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Machine Learning

Booking Regression

Bank Fraud Model

Random Forest Explanation

Xgboost vs Random Forest

PCA and K-Means

Data Pipelines

Design a data pipeline for hourly user analytics.

Data Pipelines and Aggregation

Let's say that you're in charge of getting payment data into your internal data warehouse. 

Payment Data Pipeline

Aggregating and collecting unstructured data.

Unstructured Data Pipeline (ETL)

Designing a pipeline for ingesting media to built-in search within LinkedIn

Text Search System

Designing an ML system to extract financial insights from market data for improved bank decision-making

Using APIs for Downstream Tasks

Analytics

How would you analyze the dataset to understand exactly where the revenue loss is occurring?

Evaluating Revenue Decline

Describing a data project and its challenges

Hurdles In Data Projects

Statistics

What are the assumptions of linear regression?

Assumptions of Linear Regression

Correlation in Regression

Forecasting & Time Series

Forecasting New Year Revenue

Call Center Resource Management

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

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

Your Strengths and Weaknesses

Brainteasers

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

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

Why Do You Want to Work With Us

Describe a data project you worked on. What were some of the challenges you faced?

What are the assumptions of linear regression?

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

Regression assumptions

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

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

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

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

Average Trees

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?

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

Input of K-means

How do you detect and handle correlation between variables in linear regression? What will happen if you ignore the correlation in the regression model?

Given a <code>users</code> table, write a query to return only its duplicate rows.

Example:

Input:

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

Duplicate Rows

An ecommerce company is experiencing a reduction in revenue for the past 12 months. You have the following transaction data:

<ul>
<li>Date of sale</li>

<li>Total $ Amount paid by customer</li>

<li>Profit Margin per unit</li>

<li>Quantity of item</li>

<li>Item category</li>

<li>Item subcategory</li>

<li>Marketing attribution source</li>

<li>% discount applied</li>
</ul>

How would you analyze the dataset to understand exactly where the revenue loss is occurring?

Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour.

How would you build this data pipeline?

Let’s say that you work on the revenue forecasting team at a company like Facebook.

An executive comes to you asking about how much revenue Facebook will make in the coming year.

How would you forecast revenue for the next year?

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

Between linear regression and random forest regression, which model would perform better and why?

What are the two key concepts of random forest regression?

What are the two key concepts of random forest regression?

Let’s say that you’re in charge of getting payment data into your internal data warehouse.

How would you build an ETL pipeline to get Stripe payment data into the database so analysts can build revenue dashboards and run analytics?

Let’s say you designed an experiment to measure the impact financial rewards have on users’ response rates.

The result shows that the treatment group with $10 rewards has a 30% response rate, while the control group without rewards has a 50% response rate.

Can you explain what happened and how you could improve this experimental design?

Experimental rewards system and ways to improve it

Reward Experiment

A/B Testing

Given a table called <code>employees</code>, get the largest salary of any employee by department

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>department</code></td>
<td>VARCHAR</td>
</tr>

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

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

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

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

Write a query to get the largest salary of any employee by department

Largest Salary by Department

Suppose that you are a machine learning engineer for a large bank, working with the modeling team that creates input data for downstream models. Downstream models can include regulatory models for risk, internal models for credit decisions, internal risk models, marketing models, audits, and any other application where ML can be used.

You have access to the following APIs:

<ul>
<li>Reddit API for all posts and comments for finance and news-related subreddits</li>
<li>Bloomberg API for daily stock prices, including opening and closing prices</li>
</ul>

How would you go about designing an ML system that extracts data from those APIs, transforms the data, and stores it in a format that downstream modeling teams can use?

You are designing an ETL pipeline for a model that uses videos as input.

How would you collect and aggregate data for multimedia information, specifically when it’s unstructured data from videos?

Suppose that you’re building a tool for in-house analytics at LinkedIn. Design a pipeline that ingests images and PDFs of resumes and transforms them into queryable text data.

Your data pipeline will have the following outflows:

<ul>
<li>A data mart that allows machine learning models to tap into the text data for natural language processing.</li>
<li>A data product that analysts in your company use to track keywords.</li>
<li>A search API that allows recruiters to scout candidates using certain keywords.</li>
</ul>

You may assume the following:

<ul>
<li>The image-to-text models are adequately accurate and are ready for use.</li>
<li>Data does not have to be real-time but has to minimize turnaround.</li>
<li>Another team is working with the privacy and security filters. You do not need to consider this in your design.</li>
</ul>

State other assumptions you might have upfront.

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

As a data scientist at a mortgage bank, how would you approach building a predictive model for loan default risk?

Risk Model for a Mortgage Bank

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>

String Shift

Data Structures & Algorithms

Assume you are receiving CSV files intermittently utilizing an SFTP. Design a pipeline to make this data usable.

SFTP Pipeline

Let’s say you work at a company that manages call centers.

Your team wants to build a model to allocate the correct number of agents at any given time to a certain client that has customers that need support.

What kind of model would be best for this situation? What metrics would you check that the model is producing the correct values?

Hint: Is it better to over-allocate or under-allocate? How would you determine which one you’d rather have?

You are required to extract a specific word <code>target_word</code> from an encrypted document. Fortunately, you have the basic information needed to decrypt that document.

The equation used to encrypt the document is as follows:

\[E_n(x) = (I(\mathsf{s}) + n) \mod 26 \]

Where:

<ul>
<li>$\mathsf{s}$ is a letter</li>
<li>$I(x) $ is the number representing the position of the letter in the alphabet (1 for <code>A</code>, 2 for <code>B</code>, etc.)</li>
<li>$n $ is an arbitrary integer.</li>
<li>$E_n(x) $ is the encrypted letter.</li>
</ul>

Return the number of occurrences of <code>target_word</code> in the document after decryption.

Note: The document only has upper case letters and spaces.

Example:

Input:

<pre tabindex="0" class="chroma"><code>encrypted_document = &#39;WKH TXLFN EURZQ IRA MXPSV RYHU WKH ODCB GRJ&#39;
n = 3
target_word = &#39;BROWN&#39;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>extract_word(encrypted_document,n,target_word) -&gt; 1
</code></pre>

Explanation:

After decrypting the document, you will have :

<pre tabindex="0" class="chroma"><code>THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG
</code></pre>

In which the <code>target_word</code> (BROWN) appears only once.

Words in Encrypted String

Let’s say you work at Amazon. It’s nearing Black Friday, and you are tasked with finding out what products should be put on sale.

You have access to historical prices data from products that have been on sale before. How would you determine what products should go on sale to best maximize profit during Black Friday?

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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