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Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Find Bigrams

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Write a function friendship_timeline to generate an output that lists the pairs of friends with their corresponding timestamps of the friendship beginning and then the timestamp of the friendship ending. 

Friendship Timeline

String Shift

Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Employee Salaries

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

Calculate the first touch attribution for each `user_id` that converted.

First Touch Attribution

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

Job Recommendation

RMS Error

Product Sense & Metrics

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

Decreasing Comments

Promoting Instagram

Threaded Comments

Group Success

Losing Users

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Ad Raters

<p>Let’s say that you’re the data scientist for the marketing/advertising division of your company. The marketing executive wants to test multiple new channels including:</p>

<ul>
<li>Youtube ads</li>
<li>Google search ads</li>
<li>Facebook ads</li>
<li>Direct mail campaigns</li>
</ul>

<p>Given these new marketing channels, how would you design an a/b test to utilize the marketing budget in the most efficient way possible?</p>


Marketing Dollar Efficiency

A/B Testing

<p>We’re given two tables, a <code>users</code> table with demographic information and the neighborhood they live in and a <code>neighborhoods</code> table.</p>

<p>Write a query that returns all neighborhoods that have 0 users. </p>

<p><strong>Example:</strong></p>

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

<p><code>users</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><code>neighborhoods</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>city_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


<p>Given a <code>employees</code> and <code>departments</code> table, select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.</p>

<p><strong>Example:</strong></p>

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

<p><code>employees</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>departments</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>percentage_over_100k</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_employees</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


<p>Write a SQL query to select the 2nd highest salary in the engineering department.</p>

<p><strong>Note:</strong> If more than one person shares the highest salary, the query should select the next highest salary.</p>

<p><strong>Example:</strong></p>

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

<p><code>employees</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>departments</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


<p>Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). </p>

<p><em>Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order.</em> </p>

<p><strong>Example:</strong></p>

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

<p><code>employees</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>departments</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong>:</p>

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

<tbody>
<tr>
<td><code>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Get the top 3 highest employee salaries by department

Top Three Salaries

<p>Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.</p>

<p>How would you assess the validity of the result?</p>


Experiment Validity

<p>Given a table of bank transactions with columns <code>id</code>, <code>transaction_value</code>, and <code>created_at</code> representing the date and time for each transaction, write a query to get the last transaction for each day.</p>

<p>The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.</p>

<p><strong>Example:</strong></p>

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

<p><code>bank_transactions</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Last Transaction

<p>Given two sorted lists, write a function to merge them into one sorted list.</p>

<p><em>Bonus: What’s the time complexity?</em></p>

<p><strong>Example:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">list1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="n">list2</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
</span></span></code></pre>

<p><strong>Output:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">merge_list</span><span class="p">(</span><span class="n">list1</span><span class="p">,</span><span class="n">list2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
</span></span></code></pre>


<p>You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort?</p>


You have two sorted lists, one with m elements and one with n elements. What would be the time complexity of merging the two lists via merge sort? 

Merging Lists Complexity

<p>Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.</p>

<p><em>Note: Assume bin buckets class intervals of one.</em></p>

<p><em>Note: Comments that were created outside of January 2020 should be counted in a “0” bucket</em></p>

<p><strong>Example:</strong></p>

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

<p><code>users</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>mail</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><code>comments</code> table</p>

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

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

<tr>
<td><code>body</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>comment_count</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>frequency</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


<p>Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first.</p>

<p>What’s the probability that Amy wins?</p>


Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first.

What’s the probability that Amy wins?

<p>Let’s say we want to build a naive recommender. We’re given two tables, one table called <code>friends</code> with a <code>user_id</code> and <code>friend_id</code> columns representing each user’s friends, and another table called <code>page_likes</code> with a <code>user_id</code> and a <code>page_id</code> representing the page each user liked.</p>

<p>Write an SQL query to create a metric to recommend pages for each user based on recommendations from their friend’s liked pages. </p>

<p><em>Note: It shouldn’t recommend pages that the user already likes.</em></p>

<p><strong>Example:</strong></p>

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

<p><code>friends</code> table</p>

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

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

<tr>
<td><code>friend_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>page_likes</code> table</p>

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

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

<tr>
<td><code>page_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>page_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>num_friend_likes</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Write a query to get the number of friends of a user that like a specific page

Liked Pages

<p>The schema below is for a retail online shopping company consisting of two tables, <code>attribution</code> and <code>user_sessions</code>.</p>

<ul>
<li><p>The attribution table logs a session visit for each row.</p></li>

<li><p>If <code>conversion</code> is <code>true</code>, then the user converted to buying on that session.</p></li>

<li><p>The <code>channel</code> column represents which advertising platform the user was attributed to for that specific session.</p></li>

<li><p>Lastly the <code>user_sessions</code> table maps many to one session visits back to one user.</p></li>
</ul>

<p>First touch attribution is defined as the channel with which the converted user was associated when they first discovered the website.</p>

<p>Calculate the first touch attribution for each <code>user_id</code> that converted. </p>

<p><strong>Example:</strong></p>

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

<p><code>attribution</code> table</p>

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

<tbody>
<tr>
<td><code>session_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>channel</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>conversion</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><code>user_sessions</code> table</p>

<table>
<thead>
<tr>
<th>column</th>
<th>type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>session_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Example output:</strong></p>

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>channel</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>123</td>
<td>facebook</td>
</tr>

<tr>
<td>145</td>
<td>google</td>
</tr>

<tr>
<td>153</td>
<td>facebook</td>
</tr>

<tr>
<td>172</td>
<td>organic</td>
</tr>

<tr>
<td>173</td>
<td>email</td>
</tr>
</tbody>
</table>


<p>A team wants to A/B test multiple different changes through a sign-up funnel.</p>

<p>For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.</p>

<p>How would you set up this test?</p>


<p>Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.</p>

<p>How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.</p>


Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

Button AB Test

<p>Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previous drawn card?</p>


<p>Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what’s the probability of each subsequent card being larger than the previously drawn card?</p>


Imagine a deck of 2000 cards numbered from 1 to 2000. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what's the probability of each subsequent card being larger than the previously drawn card?

2000 cards

<p>Let’s say you want to test the close friends feature on Instagram Stories.</p>

<p>How would you make a control group and test group to account for network effects?</p>


<p>What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?</p>

What could be a potential risk when Facebook segments the metrics of the user interface change by market or demographic groups?

Network Experiment Design I

<p>What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?</p>

What is the primary metric that Facebook should monitor if it decides to make the user interface of its posting feature more like Instagram's?

Network Experiment Design II

<p>If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?</p>

If Facebook changes its user interface to mimic Instagram's, which adverse effect should they anticipate and monitor?

Network Experiment Design III

Network Experiment Design

<p>Netflix has hired people to rate movies.</p>

<p>Out of all of the raters, 80% of the raters carefully rate movies and rate 60% of the movies as good and 40% as bad. The other 20% are lazy raters and rate 100% of the movies as good.</p>

<p>Assuming all raters rate the same amount of movies, what is the probability that a movie is rated good?</p>


<p>There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.</p>

There are two categories of movie raters: careful and lazy. Careful raters make up 80% of the total. They rate 60% of the movies as good and 40% as bad. Lazy raters, constitute 20% of the raters and 100% of the movies as good. Given these parameters, calculate the overall probability of a movie being rated good.

Lazy Raters

<p>Given a table called <code>user_experiences</code>, write a query to determine the percentage of users that held the title of <code>&#34;Data Analyst&#34;</code> immediately before holding the title <code>&#34;Data Scientist&#34;</code>.</p>

<p>Immediate is defined as the user holding no other titles between the <code>&#34;Data Analyst&#34;</code> and <code>&#34;Data Scientist&#34;</code> roles.</p>

<p><strong>Example:</strong></p>

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

<p><code>user_experiences</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>position_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>start_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>percentage</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


User Experience Percentage

<p>Given the tables <code>users</code> and <code>rides</code>, write a query to report the distance traveled by each user in descending order.</p>

<p><strong>Example:</strong></p>

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

<p><code>users</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>rides</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>passenger_user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>distance</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>distance_traveled</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Distance Traveled

<p>Given a table of product subscriptions with a subscription start date and end date for each user, write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other completed subscription.</p>

<p>Completed subscriptions have <code>end_date</code> recorded.</p>

<p><strong>Example:</strong></p>

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

<p><code>subscriptions</code> table</p>

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

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

<tr>
<td><code>start_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>start_date</code></th>
<th><code>end_date</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2019-01-01</td>
<td>2019-01-31</td>
</tr>

<tr>
<td>2</td>
<td>2019-01-15</td>
<td>2019-01-17</td>
</tr>

<tr>
<td>3</td>
<td>2019-01-29</td>
<td>2019-02-04</td>
</tr>

<tr>
<td>4</td>
<td>2019-02-05</td>
<td>2019-02-10</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>overlap</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>1</td>
</tr>

<tr>
<td>2</td>
<td>1</td>
</tr>

<tr>
<td>3</td>
<td>1</td>
</tr>

<tr>
<td>4</td>
<td>0</td>
</tr>
</tbody>
</table>


Subscription Overlap

<p>How would you explain what a p-value is to someone who is not technical?</p>


<p>What does a p-value in a statistical test represent?</p>

What does a p-value in a statistical test represent?

P-value to a Layman I

<p>In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?</p>

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

P-value to a Layman

Statistics

<p>Let’s say we have a very naive advertising platform. There is an audience of size <em>A</em> and a limited amount of impressions <em>B</em>.</p>

<p>Each of the impressions goes to just one user, defined at random. Every user in the audience has the same random chance of receiving each impression. </p>

<ol>
<li><p>Compute the probability that a user has exactly 0 impressions. </p></li>

<li><p>What’s the probability that every person has at least 1 impression?</p></li>
</ol>


Impression Reach

<p>Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.</p>

<p>The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.</p>

<p>How would we build this model?</p>


<p>Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.</p>

<p>The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.</p>

<p>Which statement is true?</p>


Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Bank Fraud Model

<p>Given a table of user sessions, write a query to get the number of days between each user’s first session and last session for the year 2020. </p>

<p><strong>Example:</strong></p>

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

<p><code>user_sessions</code> table</p>

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

<tbody>
<tr>
<td><code>session_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>no_of_days</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Find the difference between users' first and last sessions in the year

Session Difference

<p>Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.</p>

<p>Write a query to randomly sample a row from this table.</p>

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

<p><code>big_table</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


Write a query to randomly sample a row from a big table.

Random SQL Sample

<p>You are a data scientist at YouTube focused on creators.</p>

<p>A PM comes to you worried that amateur video creators could do well before but now it seems like only “superstars” do well.</p>

<p>What data points and metrics would you look at to decide if this is true or not?</p>


Amateur Performance

<p>You’re given three tables.</p>

<p>An <strong>ads</strong> table holds an ID and the advertisement name like “Labor day shirts sale”. The <strong>feed_comments</strong> table holds the comments on ads by different users that occur in the regular feed. The <strong>moments_comments</strong> table holds the comments on ads by different users in the moments section.</p>

<p>Write a query to get the percentage of comments, by ad, that occurs in the feed versus mentions sections of the app.</p>

<p><strong>Example:</strong></p>

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

<p><code>feed_comments</code> table</p>

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

<tbody>
<tr>
<td><code>ad_id</code></td>
<td>integer</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>integer</td>
</tr>

<tr>
<td><code>comment_id</code></td>
<td>integer</td>
</tr>
</tbody>
</table>
<p><code>moments_comments</code> table</p>

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

<tbody>
<tr>
<td><code>ad_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>comment_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>ads</code> table</p>

<table>
<thead>
<tr>
<th><strong>column</strong></th>
<th><strong>type</strong></th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>name</th>
<th>percentage_feed</th>
<th>percentage_moments</th>
</tr>
</thead>

<tbody>
<tr>
<td>Labor Day</td>
<td>.6</td>
<td>.4</td>
</tr>

<tr>
<td>Polo Shirts</td>
<td>.85</td>
<td>.15</td>
</tr>
</tbody>
</table>


Write a query to get the percentage of comments, by ad, that occurs in the feed versus mentions sections of the app.

Ad Comments

<p>Given the <code>transactions</code> table below, write a query that finds the third purchase of every user.</p>

<p><em>Note: Sort the results by the <code>user_id</code> in ascending order. If a user purchases two products at the same time, the lower <code>id</code> field is used to determine which is the first purchase.</em></p>

<p><strong>Example:</strong></p>

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

<p><code>transactions</code> table</p>

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

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

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

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>


Third Purchase

<p>Given two tables:  <code>accounts</code>, and <code>downloads</code>, find the average number of downloads for free vs paying accounts, broken down by day.</p>

<p><em>Note: You only need to consider accounts that have had at least one download before when calculating the average.</em></p>

<p><em>Note: round <code>average_downloads</code> to 2 decimal places.</em></p>

<p><strong>Example:</strong></p>

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

<p><code>accounts</code> table</p>

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

<tbody>
<tr>
<td><code>account_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>paying_customer</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
<p><code>downloads</code> table</p>

<table>
<thead>
<tr>
<th>column</th>
<th>type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>account_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>download_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>downloads</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td><code>download_date</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>paying_customer</code></td>
<td>BOOLEAN</td>
</tr>

<tr>
<td><code>average_downloads</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Download Facts

<p>The <code>events</code> table tracks every time a user performs a certain action (like, post_enter, etc.) on a platform. An example of this table would look like this:</p>

<table>
<thead>
<tr>
<th>user_id</th>
<th>created_at</th>
<th>action</th>
<th>platform</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2021-01-01 12:00:00</td>
<td>email_opened</td>
<td>Android</td>
</tr>

<tr>
<td>2</td>
<td>2021-01-01 12:05:00</td>
<td>like</td>
<td>Outlook</td>
</tr>

<tr>
<td>3</td>
<td>2021-01-01 12:10:00</td>
<td>post_enter</td>
<td>Gmail</td>
</tr>

<tr>
<td>4</td>
<td>2021-01-01 12:15:00</td>
<td>post_cancel</td>
<td>Outlook</td>
</tr>

<tr>
<td>5</td>
<td>2021-01-01 12:20:00</td>
<td>like</td>
<td>Gmail</td>
</tr>
</tbody>
</table>
<p>What query would you write to determine how many different users gave a like on June 6, 2020?</p>

<p><strong>Example:</strong></p>

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

<p><code>events</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>created_at</td>
<td>DATETIME</td>
</tr>

<tr>
<td>action</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>platform</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

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

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


Find out the number of users that gave a like on a certain date

Like Tracker

<p>Let’s say that you’re a data scientist on the engagement team. A product manager comes up to you and says that the weekly active users metric is up 5% but email notification open rates are down 2%.</p>

<p>What would you investigate to diagnose what’s happening?</p>


Calculate Moving Average	SQL	Easy
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Recommend Similar Products	Machine Learning	Hard

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