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Data Structures & Algorithms

Simulate a series of coin tosses given the number of tosses and the probability of getting heads.

Simulating Coin Tosses

How do you calculate weekly active paying users (WAPU) by joining gameplay and purchase activity by week?

Track Your Most Valuable Gamers

Machine Learning

How would you build a model or algorithm to generate respawn locations for an online third person shooter game like Halo?

Video Game Respawn Model

<p>You’re an analyst at Ubisoft Gaming. Leadership wants to track <strong>Weekly Active Paying Users (WAPU)</strong>, players who record at least one game session and at least one in-app purchase in the same calendar week.</p>

<p>Write a SQL query that returns, for each week in the dataset:</p>

<ol>
<li><code>week_start</code>- the Monday of that calendar week<br/>
</li>
<li><code>wapu</code>- the number of distinct users who qualify as WAPU in that week<br/>
</li>
</ol>

<p><em>Note: Assume all timestamps are in UTC. Output should be ordered by <code>week_start</code> ascending.</em></p>

<h3><strong>Schema:</strong></h3>

<p><strong>Input:</strong></p>

<p><code>game_sessions</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>user_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>session_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>session_start</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>country</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><code>purchases</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>purchase_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>user_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>purchase_time</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>amount</td>
<td>DECIMAL(10,2)</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>week_start</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>wapu</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

<h3><strong>Example:</strong></h3>

<p><strong>Input:</strong></p>

<p><code>game_sessions</code> table</p>

<table>
<thead>
<tr>
<th>user_id</th>
<th>session_id</th>
<th>session_start</th>
<th>country</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>101</td>
<td>2023-10-02</td>
<td>USA</td>
</tr>

<tr>
<td>2</td>
<td>102</td>
<td>2023-10-03</td>
<td>USA</td>
</tr>

<tr>
<td>1</td>
<td>103</td>
<td>2023-10-04</td>
<td>USA</td>
</tr>

<tr>
<td>3</td>
<td>104</td>
<td>2023-10-05</td>
<td>USA</td>
</tr>
</tbody>
</table>
<p><code>purchases</code> table</p>

<table>
<thead>
<tr>
<th>purchase_id</th>
<th>user_id</th>
<th>purchase_time</th>
<th>amount</th>
</tr>
</thead>

<tbody>
<tr>
<td>201</td>
<td>1</td>
<td>2023-10-02</td>
<td>5.99</td>
</tr>

<tr>
<td>202</td>
<td>2</td>
<td>2023-10-03</td>
<td>10.00</td>
</tr>

<tr>
<td>203</td>
<td>1</td>
<td>2023-10-04</td>
<td>2.99</td>
</tr>

<tr>
<td>204</td>
<td>3</td>
<td>2023-10-05</td>
<td>7.49</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>week_start</td>
<td>2023-10-02</td>
</tr>

<tr>
<td>wapu</td>
<td>3</td>
</tr>
</tbody>
</table>
<p><strong>Explanation:</strong></p>

<p>All sessions and purchases occur during the same calendar week:</p>

<ul>
<li><strong>Week start:</strong> Monday <strong>2023-10-02</strong></li>
</ul>

<p>Users who qualify as WAPU (≥1 session AND ≥1 purchase in the same week):</p>

<ul>
<li><strong>User 1</strong> - sessions on Oct 2 &amp; 4; purchases on Oct 2 &amp; 4</li>
<li><strong>User 2</strong> - session on Oct 3; purchase on Oct 3</li>
<li><strong>User 3</strong> - session on Oct 5; purchase on Oct 5</li>
</ul>

<p>Total WAPU = <strong>3 users</strong></p>


<p>Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.</p>

<p>What factors could have biased this result, and what would you look into?</p>


<p>What could potentially bias the average boarding time in favor of an airline that only flies domestically?</p>

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

Statistics

<p>Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.</p>

<p>The company has been consistently growing new users in the city from January to March.</p>

<p>What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?</p>


<p>What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?</p>

What metric could be used to measure if active users are still engaging on the platform despite an overall decrease in the average number of comments per user?

Decreasing Comments

How would you investigate a decline in the average number of comments per user?

Question	Topic	Difficulty	Ask Chance
Track Your Most Valuable Gamers	SQL	Medium	Very High
Boarding Times Bias	Statistics	Medium	Very Low
Decreasing Comments	Product Sense & Metrics	Easy	Very Low

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