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Machine Learning

Missing Housing Data

85% vs 82%

Spam Classifier 

Classification and Regression

choosing k value during k-means clustering

Choosing k

Statistics

P-value to a Layman

Reducing Error Margin

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

Brainteasers

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

Why Do You Want to Work With Us

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

Your Strengths and Weaknesses

Analytics

Describing a data project and its challenges

Hurdles In Data Projects

Data Structures & Algorithms

Transform a dataframe containing a list of user IDs and their full names into one that contains only the user ids and the first name of each user.

First Names Only

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

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

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

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

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

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

PCA and K-Means

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years but found that around 20% of the listings are missing square footage data.

How do we deal with the missing data to construct our model?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?

We want to build a model to predict housing prices in the city of Seattle. We’ve scraped 100K sold listings over the past three years, but have discovered that around 20% of the listings are missing square footage data.

How would you approach dealing with this missing data, in order to construct the most useful predictive model possible?



How would you choose the k value when using k-means clustering?

Let’s say you are tasked with building a spam classifier for emails. Assume that you’ve built a V1 of the model.

What metrics would you use to track accuracy and validity of the model?

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

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

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

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

Neural Net Use Cases

Justify a Neural Network

Let’s say we have a sample size of \(n\). The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

Let's say we have a sample size of N. The margin of error for our sample size is 3.

How many more samples would we need to decrease the margin of error to 0.3?

You’re given a dataframe containing a list of user IDs and their full names (e.g. ‘James Emerson’).

Transform this dataframe into a dataframe that contains the user ids and only the first name of each user.

Example:

Input:

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

<tbody>
<tr>
<td>1034</td>
<td>James Emerson</td>
</tr>

<tr>
<td>9430</td>
<td>Fiona Woodward</td>
</tr>

<tr>
<td>7281</td>
<td>Alvin Gross</td>
</tr>

<tr>
<td>5264</td>
<td>Deborah Handler</td>
</tr>

<tr>
<td>8995</td>
<td>Leah Xue</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>1034</td>
<td>James</td>
</tr>

<tr>
<td>9430</td>
<td>Fiona</td>
</tr>

<tr>
<td>7281</td>
<td>Alvin</td>
</tr>

<tr>
<td>5264</td>
<td>Deborah</td>
</tr>

<tr>
<td>8995</td>
<td>Leah</td>
</tr>
</tbody>
</table>

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

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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