Prepare for and practice interview questions from DemystData across topics like Presentation, Product Metrics, Machine Learning and more.

DemystData Interview Questions

DemystData Interview Guides

Data Structures & Algorithms

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

Merge Sorted Lists

Find the missing integer from a array of consequtive integers

Find the Missing Number

Write a function can_shift to return whether or not A can be shifted some number of places to get B

String Shift

Given a list of integers, and an integer N, write a function to find all combinations that sum to the value N.

Sum to N

Minimum Change

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

Write a query to return the two students with the closest test scores and the score difference

Closest SAT Scores

Write a query to get the total three-day rolling average for deposits by day

Rolling Bank Transactions

Paired Products

Machine Learning

How would you encode a categorical variable with thousands of distinct values

Encoding Categorical Features

Interpret coefficients of logistic regression for categorical and boolean variables

Coefficients of Logistic Regression

How would we build a model to detect fraud and text customers to approve or deny fraudulent transactions

Bank Fraud Model

Lasso vs Ridge

Rebalancing outcome probabilities for a classifier on imbalanced data.

Rebalance Probabilities

A/B Testing

How would you assess the validity of the result?

Experiment Validity

Button AB Test

How would you analyze the effectiveness of this new feature?

Green Dot

How would you make a control group and test group to account for network effects

Network Experiment Design

Can you determine if the test will result in bias towards the smaller group?

Sample Size Bias

Product Sense & Metrics

How do we measure the launch of Robinhood’s fractional shares program

Fractional Shares

What’s the expected churn rate in March for all customers that bought the product since January 1st?

Expected Churn

Google Maps Improvement

How would you measure the success of Uber Eats?

Uber Eats Success

What are the top five metrics you would start tracking to understand the health of Google Docs

Docs Metrics

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.

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

Example:

Input:

<code>users</code> table

<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>
<code>neighborhoods</code> table

<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>
Output:

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

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

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<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>
<code>departments</code> table

<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>
Output:

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

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

We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.

Write a query to get the total three-day rolling average for deposits by day.

Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout

Example:

Input:

<code>bank_transactions</code> table

<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>
Output:

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

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

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

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). 

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. 

Example:

Input:

<code>employees</code> table

<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>
<code>departments</code> table

<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>
Output:

<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

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

Bonus: What’s the time complexity?

Example:

Input:

<pre tabindex="0" class="chroma"><code>list1 = [1,2,5]
list2 = [2,4,6]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def merge_list(list1,list2) -&gt; [1,2,2,4,5,6]
</code></pre>

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?

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

Given a table of students and their SAT test scores, write a query to return the two students with the closest test scores with the score difference.

If there are multiple students with the same minimum score difference, select the student name combination that is higher in the alphabet. 

Example:

Input:

<code>scores</code> table

<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>student</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>score</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>other_student</code></td>
<td>VARCHAR</td>
</tr>

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

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.

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

Example:

Input:

<code>bank_transactions</code> table

<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>
Output:

<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

You have an array of integers, <code>nums</code> of length <code>n</code> spanning <code>0</code> to <code>n</code> with one missing. Write a function <code>missing_number</code> that returns the missing number in the array.

Note: Complexity of $O(n)$ required.

Example:

Input:

<pre tabindex="0" class="chroma"><code>nums = [0,1,2,4,5] 
missing_number(nums) -&gt; 3
</code></pre>

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

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

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

Prime to N

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.

How would you assess the validity of the result?

Over budget on a project is defined when the salaries, prorated to the day, exceed the budget of the project.

For example, if Alice and Bob both combined income make 200K and work on a project of a budget of 50K that takes half a year, then the project is over budget given 0.5 * 200K = 100K &gt; 50K.

Write a query to forecast the budget for all projects and return a label of <code>&#34;overbudget&#34;</code> if it is over budget and <code>&#34;within budget&#34;</code> otherwise.

Note: Assume that employees only work on one project at a time.

Example:

Input:

<code>employees</code> table

<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>
<code>projects</code> table

<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>title</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>state_date</code></td>
<td>DATETIME</td>
</tr>

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

<tr>
<td><code>budget</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
departments table

<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>
<code>employee_projects</code> table

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

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

<tr>
<td><code>employee_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
Output:

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

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

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

Write a query to forecast each project's budget and label it overbudget or within budget

Over-Budget Projects

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

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.

Example:

Input:

<code>transactions</code> table

<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>
Output:

<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>

Write a query to find the third purchase of every user sorted by user_id ascending

Third Purchase

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?

Describing a data project and its challenges

Hurdles In Data Projects

Behavioral

Given two strings <code>A</code> and <code>B</code>, write a function <code>can_shift</code> to return whether or not <code>A</code> can be shifted some number of places to get <code>B</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>A = &#39;abcde&#39;
B = &#39;cdeab&#39;
can_shift(A, B) == True

A = &#39;abc&#39;
B = &#39;acb&#39;
can_shift(A, B) == False
</code></pre>

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 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.

How would we build this model?

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?

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?

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

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.

How would you set up this test?

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.

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

Let’s say we are given a table <code>user_logins</code>.

Using this table, calculate how many users logged in an identical number of times on January 1st, 2022.

For example, it may be the case that on that date three users logged in seven times, four users logged in five times, two users logged in ten times, etc.

Example:

Input:

<code>user_logins</code> table

<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>login_date</code></td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
Output:

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

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

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

Find how many users logged in a certain number of times on a given day

Daily Logins

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:

<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>
What query would you write to determine how many different users gave a like on June 6, 2020?

Example:

Input:

<code>events</code> table

<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>
Output

<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

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.

Write a query to get the total amount spent on each item in the ‘purchases’ table by users that registered in 2022.

Example:

Input:

<code>users</code> table

<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>
<code>purchases</code> table

<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>
Output:

<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

Let’s say we have two tables, <code>transactions</code> and <code>products</code>. Hypothetically the <code>transactions</code> table consists of over a billion rows of purchases bought by users.

We are trying to find paired products that are often purchased together by the same user, such as wine and bottle openers, chips and beer, etc..

Write a query to find the top five paired products and their names.

Notes:
 For the purposes of satisfying the test case, p2 should be the item that comes first in the alphabet.
 The <code>qty</code> column represents paired products count

Example:

Input:

<code>transactions</code> table

<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>
<code>products</code> table

<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>
Output:

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

<tbody>
<tr>
<td><code>p1</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>p2</code></td>
<td>VARCHAR</td>
</tr>

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

Given a list of strings of letters from <code>a</code> to <code>z</code>, create a function, <code>sum_alphabet</code>, that returns a list of the alphabet sum of each word in the string.

The alphabet sum is the sum of the ordinal position of each of the string’s letters in the standard English alphabet ordering. So, the letter <code>a</code> will have a value of <code>1</code>, <code>z</code> will have a value of <code>26</code>, and so on.

As an example of the alphabet sum of a string, the string <code>sport</code> will have an <code>alphabet sum of 19 + 16 + 15 + 18 + 20 = 88</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>words = [&#34;sport&#34; , &#34;good&#34; , &#34;bad&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_alphabet(words) -&gt; [88 , 41 , 7]
</code></pre>

Write a function sum_alphabet that returns the alphabet sum of each word in a list of strings

Alphabet Sum

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

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

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

P-value to a Layman I

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

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

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

P-value to a Layman

Statistics

Assume you have a logistic model that is heavily weighted on one variable and that one variable has sample data like 50.00, 100.00, 40.00, etc….

Next let’s assume that there was a data quality issue with that variable and an unknown number of values removed the decimal point. For example 100.00 turned into 10000.

Would the model still be valid? Why or why not? How would you fix the model?

In predicting Airbnb booking prices with a dataset containing thousands of diverse features, which regression model could potentially perform better?

In predicting Airbnb booking prices with a dataset containing thousands of diverse features, which regression model could potentially perform better?

Variable Error

Would a logistic model remain valid if one variable had decimal points accidentally removed, and how would you fix it

There are two tables. One table is called <code>swipes</code> that holds a row for every Tinder swipe and contains a boolean column that determines if the swipe was a right or left swipe called <code>is_right_swipe</code>. The second is a table named <code>variants</code> that determines which user has which variant of an AB test.

Write a SQL query to output the average number of right swipes for two different variants of a feed ranking algorithm by comparing users that have swiped 10, 50, and 100 swipes in a <code>feed_change</code> experiment.

Note: Users have to have swiped at least 10 times to be included in the subset of users to analyze the mean number of right swipes.

Example:

Input:

<code>variants</code> table

<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>experiment</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>variant</code></td>
<td>VARCHAR</td>
</tr>

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

<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>swiped_user_id</code></td>
<td>INTEGER</td>
</tr>

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

<tr>
<td><code>is_right_swipe</code></td>
<td>BOOLEAN</td>
</tr>
</tbody>
</table>
Output:

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

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

<tr>
<td><code>mean_right_swipes</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>swipe_threshold</code></td>
<td>INTEGER</td>
</tr>

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

Write a SQL query to find the average number of right swipes for different ranking algorithms.

Swipe Precision

You have a table containing information about the time that employees have worked on a project.

Write a query to find how many unique calendar days each employee worked. Order your query by the <code>employee_id</code>.

Example:

An employee worked on two projects, and recorded their time like this:

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>start_date</th>
<th>end_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1	</td>
<td>1</td>
<td>2024-01-01</td>
<td>2024-01-06</td>
</tr>

<tr>
<td>1	</td>
<td>2</td>
<td>2024-01-03</td>
<td>2024-01-07</td>
</tr>
</tbody>
</table>
The number of unique calendar days is therefore 7.

<h3>Schema</h3>

Input:

<code>projects</code> table

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

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

<tr>
<td><code>project_id</code>	</td>
<td>	INTEGER	</td>
</tr>

<tr>
<td><code>start_date</code>	</td>
<td>	DATE	</td>
</tr>

<tr>
<td><code>end_date</code>	</td>
<td>	DATE	</td>
</tr>
</tbody>
</table>
Output:

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

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

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

<h3>Example</h3>

Input:

<code>projects</code> table

<table>
<thead>
<tr>
<th>employee_id</th>
<th>project_id</th>
<th>start_date</th>
<th>end_date</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>1</td>
<td>2024-01-01</td>
<td>2024-01-15</td>
</tr>

<tr>
<td>1</td>
<td>1</td>
<td>2024-01-21</td>
<td>2024-01-31</td>
</tr>

<tr>
<td>1</td>
<td>2</td>
<td>2024-01-25</td>
<td>2024-02-28</td>
</tr>

<tr>
<td>2</td>
<td>3</td>
<td>2024-01-01</td>
<td>2024-01-31</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>employee_id</th>
<th>unique_days_worked</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>54</td>
</tr>

<tr>
<td>2</td>
<td>31</td>
</tr>
</tbody>
</table>

Unique Work Days

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

Give an example of the tradeoffs between the two.

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

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

Bagging vs. Boosting

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

Bagging vs Boosting

You’re given two dataframes: <code>transactions</code> and <code>products</code>.

The transactions dataframe contains transaction ids, product ids, and the total amount of each product sold.

The products dataframe contains product ids and prices.

Write a function to return a dataframe containing every transaction with a total value of over $100. Include the total value of the transaction as a new column in the dataframe.

Example:

Input:

<pre tabindex="0" class="chroma"><code>import pandas as pd

transactions = {&#34;transaction_id&#34; : [1, 2, 3, 4, 5], &#34;product_id&#34; : [101, 102, 103, 104, 105], &#34;amount&#34; : [3, 5, 8, 3, 2]}

products = {&#34;product_id&#34; : [101, 102, 103, 104, 105], &#34;price&#34; : [20.00, 21.00, 15.00, 16.00, 52.00]}

df_transactions = pd.DataFrame(transactions)

df_products = pd.DataFrame(products)
</code></pre>

Output:

<table>
<thead>
<tr>
<th>transaction_id</th>
<th>product_id</th>
<th>amount</th>
<th>total_value</th>
</tr>
</thead>

<tbody>
<tr>
<td>2</td>
<td>102</td>
<td>5</td>
<td>105.00</td>
</tr>

<tr>
<td>3</td>
<td>103</td>
<td>8</td>
<td>120.00</td>
</tr>

<tr>
<td>5</td>
<td>105</td>
<td>2</td>
<td>104.00</td>
</tr>
</tbody>
</table>

Write a function to return a dataframe containing every transaction with a total value of over $100.

Over 100 Dollars

Consider a trip from one city to another that may contain many layovers. Given the list of flights out of order, each with a starting city and end city, write a function <code>plan_trip</code> to reconstruct the path of the trip so the trip tickets are in order.

Example:

For a trip from Bombay to Beijing:

Input:

<pre tabindex="0" class="chroma"><code>flights = [
 [&#39;Chennai&#39;, &#39;Bangalore&#39;], 
 [&#39;Bombay&#39;, &#39;Delhi&#39;], 
 [&#39;Goa&#39;, &#39;Chennai&#39;], 
 [&#39;Delhi&#39;, &#39;Goa&#39;], 
 [&#39;Bangalore&#39;, &#39;Beijing&#39;]
]
output = [
 [&#39;Bombay&#39;, &#39;Delhi&#39;], 
 [&#39;Delhi&#39;, &#39;Goa&#39;], 
 [&#39;Goa&#39;, &#39;Chennai&#39;], 
 [&#39;Chennai&#39;, &#39;Bangalore&#39;], 
 [&#39;Bangalore&#39;, &#39;Beijing&#39;],
]
</code></pre>

Reconstruct the path of a trip so that the trip tickets are in order.

Scrambled Tickets

Write a function <code>minimum_change</code> to find the minimum number of coins that make up the given amount of change <code>cents</code>. Assume we only have coins of value 1, 5, 10, and 25 cents.

Example:

Input:

<pre tabindex="0" class="chroma"><code>cents = 73
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def minimum_change(cents) -&gt; 7
#(25 + 25 + 10 + 10 + 1 + 1 + 1)
</code></pre>

You are given two rectangles <code>a</code> and <code>b</code> each defined by four ordered pairs denoting their corners on the <code>x</code>, <code>y</code> plane. Write a function <code>rectangle_overlap</code> to determine whether or not they overlap. Return <code>True</code> if so, and <code>False</code> otherwise.

Note: If the two rectangles border one another, or share a corner like two diagonally adjacent positions on a chessboard, they are said to overlap.

Note: The lists of ordered pairs are in no particular order. The first entry in list <code>a</code> could be the top left corner while the first in list <code>b</code> is the bottom right.

Example:

Input:

<pre tabindex="0" class="chroma"><code>
a = [(-3,5), (-3,2),(0,5),(0,2)]
b = [(-1,4), (3,4), (3,1), (-1,1)]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def rectangle_overlap(a, b) -&gt; True
</code></pre>

As point <code>(0,2)</code> is fully contained in rectangle <code>b</code> and point <code>(-1,4)</code> is fully contained in a rectangle <code>a</code>.

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

DemystData Interview Questions

DemystData Interview Guides

DemystData Interview Questions

Challenge

DemystData Salaries by Position

Discussion & Interview Experiences

Discussion & Interview Experiences