Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
What Are They Asking?: Understanding Code Challenge Problems
Rating: 5.0 out of 5(1 rating)
419 students

What Are They Asking?: Understanding Code Challenge Problems

Master the Art of Decoding Problem Statements in Coding Challenges
Created byNorbert Grover
Last updated 1/2026
English

What you'll learn

  • Translate complex coding problem statements into clear, understandable goals.
  • Identify hidden requirements, constraints, and edge cases within technical prompts.
  • Break down multi-part coding problems into manageable, logical steps.
  • Recognize and interpret unfamiliar terminology and technical jargon.
  • Apply a repeatable method for reading, analyzing, and decoding problem statements.
  • Develop confidence in understanding vague or tricky interview questions.
  • Avoid common misunderstandings that lead to incorrect implementations.
  • Practice analyzing real-world problem statements from coding platforms and interviews.
  • Understand how problem wording impacts the algorithm or approach needed.
  • Build better problem-solving habits before writing any code.

Course content

1 section13 lectures2h 5m total length
  • Introduction0:30

    Learn how to confidently interpret confusing or complex coding challenge prompts. This course teaches you how to break down problem statements, identify key requirements, and clarify what’s really being asked—before you start coding.

  • Generate the Sum of Even Integer numbers in an Array7:39

    This lecture simply demonstrates how to interpret a problem statement so that a person may be able solve a simple coding challenge.

  • Balanced Sequence Detector18:46

    This challenge is a textbook example of how reading comprehension in code problems is just as critical as programming skill. Here's why it works so well as a teaching example:

    Why the Challenge Is Tricky

    1. The phrase “contains a balanced subsequence” is vague
      Many people will assume it refers to the entire array, not just a part of it.

    2. “Balanced” is a domain-specific word
      It sounds intuitive, but what counts as “balanced” depends entirely on the precise definition — which hides a small but crucial detail: ignore the middle element if the length is odd.

    3. “Contiguous subsequence of length 2 or more” is easy to skim
      Learners may gloss over this and either:

      • Miss that the function should check parts of the array

      • Miss that it should return true on the first match, not the best or largest one

    4. The function returns a boolean, not a specific match
      This subtly encourages early returns — and masks what’s actually being matched unless you log or trace it.

    Why This Makes a Great Learning Moment

    • It shows that deciphering the prompt is a skill — not fluff, but an essential technical ability

    • It exposes how assumptions sneak in and lead you away from what the code is actually doing

    • It creates space for a meaningful “aha” moment — just like you had — when you realize:
      “Oh! The function returns true because it found a small valid part — not because the whole thing matched.”

  • Minimal Split Sum15:01

    Confusing aspects of the Problem Statement

    1. Ambiguous Use of “Split”

    "Split into two non-empty parts..."

    Most readers will interpret this as:

    • "Two parts that each contain at least one number"

    But then the constraints say:

    "...if no such split exists (e.g., empty or single-element list), treat one side as empty..."

    That seems to contradict the first part. Are we allowed to have an empty side or not?

    This mixed messaging forces the reader to mentally patch the rule.

    2. Assuming It’s About the Best Split Location

    The phrasing:

    "determine whether there exists a position..."

    Makes it sound like the goal is to find that position, but the actual task is just to return the minimum difference.

    This could lead a reader to:

    • Try to track index positions

    • Or return the index instead of the difference

    3. The Word “Minimal” Can Be Misleading

    "Return the minimum possible absolute difference..."

    This wording may feel open-ended. "Minimum compared to what?" is a common internal question.

    It might not be obvious that:

    • You're checking all possible valid split points

    • And comparing their absolute differences

    A clearer phrasing would be:

    "Return the smallest absolute difference of sums that can be obtained by splitting the array at any index."

  • Zero-Shifted Sum4:53

    Why This Problem Statement Can Be Confusing

    1. "Zero-Shifted Sum" Is a Made-Up Term

    • Most programmers (even experienced ones) have never heard of a "zero-shifted sum."

    • It's not a standard term in computer science or mathematics.

    • Without a definition, readers might:

      • Assume it's some known algorithm or shorthand

      • Misinterpret the behavior based on the name alone

    Takeaway: Students must learn to not assume meaning from jargon — instead, they need to rely on the precise behavior described in the statement.

    2. Mental Model of “Shift” Can Vary

    • The idea of “shifting” a list often implies rotation or circular movement in other contexts.

    • But here, “shift to the right and put zero in the first slot” is not a rotation — it's an overwrite.

    • So someone might interpret it as:

      • Move all elements right, drop the last one

      • Rotate the list

      • Insert zero and push the rest (which you are supposed to do)

      • Or even replace all elements with zeroes

    Takeaway: When a problem says “shift,” students must slow down and confirm: “What exactly happens to each element?”

  • In-Place Hashing5:49

    This lecture explains In-Place Hashing and why it's used for coding challenges.

  • First Missing Gap15:58

    This lecture explains how to code an in-place hashing system for a function that finds the missing integer within a series of integers. This is important because in coding challenges: many challenges require that you understand how to preserve space complexity that is defined as O(n) or constant.

  • Sum of Unique Doubles6:12

    This lecture explores how to interpret and solve a coding challenge that requires identifying numbers that appear exactly twice in a list. You'll learn to distinguish between similar but subtly different problem types, handle frequency counting effectively, and evaluate tradeoffs between using extra space versus optimizing for time and memory.

  • Explanation for Longest Normalized Span10:23

    This lecture gives a step by step explanation for what a 'Span' is. Coding challenges are so ambiguous sometimes, so let's decode what the problem statement is trying to say and get down to the business of coding our solution.

  • Longest Normalized Span7:21

    This lecture demonstrates how to code the solution and address spans and sliding windows.

  • Explanation of a Segmented Stability Score12:14

    This lecture explains how a segmented stability score works — a critical concept to understand if you plan to become a Swift/iOS developer working in domains like fintech, robotics, or any field that involves analyzing time-series data. Understanding how to identify and measure stability in streaming or ordered data can help you build more intelligent, responsive, and data-aware applications.

  • Coding the Maximum Segmented Stability Score6:26

    This lecture goes over the code used to find the max segment stability score.

  • Finding the Second Smallest Unique Index14:01

    This lecture explains a coding challenge that may be simple to some but tricky to others and may lead to you missing out on that career opportunity that can be a life changer.

Requirements

  • Basic understanding of variables, loops, and conditionals in any programming language.
  • Familiarity with arrays or lists and how to iterate over them.
  • Some experience writing simple functions or methods.
  • Ability to read and follow basic programming logic (e.g., if-statements, for-loops).
  • Willingness to slow down and focus on understanding problems before coding.
  • Comfort with reading beginner to intermediate-level code challenges.
  • No need to be a computer science major — just logical thinking and curiosity.
  • Some exposure to online coding platforms (like LeetCode, HackerRank, etc.) is helpful.
  • An open mindset and a desire to improve problem-solving clarity.

Description

Understanding the problem is half the problem — and in coding challenges, it’s often the hardest half. In technical interviews, take-home tests, and competitive programming, developers frequently struggle not with coding itself, but with interpreting the problem statement. The real challenge? Figuring out what they’re actually asking.

This course is designed for programmers who feel overwhelmed or confused by the way coding problems are described. If you’ve ever read a prompt multiple times and still felt unsure what to do — you’re not alone. Many coding problems use vague phrasing, unusual terminology, or imply requirements that aren’t clearly stated. Most tutorials skip over this entirely, assuming you already “get” the question. This course doesn’t.

"What Are They Asking?" focuses entirely on breaking down coding problem statements. You'll learn how to identify constraints, translate technical prompts into clear instructions, and develop a step-by-step process to dissect and analyze problems before writing a single line of code. We’ll explore real-world examples that demonstrate how tricky or misleading language can derail even experienced developers — and how to avoid those traps.

If you've ever thought, "I could solve this if I just understood what they wanted," this course is for you. Understanding the question isn't optional — it's a skill. Let's build it.

Who this course is for:

  • Beginner developers who struggle to understand coding challenge instructions.
  • Aspiring iOS/Swift developers preparing for technical interviews.
  • Self-taught programmers who want to sharpen their problem analysis skills.
  • Students who get stuck not because of code — but because of confusing problem wording.
  • Developers transitioning from UI work to algorithmic problem solving.
  • Job seekers aiming to pass online assessments or whiteboard interviews.
  • Bootcamp graduates looking to build stronger problem-decoding habits.
  • Anyone who’s ever read a coding prompt and thought: “Wait... what are they asking?”
  • Programmers who want to improve their test-taking confidence and speed.