sephoram / ls-small-problems

Launch School's Small Problems Exercises

LS-small-problems

Launch School's Small Problems Exercises

General Approach for Problem Solving Outline by Launch School

Small Code Challenge Problems

• 20 - 45 minutes
• typical solutions: 10-40 LOC
• used extensively in interviews for a reason
  • mastery on a language
  • logic / reasoning
  • communications
• not a skill that you "acquire and file away", but needs a lot of practice

Understand the Problem

• requirements are explicit
  • take notes
• requirements are not so explicit and need to be modeled
  • examples need to be described in computational words
• implicit knowledge needs to be captured
  • convert to explicit rules, in computational language
• identifying and defining important terms and concepts
• ask questions to verify your understanding!

Examples / Test Cases

• Input / Output
• Test cases serve two purposes:
  • help you understand the problem
  • allow you to verify your solution
• happy paths
  • combination of requirements; the "obvious" result
• Edge cases
  • focus on input
  • emptiness (nil/null, "", [], {})
  • boundary conditions
  • repetition / duplication
  • data types
• Failures / Bad Input
  • raise exceptions / report errors
  • return a special value (nil/null, 0, "", [] etc)
• ask questions to verify your understanding!

Data Structure

• input data

• rules/requirements as data

  • hash / object

• string, array, hash/object, number

  • string
    • concact, strip, reverse, etc
    • Regular Expression! split, replace, match...
  • array
    • need to walk through it (iteration)
    • index
    • abstractions!!
      • map
      • reduce
      • select/filter
      • all
      • ...
  • hash/object
    • lookup table / dictionary
    • partition data for more efficient access downstream
    • digest
  • number
    • math operations
    • number as string may have advantage over numbers

• compound data structures

  • array of arrays
  • hash with array/object values, etc

Algorithm

• Algorithms have to be described in the language of chosen data structure!
  • "then solve it" doesn't count
• Have to be really fluent with
  • String / Regex
  • Array
    • Ruby: Enumerable
    • JavaScript: Higher Order Functions
  • Hash / Object
    • Creation (default values)
    • Access (default values)
    • Iteration
• verify your algorithm with your examples / test cases

Abstraction

• Very important, rely on language built in abstractions: String/Regex, Array, Hash/Object
• Avoid solving big problems!
  • Always, always, break bigger problems into smaller problems
  • Don't try to walk through a wall
• lay out the general steps of an algorithm, without having to go to details
  • Try to solve a problem in 1-2 sentences!!!
  • If you can't, raise your abstraction
    • create helper methods/functions
    • push detailed steps to methods/functions

Interview Tips

• Communicative
• Getting to a working solution >> no solution
• De-risking
  • Check your building blocks
    • run code often
    • debug locally
• Manage your energy