: model stores data, controller updates model, view generates user interface
• Factory method: use a factory object to create other objects rather than using a constructor
• Singleton: restrict instantiation of a class to a single object
• Observer: subjects notify observers of any state changes (usually by calling their methods); used in MVC
• Lots more

The Internet

HTTP Methods

• GET: used to retrieve data, no other effect on the data
• POST: used to send data to the server (e.g. form)
• PUT: replaces current representation of resource (idempotent)
• DELETE: remove current representation resource

HTTP Status Codes

• 200 OK: success
• 400 Bad Request: syntax could not be understood
• 401 Unauthorized: request not fulfilled due to lack of authorization
• 403 Forbidden: request understood but not fulfilled, authorization will not help
• 404 Not Found: URI could not be matched
• 408 Request Timeout: server did not receive a timely response from client
• 418 I'm a teapot: the resulting entity body may be short and stout
• 500 Internal Server Error: server exception
• 503 Service Unavailable: server unable to handle the request (temporary)
• 504 Gateway Timeout: server did not receive a timely response from upstream server

Networking

• OSI Model

Recursion

• Master theorem: is most work performed in the root node, in the leaves, or evenly distributed in the rows of the recursion tree?

Terminal Commands

• Basic commands: ls, cd, mkdir,touch, cp, mv, rm, pwd, chmod, chown, man
• ping: ping a server, used for network diagnostics
• ps: display info about processes running on the system
• grep: searches through files for lines matching a given regular expression
• tar, zip, unzip: make and open compressed archives
• curl: send requests to web servers
• wget: download files from the web (can do recursively)
• dig: query over DNS
• crontab: use Cron to schedule recurring tasks

Git

• init: creates/initializes .git folder in current directory
• clone: clone repo into new directory
• pull: fetch from another repo and integrate
  • git pull is same as git fetch then git merge FETCH_HEAD
• add: add files to index of contents for next commit
• rm: remove files from working tree and index
• commit: record changes to the repo, along with a commit message
• rebase: transplant changes on one branch to another, edit commit history
• branch: list, create, or delete branches
• checkout: switch branches (or just get a version of specific files)
• status: show the working tree's status
• diff: show changes between commits or the working tree
• log: show commit logs in a repo
• remote: manage tracked remote repos
• reset: reset current HEAD to a different state (can do --hard or --soft)
• Also cool things like bisect, fixup

Math

Combinatorics

• n(n-1)/2: number of handshakes in a group
• n-1: number of rounds in a knockout tournament
• 2^k: number of binary strings of length k
• n!/(n-k)!: permutations of n items taken k at a time
• n!/(k!(n-k)!): combinations of n items taken k at a time

Probability

• Bayes' theorem: P(A|B) = P(B|A)P(A)/P(B)

Common Problems

Lots of these taken from this blog.

• Fibonacci sequence: print the nth Fibonacci number

  • Optimally, do this recursively and cache the subproblem solutions

• Array pair sums: given an array, output pairs which sum to a number k

  • Can do in O(n) with a set data structure. For each element in the array, check to see if k-a[i] is in the set, then add the element to a set.

• Reverse a linked list: reverse a singly linked list

  • Track previous and current nodes; iterate through list and swap the direction of pointers. Time is O(n) and space is O(1).

• Matrix region sum: given multiple rectangular regions in a matrix, compute the sum of numbers in that region

  • Memoize sums of regions with the constraint that corners are at m[0][0]

• Word permutation: find all permutations of a word

  ```python
  def permute(word):
      if len(word) == 1:
          return {word}
      else:
          result = set()
          permutations = permute(word[:-1])
          letter = word[-1]
          for p in permutations:
              result.update([p[0:i]+letter+p[i:] for i in range(0,len(word)+1)])
          return result
  ```
  

• Median of number stream: given a continuous stream of numbers, find the median of numbers so far at any time

  • Optimally, keep a max-heap of the smaller half of the numbers and a min-heap of the larger half of the numbers

• Infinite array search: given a sorted, infinite-length array, find a given value

  • Modify binary search to start at the array's first element and exponentially increase the index you search at. Time is O(log n)

• Anagram pair: determine if two words are anagrams

  • Comparison sort: sort the words in alphabetical order and check for equality. O(n log n), where n is word length.
  • Count letters: use a hash table to track counts of letters in both words. O(n) runtime.

• Anagram dictionary: determine which words in a list are anagrams of a given word

  • Check for the membership of every permutation of the input word in the dictionary

• Anagram list: determine which sets of words in a dictionary are anagrams

  • Abstractly, hash each word and group by word. A hash can be a 26-digit string, or you can sort each word.

• Binary search tree verification: verify whether a tree satisfies the binary search tree property

  • For each node, track its possible minimum and maximum values
  • Performing an inorder traversal should produce a sorted list

• Largest continuous sum: in an array of integers, determine the subsequence with the largest sum

  • Track maximum sum encountered so far and check whether current sum is greater. Reset current sum when it becomes negative. Time is O(n) and space is O(1).

• -1/0/1 array: given an array where values are -1, 0, or 1, sort the array

  • Bucket sort (but this takes O(n) space)
  • Iterate through the list and track pointers for min and max index. If a value is -1, swap it with the element at the min index and increment min index. If a value is 1, swap it with the element at max index and decrement max index. Time is O(n) and space is O(1).

• k-th largest element: find the kth largest element in an unsorted array

  • Modify quicksort to recursively sort on pivots in left/right subarrays (average O(n), worst-case O(n^2))
  • Median of medians algorithm

• Find missing number: given an array where every number except one appears an even number of times, find the number that appears an odd number of times

  • Optimally, bitwise XOR by numbers in the list (XORing an even number of times resets the number to its original value). Time is O(n) and space is O(1)

• Knapsack: given a set of items each with weights and values, maximize value while keeping total weight under a limit

  • Dynamic programming: say weight limit is W. Create an array m[w] where each element is the maximum value of items with a weight limit w≤W. Optimize by dividing item weights and weight limit by their greatest common divisor. Runtime O(nW).

• Balanced partition: given a set of numbers, partition them so that the sums of the partitions are as close as possible

  • Greedy method: iterate through sorted list and add items to the smaller-sum partition
  • Dynamic programming: determine if a subset of the input sums to n/2 (where n is the sum of the input numbers)

• LRU Cache: implement a least-recently used cache

  • Use two data structures: queue (implemented using doubly linked list) and hash table. Queue contains pages in access order & hash map maps pages to queue node

Just Python Things

Strings

• s.center(w,[fillchar]): returns centered string in string of width w
• s.count(sub[,start[,end]]): returns count of non-overlapping occurences of substring
• sub in s: returns True if sub is in s
• s.find(sub[,start[,end]]): returns start index of substring or -1
• s.join(iter): join items in iterable, separated by s
• s.strip([chars]): removing leading and trailing characters
• s.replace(old,new[,count]): returns copy of s with old replaced by new
• s.isalpha(): returns True if all characters in s are alphabetic
• s.isdigit(): returns True if all characters in s are digits

Lists

• l=[]: initialize
• len(l): get size
• l.append(val): append a value
• l.insert(i,val): insert a value at position
• l.extend(lst): append all values in a list
• l.pop([i]): remove an item and return it (defaults to last item)
• x in l: check membership
• l.sort(cmp=None,key=None,reverse=False): sort in place
• sorted(iterable[, cmp[, key[, reverse]]]): return a new stably sorted list
• l.reverse(): reverse a list in place
• range(start,end): get a list with items from start (inclusive) to end (exclusive)
• [<expr> for <var> in <list> if <condition>]: list comprehension
• listname[start:end:slice_size]: slicing

Sets

• set() or {l}: initialize
• len(s): get cardinality
• x in s: check membership
• s.update(other): add values of other to s
• s | other | ...: return a union of sets
• s & other & ...: return an intersection of sets
• s - other - ...: return difference of sets
• s ^ other: return set of elements uniquely in sets

Dictionaries

• d={}: initialize
• d[key] or d.get(key): get the value at key (the latter returns None if not found)
• len(d): get item count
• key in d: check membership
• d.pop(key): remove and return a value in the dictionary
• del d[key]: delete an item
• d.update(other): update/overwrite with keys & values from other
• d.items(): return a list of (key,value) tuples
• d.keys(): return a list of dicionary keys
• d.values(): return a list of dictionary values
• {<key>: <val> for <var> in <list> if <condition>}: list comprehension

Error Handling

• Common Python way to indicate error is to raise Exception (or a subclass of Exception)
• Catch these with try/except blocks

Classes

class Node(ParentClass):
  def __init__(self, val, parent):
    self.val = val
    self.parent = parent
    self.children = []
  def add_child(self, child):
    self.children.append(child)

n = Node("root", None)

• Read about Python metaclasses
• Inherit from object to use new-style classes
• Note that Python supports multiple inheritance
  • Be cautious of method resolution order (__mro__)

Non-Decimal Numbers

• Binary numbers: preface with 0b; use bin(int) to convert
  • Left and right shift: << and >>
  • Bitwise AND, OR, XOR, NOT: &, |, ^, ~
  • [Bitmasks](https://en.wikipedia.org/wiki/Mask_(computing)

File I/O

• f = open('filename', <mode>): open a file
• f.close(): close file
• f.readline(): read a line from the file
• for line in f: iterate through lines in file
• f.write(): write a string to the file

Bitwise Operators

• x << y: left shift by y bits
• x >> y: right shift by y bits
• x & y: bitwise AND
• x | y: bitwise OR
• x ^ y: bitwise XOR
• ~x: complement of x

Magic Methods

• __init__(self,[...]): initializer for a class
• __cmp__(self,other): return negative for <, 0 for ==, positive for >
• __eq__(self,other): define behavior for ==
  • Also ne, lt, le, gt, ge
• __str__(self): return string representation
• __repr__(self): return machine-readable representation
• __format__(self, formatstr): return new-style formatted string
• __hash__(self): return an integer such that a==b implies hash(a)==hash(b)
• __getitem__(self, key): defines what happens when you access self[key]
• __getattr__(self, key): defines what happens when you access self.key
• __contains__(self, item): defines behavior when using in/not in for membership checking

Useful Packages

• copy
  • copy.copy(x): return shallow copy of x
  • copy.deepcopy(x): return deep copy of x
• collections (use collections.deque)
  • dq.pop(), dq.popleft(), dq.appendleft(val), dq.extendleft(lst), dq.rotate(n)
• heapq
  • heapq.push(heap,item): add an item
  • heapq.pop(heap): pop an item
  • heapq.heapify(l): make a list into a heap in linear time
• BeautifulSoup
• scipy
• numpy
• scikit-learn
• nltk
• requests
• unirest
• networkx
• pdb
  • pdb.set_trace() sets a breakpoint at the current line and gives the user a CLI with which to inspect various objects and their values at runtime. Also allows you to continue code execution line by line.
• pprint: Pretty Print
  • pprint.pprint(iter): Print out a version of iter with JSON-like formatting. Useful for inspecting large, deeply nested objects.

List Functionals

• zip(seq1 [,seq2 [...]]): return list of tuples where each tuple contains the i-th element from each sequence. Truncated to length of shortest sequence ([(seq1[0], seq2[0] ...), (...)])
• map(f, seq): return list of the results of f applied to each element of seq ([f(seq[0]), f(seq[1]), ...])
• filter(f, seq): return list of items in seq for which f(seq[i]) == True
• reduce(f, seq): apply f to pairs of elements in seq until iterable is a single value

Other

• Infinity: float("inf")
• Simultaneous assignment: a,b = b,a to swap
• lambda x: <body>: lambda function; don't need return statement (last value is return value)
• Tuples are immutable lists; strings are also immutable
• zip(): combine multiple lists into single list of tuples
• Four numeric types: int, long, float, complex
• Logical operations: and, or, not
• is vs ==: former for object identity, latter for object equality
• Falsey values:
  • None
  • False
  • Zero of any numeric type
  • Empty sequences & mappings
  • When __nonzero__() returns False
  • When __len__() returns zero for a user-defined class
• People like the word "Pythonic"

Java Cheatsheet

Program structure

public class Program {
  // main
  public static void main(String[] args) {
    Hello h = new Hello("hi");
    System.out.println(h);
  }
}

public class Hello {
  private String text; // private instance variable
  // constructor
  public Hello(String helloText) {
    text = helloText;
  }
  public String toString() {
    return "Hello" + text;
  }
}

Data Types

• Primitive: int, double, boolean, char, byte, short, long, float
• Also Integer, Double, String classes
  • Note that char literals have single quotes and String literals have double quotes
• Arrays: use [] after type name (fixed length, length variable)
• Interfaces -> concrete classes:
  • List -> ArrayList, LinkedList (variable length, size() method, can't store primitives)
  • Set -> HashSet, LinkedHashSet, TreeSet
  • Map -> HashMap, LinkedHashMap, TreeMap
• Collection parent interface of Set, List, Queue, Deque
• Others: File, Math, Scanner, StringTokenizer
• Object class at the top of the hierarchy

Inspired by http://introcs.cs.princeton.edu/java/11cheatsheet/

Programming Languages

• Low level vs high level
• Compiled vs interpreted
• Imperative vs declarative (and functional)
• Statically typed vs dynamically typed
• See Comparison of programming languages

A Tiny Bit of C

#include <stdio.h>
int main()
{
   printf("Hello, World!");
   return 0;
}

A Tiny Bit of C++

#include <iostream>
using namespace std;

class Hello {
    std::string name;
  public:
    Hello (std::string newName) { name = newName; }
    std::string hello () { return "Hello " + name; }
};

int main () {
  Hello h ("world");
  cout << h.hello();
  return 0;
}

A Tiny Bit of Ruby

class HelloWorld
   def initialize(name)
      @name = name
   end
   def hello
      puts "Hello #{@name}!"
   end
end

h = HelloWorld.new("World")
h.hello

A Tiny Bit of Go

package main
import "fmt"

type hello struct {
    name string
}
func (h hello) hello() string {
    return "hello " + h.name
}

func main() {
    h := hello{name: "world"}
    fmt.Println(h.hello())
}

• Go has built-in support for parallel programming with goroutines and channels
• How Goroutines Work

Problem-solving Strategies

General categories of problems

• Straight-forward instruction following
• String manipulation
• Tree traversal
• Graph search
• Dynamic programming

Approaching coding interview questions

• Be thorough and verbalize your thought process (esp. if you're stuck!)
• First, clarify the question and any assumptions you're making about input/output/behavior
• Walk through potential solutions (if you can think of multiple with different runtime/space requirements, explain the tradeoffs and pick the one you'll implement)
• Write out the function header & return value type
• Implement the function body, explaining your code as you go & mentioning any invariants
• When you're done, say so and walk through simple examples
• Write out some test cases, esp edge cases
• Talk about the runtime and space requirements of your solution

See https://www.topcoder.com/community/data-science/data-science-tutorials/how-to-find-a-solution/

Final Thoughts

Interviews can seem scary, but don't let them stress you out. Honestly, they can be really insightful experiences--I've learned so much on those occasions when interviewers take the time to have a conversation (about code or their work). Just be prepared and confident, and remember that good interviews try to assess your ability to learn and work with a team, not just your knowledge. And, if you get to the stage where you're looking at job offers (congrats!), check out my other guide for things to consider when evaluating a role at a startup!