Today, we will tackle common interview challenges regarding linked lists, focusing on honing your problem-solving skills. Like figuring out the best way to organize a messy drawer, we'll explore ways to streamline and refine our coding techniques when working with linked lists. By the end, you'll have a better grasp of crafting efficient algorithms and writing clean, effective JavaScript code.

In this lesson, we will use the same linked list implementation as in the previous one, but with one additional method to display the list:

Our journey begins with a linked list with several duplicate values, resembling a situation where an email campaign mistakenly sends multiple invites to the same guests. Our goal is equivalent to ensuring each guest receives only one invitation, which means we must eliminate these duplicate nodes from our linked list.

Much as you would mark songs you have already checked, we can use a Set to keep track of the node values we've seen. When a duplicate value surfaces, we bypass the node altogether, simplifying the process to a manageable single pass through the list or an $O(n)$ operation — much as you would realize you've already heard this song and skip it. This method efficiently preserves our list's uniqueness, much like that perfect, non-repetitive playlist you'd enjoy on a long drive.

Here’s a step-by-step breakdown of how you'd implement such a solution:

We kick things off with an empty 'guest list' — our Set. Moving through the linked list, we add each unique value to the set. Whenever we encounter a value already in the set, we skip over the node containing it. This method ensures we only 'invite' each unique value once.

Here is an example of how we might use this function:

It successfully removes all the duplicates from the defined list.

For our next challenge, let's calculate the average of every third element in a linked list. Picture yourself in a fruit orchard, tasting every third apple to understand the overall harvest quality. While you would only sample some apples, this systematic approach lets you estimate the batch's quality reliably.

Much like our orchard sampling, sampling at consistent intervals can reduce noise in signal processing, helping us focus on signal trends. Consider financial analysts, who might average quarterly results to understand a company's performance trend.

We'll keep a running sum and a tally of every third element as we traverse the linked list. Once we reach the end, we can easily calculate the average, avoiding extra storage or double-handling of nodes. This simplification keeps our operation at $O(n)$ complexity, like sampling apples directly from the tree without hauling them all back to the barn first!

Here's how to implement this sampling:

Our implementation starts with checks for a 'no apples' scenario. Strolling through our orchard, we sample every third apple and update our running total. Note that we use (index + 1) in the condition (index + 1) % 3 === 0, because we need every third apple, and indexing in JavaScript starts with 0. Finally, we average our notes to convey the orchard's overall apple quality. Rounding our average to two decimal places is comparable to briefly summarizing our tastings.

Here is an example of how we might use this function:

The answer is 6, because it is an average value between the third value 7 and the sixth value 5.

In this session, we've applied discernment and mathematics to solve two linked list problems you're likely to encounter in job interviews. We've used clear real-world analogies to relate the practical approaches to everyday tasks, enabling easier comprehension. Keeping our code efficient and to the point, we've unwound the issues to straightforward and quick solutions.

Now that the explanations have been fleshed out, it's time to apply what you've learned.