Prepare for and practice interview questions from Boston Scientific across topics like SQL, Product Metrics, Statistics and more.

Boston Scientific Interview Questions

Boston Scientific Interview Guides

Machine Learning

How would you build a job recommendation feed?

Job Recommendation

Write a function calculate_rmse to calculate the root mean squared error from y_pred and y_true

RMS Error

Lasso vs Ridge

What are some machine learning methods for building this system

FAQ Matching

In which case would you use a bagging algorithm versus a boosting algorithm

Bagging vs Boosting

Data Structures & Algorithms

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Write a function moving_window to find the moving window average

Moving Window

Given a json string with nested objects, write a function that flattens all the objects to a single key-value dictionary.

Flatten JSON

Write a function str_map to determine if there exists a one-to-one correspondence between characters of string1 and string2

String Mapping

Write a function `sorting` from scratch to sort a list of strings in ascending alphabetical order

Sort Strings

Select the 2nd highest salary in the engineering department

2nd Highest Salary

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

Empty Neighborhoods

Last Transaction

Flight Records

Write a SQL query that creates a cumulative distribution of the number of comments per user with bin buckets of one

Cumulative Distribution

Product Sense & Metrics

Where and how could you promote Instagram through Facebook

Promoting Instagram

How would you measure the success of Facebook Groups

Group Success

How would you measure the success of the Instagram TV product

Instagram TV Success

You work as a data scientist for ride-sharing company.  An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea?  How would you implement it? What metrics would you track?

Rider Discount

Pitching a feature idea that could drive success for Instagram Stories

Boosting Instagram Stories

Statistics

Explain what a p-value is to someone who is not technical

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

What is the downside of only using the R-Squared (R^2) value to determine a relationship between two variables

Using R Squared

Explain the difference between covariance and correlation and provide an example

Covariance vs Correlation

Explain what an unbiased estimator is and provide a layman-friendly example

Unbiased Estimator

<p>We have two models: one with 85% accuracy, one 82%. Which one do you pick?</p>


Which model do you pick given 85% and 82% accuracy

85% vs 82%

<p>Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?</p>


Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Behavioral

<p>Let’s say your manager asks you to build a model with a neural network to solve a business problem.</p>

<p>How would you justify the complexity of building such a model and explain the predictions to non-technical stakeholders?</p>


<p>Which is the following domains is generally NOT a good use case for using a deep neural net?</p>


Which is the following domains is generally NOT a good use case for using a deep neural net?

Neural Net Use Cases

How would you justify the complexity of building such a model and explain the predictions to non-technical stakeholders

Justify a Neural Network

<p>Suppose you are analyzing auto insurance data. You find that in the demographic information for all insurance clients that the marriage attribute column is marked <code>TRUE</code> for all customers.</p>

<p>How would you debug what happened? What data would you look into and how would you find out who is actually married and who is not?</p>


How would you debug why the marriage attribute is marked TRUE for all customers and determine who is actually married

Debug Marriage Data

Analytics

<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>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>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>


How would you assess the validity of the result?

Experiment Validity

A/B Testing

<p>Given the two tables, write a SQL query that creates a cumulative distribution of the number of comments per user. Assume bin buckets class intervals of one.</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>sex</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>Columns</th>
<th>Type</th>
</tr>
</thead>

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

<tr>
<td><code>cum_total</code></td>
<td>FLOAT</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>Tell me about a data project that didn’t go the way you expected. What did you set out to do, what surprised you, and how did you handle it?</p>


Describing a data project and its challenges

Hurdles In Data Projects

<p>Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.</p>

<p><em>Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.</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">N</span> <span class="o">=</span> <span class="mi">3</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">prime_numbers</span><span class="p">(</span><span class="n">N</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
</span></span></code></pre>


<p>Let’s say you work at an advertising firm. You have a table of users’ impressions of ad campaigns over time. Each <code>impression_id</code> consists of values of user engagement specified by <code>Excited</code>, <code>OK</code>, and <code>Bored</code>.</p>

<p>Write a query to find all users who were at some point “Excited” and have never been “Bored” with a campaign.</p>

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

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

<p><code>ad_impressions</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>dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>campaign_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>impression_id</code></td>
<td>TEXT</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>
</tbody>
</table>


Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign.

Always Excited Users

<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>


<p>The probability that it will rain tomorrow is dependent on whether or not it is raining today and whether or not it rained yesterday.</p>

<p>If it rained yesterday and today, there is a 20% chance it will rain tomorrow. If it rained one of the days, there is a 60% chance it will rain tomorrow. If it rained neither today nor yesterday, there is a 20% chance it will rain tomorrow.</p>

<p>Given that it is raining today and that it rained yesterday, write a function <code>rain_days</code> to calculate the probability that it will rain on the nth day after today.</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">n</span><span class="o">=</span><span class="mi">5</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">rain_days</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mf">0.39968</span>
</span></span></code></pre>


Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.

Rain in N Days

Probability

<p>Write a query to create a new table, named flight routes, that displays unique pairs of two locations.</p>

<p><em>Note: Duplicate pairs from the <code>flights</code> table, such as Dallas to Seattle and Seattle to Dallas, should have one entry in the flight routes table.</em></p>

<h3>Schema</h3>

<p><code>flights</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>source_location</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>destination_location</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>destination_one</code></td>
<td>VARCHAR</td>
</tr>

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

<h3>Example</h3>

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

<p><strong><code>flights</code> table</strong></p>

<table>
<thead>
<tr>
<th>id</th>
<th>source_location</th>
<th>destination_location</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>Seattle, WA</td>
<td>Dallas, TX</td>
</tr>

<tr>
<td>2</td>
<td>Dallas, TX</td>
<td>Seattle, WA</td>
</tr>

<tr>
<td>3</td>
<td>Seattle, WA</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>4</td>
<td>San Francisco, CA</td>
<td>Dallas, TX</td>
</tr>

<tr>
<td>5</td>
<td>San Francisco, CA</td>
<td>Portland, OR</td>
</tr>

<tr>
<td>6</td>
<td>Seattle, WA</td>
<td>Dallas, TX</td>
</tr>

<tr>
<td>7</td>
<td>Portland, OR</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>8</td>
<td>San Francisco, CA</td>
<td>Los Angeles, CA</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>destination_one</th>
<th>destination_two</th>
</tr>
</thead>

<tbody>
<tr>
<td>Dallas, TX</td>
<td>Seattle, WA</td>
</tr>

<tr>
<td>Portland, OR</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>San Francisco, CA</td>
<td>Seattle, WA</td>
</tr>

<tr>
<td>Dallas, TX</td>
<td>San Francisco, CA</td>
</tr>

<tr>
<td>Los Angeles, CA</td>
<td>San Francisco, CA</td>
</tr>
</tbody>
</table>


<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>Say you are tasked with designing a data mart or data warehouse for a new online retailer.</p>

<p>How would you design the system?</p>

<p><em>Note: Sketch a star schema to explain your design.</em></p>


Design a data warehouse for a new online retailer

Retailer Data Warehouse

Data Modeling

<p>Let’s say you work at Costco. Costco has a database with two tables. The first is <code>users</code> composed of user information, including their registration date, and the second is <code>purchases</code> that has the entire item purchase history (if any) for those users.</p>

<p>Write a query to get the total amount spent on each item in the ‘purchases’ table by users that registered in 2022.</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>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>registration_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><code>purchases</code> table</p>

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

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

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

<tr>
<td><code>item</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>price</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>purchase_date</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>item</code></td>
<td>VARCHAR</td>
</tr>

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


Total Spent on Products

<p>Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? </p>

<p>Give an example of the tradeoffs between the two.</p>


<p>In machine learning, when would you use a bagging algorithm over a boosting algorithm?</p>


In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

<p>Let’s say that you’re working on a job recommendation engine. You have access to all user Linkedin profiles, a list of jobs each user applied to, and answers to questions that the user filled in about their job search.</p>

<p>Using this information, how would you build a job recommendation feed?</p>


<p>What problem can arise in job recommendation systems with regard to initial recommendations?</p>

What problem can arise in job recommendation systems with regard to initial recommendations?

<p>Write a function <code>calculate_rmse</code> to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions <code>y_pred</code> and another with the target values <code>y_true</code>.</p>

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

<p><strong>Example 1: </strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</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">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">calculate_rmse</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span><span class="n">y_true</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mi">0</span>
</span></span></code></pre>

<p><strong>Example 2:</strong></p>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">y_pred</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</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">y_true</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="k">def</span> <span class="nf">calculate_rmse</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span><span class="n">y_true</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mf">1.63</span>
</span></span></code></pre>


<p>A biomedical research lab is studying treatment effectiveness across different brain cancer categories. Each patient is associated with one cancer type and may undergo zero or more treatments. Write a query to perform an aggregated analysis across all cancer categories.</p>

<p>For each cancer type, compute:</p>

<ol>
<li>Total number of patients</li>
<li>Percentage of patients who survived ≥ 12 months

<ul>
<li>Round to 2 decimal places</li>
<li>A “1-year survivor” is defined as <code>survival_months &gt;= 12</code></li>
</ul></li>
<li>Average number of treatments per patient

<ul>
<li>Round to 2 decimal places</li>
<li>Include patients with zero treatments</li>
</ul></li>
</ol>

<p>Sort the results in descending order by survival rate.</p>

<h3><strong>Example 1</strong></h3>

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

<p><strong>Table <code>patients</code></strong></p>

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

<tbody>
<tr>
<td>patient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>cancer_type</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>survival_months</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><strong>Table <code>treatments</code></strong></p>

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

<tbody>
<tr>
<td>treatment_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>patient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>treatment_name</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>treatment_date</td>
<td>DATE</td>
</tr>
</tbody>
</table>
<p><strong>Output :</strong></p>

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

<tbody>
<tr>
<td>cancer_type</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>total_patients</td>
<td>INTEGER</td>
</tr>

<tr>
<td>pct_one_year_survivors</td>
<td>DECIMAL</td>
</tr>

<tr>
<td>avg_treatments_per_patient</td>
<td>DECIMAL</td>
</tr>
</tbody>
</table>

<h3><strong>Example 2</strong></h3>

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

<p><strong><code>patients</code> table</strong></p>

<table>
<thead>
<tr>
<th>patient_id</th>
<th>cancer_type</th>
<th>survival_months</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>Glioblastoma</td>
<td>10</td>
</tr>

<tr>
<td>2</td>
<td>Meningioma</td>
<td>24</td>
</tr>

<tr>
<td>3</td>
<td>Astrocytoma</td>
<td>15</td>
</tr>

<tr>
<td>4</td>
<td>Glioblastoma</td>
<td>14</td>
</tr>

<tr>
<td>5</td>
<td>Meningioma</td>
<td>7</td>
</tr>
</tbody>
</table>
<p><strong><code>treatments</code> table</strong></p>

<table>
<thead>
<tr>
<th>treatment_id</th>
<th>patient_id</th>
<th>treatment_name</th>
<th>treatment_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>201</td>
<td>1</td>
<td>Radiation</td>
<td>2024-03-01</td>
</tr>

<tr>
<td>202</td>
<td>1</td>
<td>Chemotherapy</td>
<td>2024-06-01</td>
</tr>

<tr>
<td>203</td>
<td>2</td>
<td>Surgery</td>
<td>2024-02-01</td>
</tr>

<tr>
<td>204</td>
<td>4</td>
<td>Chemotherapy</td>
<td>2024-08-01</td>
</tr>
</tbody>
</table>

<h3><strong>Output:</strong></h3>

<table>
<thead>
<tr>
<th>cancer_type</th>
<th>total_patients</th>
<th>pct_one_year_survivors</th>
<th>avg_treatments_per_patient</th>
</tr>
</thead>

<tbody>
<tr>
<td>Astrocytoma</td>
<td>1</td>
<td>100.00</td>
<td>0.00</td>
</tr>

<tr>
<td>Meningioma</td>
<td>2</td>
<td>50.00</td>
<td>0.50</td>
</tr>

<tr>
<td>Glioblastoma</td>
<td>2</td>
<td>50.00</td>
<td>1.00</td>
</tr>
</tbody>
</table>

<h3><strong>Explanation:</strong></h3>

<p><strong>Astrocytoma</strong></p>

<ul>
<li>Total patients: 1</li>
<li>Survivors: <sup>1</sup>⁄<sub>1</sub> → 100.00%</li>
<li>Treatments: 0 total → 0.00 per patient</li>
</ul>

<p><strong>Meningioma</strong></p>

<ul>
<li>Total patients: 2</li>
<li>Survivors: patient 2 (24 months) → 50.00%</li>
<li>Treatments: 1 total (patient 2) → 0.50 per patient</li>
</ul>

<p><strong>Glioblastoma</strong></p>

<ul>
<li>Total patients: 2</li>
<li>Survivors: patient 4 (14 months) → 50.00%</li>
<li>Treatments: 3 total (2 for patient 1, 1 for patient 4) → 1.00 per patient</li>
</ul>


For each cancer type, compute total patients, percentage surviving at least 12 months, and average treatments per patient

Brain Cancer Treatment Outcomes

<p>Let’s say you’re interning at a fintech startup that’s building a platform for online games of chance. During testing, one of your simulations flips a coin 10 times and gets 8 tails and 2 heads.</p>

<p>How would you determine if the coin is fair, and what statistical approach would you use to support your conclusion?</p>


How would you assess if a coin is fair after observing 8 tails in 10 flips?

Fair Coin

<p>Let’s say you work at Allstate. Allstate is running <code>N</code>  online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).</p>

<p>Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations</p>

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

<p><em>Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types</em></p>

<p><em>Note: Please return only one query with each number in a different row</em></p>

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

<p><strong>Input:</strong></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>join_type</code></td>
<td>VARCHAR</td>
</tr>

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


Create top_ads with the top 3 ads and return the row counts for inner, left, right, and cross joins with ads

Size of Joins

<p>Given a <code>users</code> table, write a query to get the cumulative number of new users added by the day, with the total reset every month. </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>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>DATE</th>
<th>INTEGER</th>
</tr>
</thead>

<tbody>
<tr>
<td>2020-01-01</td>
<td>5</td>
</tr>

<tr>
<td>2020-01-02</td>
<td>12</td>
</tr>

<tr>
<td>…</td>
<td>…</td>
</tr>

<tr>
<td>2020-02-01</td>
<td>8</td>
</tr>

<tr>
<td>2020-02-02</td>
<td>17</td>
</tr>

<tr>
<td>2020-02-03</td>
<td>23</td>
</tr>
</tbody>
</table>


Given a users table, write a query to get the cumulative number of new users added by the day, with the total reset every month. 

Cumulative Reset

<p>Let’s say you’re a Product Data Scientist at Instagram. How would you measure the success of the Instagram TV product?</p>


<p>What is the primary goal of Instagram TV as a product feature?</p>

What is the primary goal of Instagram TV as a product feature?

<p>How would you measure the success of Facebook Groups?</p>


<p>Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?</p>


Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

<p>A digital health startup is analyzing ECG sensor data captured from wearable heart monitors. Each row in the dataset represents one heartbeat, including its timestamp and calculated heart rate at that moment.</p>

<p>The clinical team wants to detect tachycardia runs, periods where a patient’s heart rate stays above 100 bpm continuously for at least 10 seconds .</p>

<p>Write a SQL query to identify every valid tachycardia run and return:</p>

<ol>
<li><code>patient_id</code></li>
<li><code>run_start</code>: timestamp of the first beat in the run</li>
<li><code>run_end</code>: timestamp of the last beat in the run</li>
<li><code>duration_seconds</code>: difference in seconds between start and end (rounded to 2 decimals)</li>
</ol>

<p>Sort results by <code>patient_id</code>, then <code>run_start</code>.</p>

<p><strong>Definition of a Tachycardia Run</strong></p>

<ul>
<li>All consecutive heartbeats must have <code>heart_rate &gt; 100</code></li>
<li>Total duration ≥ 10 seconds</li>
<li>Any gap ≥ 3 seconds between consecutive beats breaks the run</li>
</ul>

<p><strong><em>Note:</em></strong> <em>A patient may have multiple separate runs.</em></p>

<p><strong>Schema</strong></p>

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

<p><strong><code>ecg_readings</code> table</strong></p>

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

<tbody>
<tr>
<td>patient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>timestamp</td>
<td>DATETIME</td>
</tr>

<tr>
<td>heart_rate</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>patient_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>run_start</td>
<td>DATETIME</td>
</tr>

<tr>
<td>run_end</td>
<td>DATETIME</td>
</tr>

<tr>
<td>duration_seconds</td>
<td>DECIMAL</td>
</tr>
</tbody>
</table>
<p><strong>Example</strong></p>

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

<p><strong><code>ecg_readings</code> table</strong></p>

<table>
<thead>
<tr>
<th>patient_id</th>
<th>timestamp</th>
<th>heart_rate</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2025-03-01 10:00:00</td>
<td>98</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:02</td>
<td>105</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:04</td>
<td>110</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:07</td>
<td>108</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:12</td>
<td>107</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:15</td>
<td>85</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:20</td>
<td>120</td>
</tr>

<tr>
<td>1</td>
<td>2025-03-01 10:00:28</td>
<td>122</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

<table>
<thead>
<tr>
<th>patient_id</th>
<th>run_start</th>
<th>run_end</th>
<th>duration_seconds</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2025-03-01 10:00:02</td>
<td>2025-03-01 10:00:12</td>
<td>10.00</td>
</tr>
</tbody>
</table>

<h3><strong>Explanation</strong></h3>

<p>The high-rate streak from 10:00:02 to 10:00:12 forms one continuous sequence because each gap between timestamps (2 to 4, 4 to 7, and 7 to 12) is less than three seconds. This creates a valid 10-second run. The later high values at seconds 20 and 28 are separated by an eight-second gap, so they form a different sequence that is shorter than 10 seconds and is therefore ignored.</p>


Identify tachycardia runs where heart rate stays above 100 bpm for at least 10 seconds and return run start, end, and duration

Detecting ECG Tachycardia Runs

<p>You’re given a string that may contain the characters <strong><code>{</code></strong>, <strong><code>}</code></strong>, <strong><code>[</code></strong>, <strong><code>]</code></strong>, <strong><code>(</code></strong>, and <strong><code>)</code></strong>.</p>

<p><strong>Task:</strong> Verify that the string is balanced. A balanced string is one where every opening character, <strong><code>{</code></strong>, <strong><code>[</code></strong>, or <strong><code>(</code></strong>, has a corresponding closing character, <strong><code>}</code></strong>, <strong><code>]</code></strong>, or <strong><code>)</code></strong>.</p>

<p>Write a function called <strong><code>is_balanced(string: str) -&gt; bool</code></strong> which verifies the balance of a string.</p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;(())[]</span><span class="si">{}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;{([()</span><span class="si">{}</span><span class="s1">])()}&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">True</span>
</span></span></code></pre>

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">is_balanced</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">[]())&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">False</span>
</span></span></code></pre>

<hr/>


Write a function that tests whether a string of brackets is balanced.

The Brackets Problem

<p>How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?</p>


<p>What happens when you average the output on multiple decision trees?</p>


What happens when you average the output on multiple decision trees?

Average Trees

How does random forest generate the forest and why use it over logistic regression

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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