Prepare for and practice interview questions from The Clorox Company.

The Clorox Company Interview Questions

The Clorox Company Interview Guides

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 query to identify customers who placed more than three transactions each in both 2019 and 2020

Customer Orders

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

Cumulative Distribution

Write a query to get the average order value by gender rounded to two decimal places

Average Order Value

Machine Learning

Which model would perform better and why?

Booking Regression

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

Bagging vs Boosting

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

Random Forest Explanation

How would you build a restaurant recommender on Facebook and what are the downfalls or worries?

Restaurant Recommender

Explain the relationship between PCA and K-means clustering

PCA and K-Means

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

What challenges might you face when deploying a new model to production and how would you address them?

Production Rollout Challenges

Data Structures & Algorithms

Given a list of strings, write a function that returns the longest common prefix

Common Prefix

Write a function to return True if two strings are anagrams of each other and False if they are not

Valid Anagram

Find Mismatched Words

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Business Case

Friend Requests Down

How would you investigate a 10% drop in usage on Google Docs

Google Docs Drop

When would someone rationally buy a more expensive version of an identical product?

Identical Pen Pricing

<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>Write a query to identify customers who placed more than three transactions each in both 2019 and 2020.</p>

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

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

<p><code>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>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><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>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>customer_name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


<p>Which of the following queries will return a list of customers and the total amount they have spent on orders?</p>


Which of the following queries will return a list of customers and the total amount they have spent on orders?

<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>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>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>Write a query to show the number of users, number of transactions placed, and total order amount per month in the year 2020. Assume that we are only interested in the monthly reports for a single year (January-December).</p>

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

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

<p><code>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>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><code>products</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>

<tr>
<td><code>price</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<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>VARCHAR</td>
</tr>

<tr>
<td><code>sex</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>month</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>num_customers</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>num_orders</code></td>
<td>INTEGER</td>
</tr>

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


Write a query to show the number of users, transactions, and total order amount per month in 2020

Monthly Customer Report

<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>As an accountant for a local grocery store, you have been tasked with calculating the daily sales of each product since their last restocking.</p>

<p>You have been provided with three tables: <code>products</code>, <code>sales</code>, and <code>restocking</code>. The <code>products</code> table contains information about each product, the <code>sales</code> table records the sales transactions, and the <code>restocking</code> table tracks the restocking events.</p>

<p>Write a query to retrieve the running total of sales for each product since its last restocking event.</p>

<p><strong><em>Notes:</em></strong> <em>The <code>sales_since_last_restock</code> column represents the cumulative sales of the product for each day since its last restocking event. Order the result set by <code>product_id</code>.</em></p>

<p>Example:</p>

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

<p>Table <code>products</code>:</p>

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

<tbody>
<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>product_name</td>
<td>varchar</td>
</tr>
</tbody>
</table>
<p>Table <code>sales</code>:</p>

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

<tbody>
<tr>
<td>sales_id</td>
<td>int</td>
</tr>

<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>date</td>
<td>date</td>
</tr>

<tr>
<td>sold_count</td>
<td>int</td>
</tr>
</tbody>
</table>
<p>Table <code>restocking</code>:</p>

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

<tbody>
<tr>
<td>restock_id</td>
<td>int</td>
</tr>

<tr>
<td>product_id</td>
<td>int</td>
</tr>

<tr>
<td>restock_date</td>
<td>date</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

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

<tbody>
<tr>
<td>product_name</td>
<td>varchar</td>
</tr>

<tr>
<td>date</td>
<td>date</td>
</tr>

<tr>
<td>sales_since_last_restock</td>
<td>int</td>
</tr>
</tbody>
</table>


Calculate daily sales of each product since last restocking.

Cumulative Sales Since Last Restocking

<p>Let’s say that on Facebook composer, the posting tool, drops from 3% posts per user last month to 2.5% post per user today. </p>

<p>How would you investigate what happened?</p>

<p>Let’s say the drop is in photo posts. What would you investigate now?</p>


<p>In the analysis of a drop in photo posts per user on a social media site like Facebook, which factor(s) could influence the change in the average number of posts per user?</p>


In the analysis of a drop in photo posts per user on a social media site like Facebook, which factor(s) could influence the change in the average number of posts per user?

Post Composer Drop

How would you investigate the drop in Facebook composer posts per user, especially photo posts

Product Sense & Metrics

<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>You’re a data analyst working for Burlington studying customer behavior during the annual Black Friday shopping spree. Each customer may visit the store multiple times throughout the day and make several purchases across different product categories. The marketing team wants to understand which customer segments were most engaged.</p>

<p>Write a SQL query to compute the following metrics for each product category:</p>

<ol>
<li>Total number of unique customers who purchased from that category</li>
<li>Average spend per customer (rounded to 2 decimal places)</li>
<li>Percentage of customers who made more than one purchase in that category (rounded to 2 decimal places)</li>
</ol>

<p>Sort the final results in descending order by average spend per customer.</p>

<p><strong><em>Notes:</em></strong></p>

<ul>
<li><em>A “purchase” means a single row in the purchases table.</em></li>
<li><em>A customer who buys multiple items in one visit still counts as multiple purchases.</em></li>
<li><em>If a customer purchased from a category only once, they should not count toward the “multiple purchase” percentage.</em></li>
<li><em>Every category present in the purchases table should appear in the output.</em></li>
</ul>

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

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

<p><strong><code>purchases</code> table</strong></p>

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

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

<tr>
<td>customer_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>category</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>amount</td>
<td>DECIMAL</td>
</tr>

<tr>
<td>purchase_time</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>category</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>unique_customers</td>
<td>INTEGER</td>
</tr>

<tr>
<td>avg_spend_per_customer</td>
<td>DECIMAL</td>
</tr>

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

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

<p><strong><code>purchases</code> table</strong></p>

<table>
<thead>
<tr>
<th>purchase_id</th>
<th>customer_id</th>
<th>category</th>
<th>amount</th>
<th>purchase_time</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>101</td>
<td>Electronics</td>
<td>120</td>
<td>2025-11-28 09:10:00</td>
</tr>

<tr>
<td>2</td>
<td>101</td>
<td>Electronics</td>
<td>80</td>
<td>2025-11-28 10:00:00</td>
</tr>

<tr>
<td>3</td>
<td>102</td>
<td>Clothing</td>
<td>40</td>
<td>2025-11-28 11:15:00</td>
</tr>

<tr>
<td>4</td>
<td>103</td>
<td>Electronics</td>
<td>300</td>
<td>2025-11-28 13:30:00</td>
</tr>

<tr>
<td>5</td>
<td>102</td>
<td>Electronics</td>
<td>150</td>
<td>2025-11-28 14:10:00</td>
</tr>

<tr>
<td>6</td>
<td>102</td>
<td>Clothing</td>
<td>10</td>
<td>2025-11-28 16:20:00</td>
</tr>

<tr>
<td>7</td>
<td>103</td>
<td>Clothing</td>
<td>50</td>
<td>2025-11-28 17:20:00</td>
</tr>
</tbody>
</table>
<p><strong>Output</strong></p>

<table>
<thead>
<tr>
<th>category</th>
<th>unique_customers</th>
<th>avg_spend_per_customer</th>
<th>pct_multi_purchase_customers</th>
</tr>
</thead>

<tbody>
<tr>
<td>Electronics</td>
<td>3</td>
<td>216.67</td>
<td>33.33</td>
</tr>

<tr>
<td>Clothing</td>
<td>2</td>
<td>50.00</td>
<td>50.00</td>
</tr>
</tbody>
</table>
<p><strong>Explanation</strong></p>

<p>For Electronics, there are three unique customers: 101, 102, and 103. Their spend amounts are 200 for customer 101 (120 + 80), 150 for customer 102, and 300 for customer 103, giving an average spend of 216.67. Only customer 101 made more than one purchase, so the multi-purchase rate is 33.33%.</p>

<p>For Clothing, there are two unique customers: 102 and 103. Customer 102 spent a total of 50 (40 + 10), while customer 103 spent 50, resulting in an average of 50.00. Customer 102 is the only one with multiple purchases, leading to a multi-purchase rate of 50.00%.</p>


Write a SQL query to compute metrics for each product category.

Black Friday Shopping Spree

<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>Given the <code>transactions</code> table, write a query to get the max quantity purchased for each distinct <code>product_id</code>, every year. </p>

<p>The output should include the <code>year</code>, <code>product_id</code>, and <code>max_quantity</code> for that product sorted by year and <code>product_id</code> ascending.</p>

<h3>Schema:</h3>

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

<p><code>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>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>Column</th>
<th>Type</th>
</tr>
</thead>

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

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

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

<h3>Example</h3>

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

<table>
<thead>
<tr>
<th>id</th>
<th>user_id</th>
<th>created_at</th>
<th>product_id</th>
<th>quantity</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>59</td>
<td>2019-10-05 00:00:00</td>
<td>500</td>
<td>1</td>
</tr>

<tr>
<td>2</td>
<td>84</td>
<td>2019-08-11 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>3</td>
<td>21</td>
<td>2020-01-25 00:00:00</td>
<td>500</td>
<td>3</td>
</tr>

<tr>
<td>4</td>
<td>47</td>
<td>2019-12-25 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>5</td>
<td>100</td>
<td>2020-03-05 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>6</td>
<td>45</td>
<td>2019-06-30 00:00:00</td>
<td>500</td>
<td>1</td>
</tr>

<tr>
<td>7</td>
<td>44</td>
<td>2020-07-07 00:00:00</td>
<td>500</td>
<td>1</td>
</tr>

<tr>
<td>8</td>
<td>38</td>
<td>2020-09-23 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>9</td>
<td>45</td>
<td>2020-03-08 00:00:00</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>10</td>
<td>16</td>
<td>2020-04-22 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>11</td>
<td>17</td>
<td>2020-06-06 00:00:00</td>
<td>999</td>
<td>2</td>
</tr>

<tr>
<td>12</td>
<td>41</td>
<td>2020-09-17 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>13</td>
<td>34</td>
<td>2019-07-05 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>14</td>
<td>78</td>
<td>2020-02-16 00:00:00</td>
<td>999</td>
<td>3</td>
</tr>

<tr>
<td>15</td>
<td>16</td>
<td>2020-05-22 00:00:00</td>
<td>999</td>
<td>4</td>
</tr>

<tr>
<td>16</td>
<td>4</td>
<td>2020-05-10 00:00:00</td>
<td>999</td>
<td>2</td>
</tr>

<tr>
<td>17</td>
<td>25</td>
<td>2019-12-15 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>18</td>
<td>71</td>
<td>2020-05-03 00:00:00</td>
<td>999</td>
<td>1</td>
</tr>

<tr>
<td>19</td>
<td>3</td>
<td>2020-08-03 00:00:00</td>
<td>999</td>
<td>2</td>
</tr>

<tr>
<td>20</td>
<td>19</td>
<td>2019-03-13 00:00:00</td>
<td>999</td>
<td>5</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>year</th>
<th>product_id</th>
<th>max_quantity</th>
</tr>
</thead>

<tbody>
<tr>
<td>2019</td>
<td>500</td>
<td>2</td>
</tr>

<tr>
<td>2019</td>
<td>999</td>
<td>5</td>
</tr>

<tr>
<td>2020</td>
<td>500</td>
<td>3</td>
</tr>

<tr>
<td>2020</td>
<td>999</td>
<td>4</td>
</tr>
</tbody>
</table>


Max Quantity

<p>Given three tables, representing customer transactions and customer attributes:</p>

<p>Write a query to get the average order value by gender.</p>

<p><em>Note: We’re looking at the average order value by users that have ever placed an order. Additionally, please round your answer to two decimal places.</em></p>

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

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

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

<tr>
<td><code>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><code>products</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>

<tr>
<td><code>price</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>sex</code></td>
<td>VARCHAR</td>
</tr>

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


<p>There was a robbery from the ATM at the bank where you work. Some unauthorized withdrawals were made, and you need to help your bank find out more about those withdrawals.</p>

<p>However, the only information you have is that there was more than 1 withdrawal, they were all performed in 10-second gaps, and no legitimate transactions were performed in between two fraudulent withdrawals.</p>

<p>We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value <code>transaction_value</code>, and <code>created_at</code> time for each transaction.</p>

<p>Write a query to retrieve all user IDs in ascending order whose transactions have exactly a 10-second gap from one another.</p>

<p><em>Note: Assume that there are only withdrawals from the ATM, which are denoted with a positive <code>transaction_value</code></em></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>user_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>For given table <code>bank_transactions</code>:</p>

<table>
<thead>
<tr>
<th><code>user_id</code></th>
<th><code>created_at</code></th>
<th><code>transaction_value</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2017-01-01 10:10:15</td>
<td>10</td>
</tr>

<tr>
<td>2</td>
<td>2017-01-01 11:11:11</td>
<td>20</td>
</tr>

<tr>
<td>3</td>
<td>2017-01-01 12:12:12</td>
<td>10</td>
</tr>

<tr>
<td>4</td>
<td>2017-01-01 10:10:20</td>
<td>30</td>
</tr>

<tr>
<td>5</td>
<td>2017-01-01 10:10:34</td>
<td>40</td>
</tr>

<tr>
<td>6</td>
<td>2017-01-01 11:11:21</td>
<td>50</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

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

<tbody>
<tr>
<td>2</td>
</tr>

<tr>
<td>6</td>
</tr>
</tbody>
</table>


There was a robbery from the ATM at the bank where you work. Some unauthorized withdrawals were made, and you need to help your bank find out more about those withdrawals.

ATM Robbery

<p>What is the difference between covariance and correlation? Provide an example.</p>


Explain the difference between covariance and correlation and provide an example

Covariance vs Correlation

Statistics

<p>Let’s say you’re given all the different marketing channels along with their respective marketing costs at a company called Mode, that sells B2B analytics dashboards.</p>

<p>What metrics would you use to determine the value of each marketing channel?</p>


<p>When determining the value of each marketing channel for the company Mode, which metric is considered the key metric?</p>

When determining the value of each marketing channel for the company Mode, which metric is considered the key metric?

Marketing Channel Metrics

What metrics would you use to determine the value of each marketing channel?

Analytics

<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>


<p>Let’s say we want to build a model to predict booking prices on Airbnb.</p>

<p>Between linear regression and random forest regression, which model would perform better and why?</p>


<p>What are the two key concepts of random forest regression?</p>

What are the two key concepts of random forest regression?

<p>Given a 2-D matrix <strong>P</strong> of predicted values and actual values, write a function <strong>precision_recall</strong> to calculate precision and recall metrics.</p>

<p>Return the ordered pair <strong>(precision, recall)</strong>.</p>

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

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="n">P</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">121</span><span class="p">,</span> <span class="mi">9</span><span class="p">],</span>
</span></span><span class="line"><span class="cl">    <span class="p">[</span><span class="mi">17</span><span class="p">,</span> <span class="mi">144</span><span class="p">]]</span>
</span></span></code></pre>

<p>where <strong>P</strong> represents the following table</p>

<table>
<thead>
<tr>
<th></th>
<th>Predicted =TRUE</th>
<th>Predicted =FALSE</th>
</tr>
</thead>

<tbody>
<tr>
<td>Actual =TRUE</td>
<td>121</td>
<td>9</td>
</tr>

<tr>
<td>Actual =FALSE</td>
<td>17</td>
<td>144</td>
</tr>
</tbody>
</table>
<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">precision_recall</span><span class="p">(</span><span class="n">P</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span><span class="mf">0.931</span><span class="p">,</span> <span class="mf">0.877</span><span class="p">,)</span>
</span></span></code></pre>


Write a function to calculate precision and recall metrics.

Precision and Recall

<p>Write a query to get the cost of all transactions by user ordered by total cost descending.</p>

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

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

<p><code>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>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><code>products</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>

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

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

<tr>
<td><code>total_cost</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><em>Note: Round the total cost to 2 decimal places.</em></p>


Write a query to get the cost of all transactions by user ordered by total cost descending

Total Transactions

<p>Given a table containing data for monthly sales, write a query to find the total amount of each product sold for each month with each product as its own column in the output table.</p>

<h3>Schema</h3>

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

<p><code>monthly_sales</code> table</p>

<table>
<thead>
<tr>
<th><code>month</code></th>
<th><code>product_id</code></th>
<th><code>amount_sold</code></th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>2</td>
<td>300</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>3</td>
<td>200</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>4</td>
<td>250</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th><strong>month</strong></th>
<th><strong>1</strong></th>
<th><strong>2</strong></th>
<th><strong>3</strong></th>
<th><strong>4</strong></th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>100</td>
<td>0</td>
<td>300</td>
<td>0</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>0</td>
<td>200</td>
<td>0</td>
<td>0</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>250</td>
</tr>
</tbody>
</table>

<h3>Example</h3>

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

<p><strong><code>monthly_sales</code> table</strong></p>

<table>
<thead>
<tr>
<th>month</th>
<th>product_id</th>
<th>amount_sold</th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>2</td>
<td>0</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>3</td>
<td>250</td>
</tr>

<tr>
<td>2021-01-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>1</td>
<td>50</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>2</td>
<td>200</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>3</td>
<td>0</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>1</td>
<td>400</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>2</td>
<td>150</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>3</td>
<td>200</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>1</td>
<td>150</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>2</td>
<td>200</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>3</td>
<td>50</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>4</td>
<td>300</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>1</td>
<td>100</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>2</td>
<td>150</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>3</td>
<td>0</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>4</td>
<td>300</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>month</th>
<th>1</th>
<th>2</th>
<th>3</th>
<th>4</th>
</tr>
</thead>

<tbody>
<tr>
<td>2021-01-01</td>
<td>100</td>
<td>0</td>
<td>250</td>
<td>300</td>
</tr>

<tr>
<td>2021-02-01</td>
<td>50</td>
<td>200</td>
<td>0</td>
<td>300</td>
</tr>

<tr>
<td>2021-03-01</td>
<td>400</td>
<td>150</td>
<td>200</td>
<td>300</td>
</tr>

<tr>
<td>2021-04-01</td>
<td>150</td>
<td>200</td>
<td>50</td>
<td>300</td>
</tr>

<tr>
<td>2021-05-01</td>
<td>100</td>
<td>150</td>
<td>0</td>
<td>300</td>
</tr>
</tbody>
</table>


Monthly Product Sales

<p>What are the assumptions of linear regression?</p>


Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

What are the assumptions of linear regression?

Assumptions of Linear Regression

<p>Given two strings, write a function to return <code>True</code> if the strings are anagrams of each other and <code>False</code> if they are not.</p>

<p><em>Note: A word is not an anagram of itself.</em></p>

<p><strong>Example 1:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">string_1</span> <span class="o">=</span> <span class="s2">&#34;listen&#34;</span>
</span></span><span class="line"><span class="cl"><span class="n">string_2</span> <span class="o">=</span> <span class="s2">&#34;silent&#34;</span>
</span></span></code></pre>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="kc">True</span>
</span></span></code></pre>

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

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">string_1</span> <span class="o">=</span> <span class="s2">&#34;banana&#34;</span>
</span></span><span class="line"><span class="cl"><span class="n">string_2</span> <span class="o">=</span> <span class="s2">&#34;bandana&#34;</span>
</span></span></code></pre>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="kc">False</span>
</span></span></code></pre>


<p>Let’s say that you work for an online media company. The media company is starting to monetize its web traffic and wants to experiment with adding web banners into the middle of its reading content to see if it can monetize effectively.</p>

<p>How would you measure the success of the banner ad strategy?</p>


How would you measure the success of a banner ad strategy?

Banner Ad Strategy Success

A/B Testing

<p>Given a list of strings, write a function that returns their longest common prefix. For example, if you were given the strings <code>&#34;flowers&#34;</code>, <code>&#34;flow&#34;</code>, and <code>&#34;flight&#34;</code>, your function should return the string <code>&#34;fl&#34;</code>.</p>

<p>If the list of strings has no common prefix, return an empty string.</p>

<p><strong>Example 1:</strong></p>

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">strings</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&#34;flowers&#34;</span><span class="p">,</span> <span class="s2">&#34;flow&#34;</span><span class="p">,</span> <span class="s2">&#34;flight&#34;</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="s2">&#34;fl&#34;</span>
</span></span></code></pre>

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

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

<pre tabindex="0" class="chroma"><code><span class="line"><span class="cl"><span class="n">strings</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&#34;showboat&#34;</span><span class="p">,</span> <span class="s2">&#34;showcase&#34;</span><span class="p">,</span> <span class="s2">&#34;shower&#34;</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="s2">&#34;show&#34;</span>
</span></span></code></pre>


<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

<p>Build a $k$ Nearest Neighbors classification model from scratch with the following conditions:</p>

<ul>
<li>Use Euclidian distance (aka, the “2 norm”) as your closeness metric</li>
<li>Your function should be able to handle data frames of arbitrary many rows and columns</li>
<li>If there is a tie in the class of the $k$ nearest neighbors, rerun the search using $k-1$ neighbors instead</li>
<li>You may use <code>pandas</code> and <code>numpy</code> but <strong><em>NOT</em></strong> <code>scikit-learn</code></li>
</ul>

<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">k</span> <span class="o">=</span> <span class="mi">5</span>
</span></span><span class="line"><span class="cl"><span class="n">new_point</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">8</span><span class="p">]</span>
</span></span><span class="line"><span class="cl"><span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
</span></span><span class="line"><span class="cl"><span class="o">...</span>
</span></span><span class="line"><span class="cl">        <span class="n">Var1</span>      <span class="n">Var2</span>      <span class="n">Var3</span>  <span class="n">Target</span>
</span></span><span class="line"><span class="cl"><span class="mi">0</span>  <span class="o">-</span><span class="mf">3.279536</span>  <span class="mf">3.362223</span>  <span class="mf">2.847892</span>       <span class="mi">2</span>
</span></span><span class="line"><span class="cl"><span class="mi">1</span>  <span class="o">-</span><span class="mf">0.791565</span>  <span class="mf">1.742475</span>  <span class="mf">2.151587</span>       <span class="mi">2</span>
</span></span><span class="line"><span class="cl"><span class="mi">2</span>  <span class="o">-</span><span class="mf">0.785992</span> <span class="o">-</span><span class="mf">0.938681</span> <span class="o">-</span><span class="mf">0.459770</span>       <span class="mi">0</span>
</span></span><span class="line"><span class="cl"><span class="mi">3</span>  <span class="o">-</span><span class="mf">1.068190</span>  <span class="mf">1.461051</span>  <span class="mf">0.127130</span>       <span class="mi">3</span>
</span></span><span class="line"><span class="cl"><span class="mi">4</span>  <span class="o">-</span><span class="mf">0.367568</span> <span class="o">-</span><span class="mf">0.870240</span> <span class="o">-</span><span class="mf">0.225734</span>       <span class="mi">0</span>
</span></span><span class="line"><span class="cl"><span class="o">..</span>       <span class="o">...</span>       <span class="o">...</span>       <span class="o">...</span>     <span class="o">...</span>
</span></span><span class="line"><span class="cl"><span class="mi">95</span> <span class="o">-</span><span class="mf">1.327175</span>  <span class="mf">1.971085</span> <span class="o">-</span><span class="mf">0.690689</span>       <span class="mi">2</span>
</span></span><span class="line"><span class="cl"><span class="mi">96</span> <span class="o">-</span><span class="mf">3.203714</span>  <span class="mf">1.847649</span>  <span class="mf">0.778901</span>       <span class="mi">2</span>
</span></span><span class="line"><span class="cl"><span class="mi">97</span> <span class="o">-</span><span class="mf">0.587640</span>  <span class="mf">0.647458</span>  <span class="mf">2.094385</span>       <span class="mi">2</span>
</span></span><span class="line"><span class="cl"><span class="mi">98</span>  <span class="mf">0.363644</span> <span class="o">-</span><span class="mf">0.509795</span>  <span class="mf">2.514191</span>       <span class="mi">1</span>
</span></span><span class="line"><span class="cl"><span class="mi">99</span> <span class="o">-</span><span class="mf">0.673498</span>  <span class="mf">2.955285</span>  <span class="mf">2.102122</span>       <span class="mi">4</span>
</span></span><span class="line"><span class="cl">
</span></span><span class="line"><span class="cl"><span class="p">[</span><span class="mi">100</span> <span class="n">rows</span> <span class="n">x</span> <span class="mi">4</span> <span class="n">columns</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">kNN</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">new_point</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="mi">2</span>
</span></span></code></pre>


Build a k Nearest Neighbors classification model from scratch.

KNN From Scratch

<p>Let’s say we work at L’Oréal, and the e-commerce team has noticed that about 8% of online beauty product orders are delivered late each week.</p>

<p>How would you frame this as a data science problem, choose an algorithm to address it, and describe your plan for deploying the solution?</p>


How would you solve for late deliveries using data science at L'Oréal?

Late Deliveries

Supply Chain

<p>Let’s say you’re leading the analytics for a $100,000 digital marketing campaign to drive new signups for a subscription app. The campaign runs across both paid social and search channels over 30 days.</p>

<p>What metrics would you focus on to evaluate its effectiveness, and how would you use data to optimize performance during the campaign?</p>


How would you use metrics and data to evaluate and improve a digital marketing campaign?

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

The Clorox Company Interview Questions

The Clorox Company Interview Guides

The Clorox Company Interview Questions

Challenge

The Clorox Company Salaries by Position

Discussion & Interview Experiences

Discussion & Interview Experiences