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Data Structures & Algorithms

Get Top N Frequent Words

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

Prime to N

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

Append Frequency

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

The Brackets Problem

Machine Learning

Lasso vs Ridge

Bagging vs Boosting

Random Forest Explanation

Regularization and Validation

choosing k value during k-means clustering

Choosing k

Statistics

MLE vs MAP

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

Data Pipelines

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

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?

Describing a data project and its challenges

Hurdles In Data Projects

Analytics

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

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

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

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

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

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

String Palindromes

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>

Select the 2nd highest salary in the engineering department

2nd Highest Salary

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

Xgboost vs Random Forest

<h1>Binary Tree Validation</h1>

You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

A valid BST is defined as follows:

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.

<h3>Example:</h3>

Input:

<img src="/s3/questions/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

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

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

Tell me about a project in which you had to clean and organize a large dataset.

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on \(O(n)\) time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</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>

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

One of the basic steps in parsing string inputs is to check whether the opening and closing characters or tags match, i.e., to check if each opened parenthesis is closed eventually.

Given a list of strings, write a <code>string</code> <code>parser</code> that verifies the integrity of the parenthesis used in the string. The parser should return a list of booleans stating whether that string’s integrity was verified.

Example:

Input:

<pre tabindex="0" class="chroma"><code>list_of_strings = [
 &#39;f(x) + g(x)&#39;, 
 &#39;sin(exp(x)}&#39;, 
 &#39;((())just some string)&#39;, 
 &#39;(4,{(3,4):x**2)&#39;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def string_parser(list_of_strings) -&gt; [True, False, True, True]
</code></pre>

Given a list of strings, verify whether the parentheses in each string are properly opened and closed.

Check Matching Parentheses

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

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

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

Example:

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;(())[]{}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{([(){}])()}&#39;) -&gt; True
</code></pre>

<pre tabindex="0" class="chroma"><code>is_balanced(&#39;{}[]())&#39;) -&gt; False
</code></pre>

<hr/>

Let’s say you’re analyzing an AB test that has both a test group and a control group.

<ol>
<li>How do you calculate the sample size necessary for an accurate measurement?</li>

<li>Let’s say that the sample size is similar and sufficient between the two groups. In order to measure very small differences between the two, should the power get bigger or smaller?</li>
</ol>

A/B Test Power Size

A/B Testing

What does the backpropagation algorithm do in the context of neural networks? What is the informal intuition behind the algorithm? What are some drawbacks of the algorithm compared to other optimization methods?

Bonus: Formally derive the backpropagation algorithm and prove that it does what it claims to do.

Backpropagation Explanation

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

You are given a dictionary with two keys <code>a</code> and <code>b</code> that hold integers as their values.

Without declaring any other variable, swap the value of <code>a</code> with the value of <code>b</code> and vice versa.

Note: Return the dictionary after editing it.

Example:

Input:

<pre tabindex="0" class="chroma"><code>numbers = {
 &#39;a&#39;:3,
 &#39;b&#39;:4
}
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def swap_values(numbers) -&gt; {&#39;a&#39;:4,&#39;b&#39;:3}
</code></pre>

Swap Variables

Write a function <code>fib</code> which takes an integer <code>n</code> and returns the nth fibonacci number. For this question, <code>fib</code> MUST be recursively defined.

Note: <code>for</code> and <code>while</code> keywords are disabled for this question, as well as <code>functools</code>. Any workarounds around this restriction is prohibited.

Example:

Input:

<pre tabindex="0" class="chroma"><code>n = 4
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def fib(n) -&gt; 3
# the sequence is [1, 1, 2, 3, 5, 8...]
</code></pre>

Implement a dynamic recursive fibonacci function.

Impossibly Iterative Fibonacci

Implement the k-means clustering algorithm in python from scratch, given the following:

<ul>
<li>A two-dimensional NumPy array <code>data_points</code> that is an arbitrary number of data points (rows) <code>n</code> and an arbitrary number of columns <code>m</code>.</li>
<li>Number of k clusters <code>k</code>.</li>
<li>The initial centroids value of the data points at each cluster <code>initial_centroids</code>.</li>
</ul>

Return a list of the cluster of each point in the original list data_points with the same order (as a integer).

Example

<img src="https://i.ibb.co/KKp2gPn/kemans-before.png" alt="before clustering"/>

After clustering the points with two clusters, the points will be clustered as follows.

<img src="https://i.ibb.co/bdwVWxR/kemans-example-after.png" alt="after clustering"/>

Note: There could be an infinite number of separating lines in this example.

Example

<pre tabindex="0" class="chroma"><code>
#Input
data_points = [(0,0),(3,4),(4,4),(1,0),(0,1),(4,3)]
k = 2
initial_centroids = [(1,1),(4,5)]


#Output 

k_means_clustering(data_points,k,initial_centroids) -&gt; [0,1,1,0,0,1]

</code></pre>

<img src="https://i.ibb.co/pRsRz12/example.png" alt=""/>

Implement the k-means clustering algorithm in python from scratch

k-Means from Scratch

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

Return the ordered pair (precision, recall).

Example:

Input:

<pre tabindex="0" class="chroma"><code>
P = [[121, 9],
 [17, 144]]
</code></pre>

where P represents the following table

<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>
Output:

<pre tabindex="0" class="chroma"><code>def precision_recall(P) -&gt; (0.931, 0.877,)
</code></pre>

Write a function to calculate precision and recall metrics.

Precision and Recall

<ol>
<li>Given an example paragraph string and an integer <code>N</code>, write a function <code>n_frequent_words</code> that returns the top <code>N</code> frequent words in the posting and the frequencies for each word.</li>

<li>What’s the function run-time?</li>
</ol>

Example:

Input:

<pre tabindex="0" class="chroma"><code>posting = &#34;&#34;&#34;
Herbal sauna uses the healing properties of herbs in combination with distilled water. 
The water evaporates and distributes the effect of the herbs throughout the room. 
A visit to the herbal sauna can cause real miracles, especially for colds. 
&#34;&#34;&#34; 
n = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>n_frequent_words(posting,N) = [
 (&#39;the&#39;, 6), 
 (&#39;herbal&#39;, 2), 
 (&#39;sauna&#39;, 2),
]
</code></pre>

Singly-linked lists are a type of linked list where nodes have only one pointer: A pointer to the next element. Create a class <code>SinglyLinkedList</code> along with its methods:

<ul>
<li><code>add_head(element)</code> - adds an element to the head of the list. This becomes the new head of the linked list.</li>
<li><code>add_tail(element)</code> - adds an element to the tail of the list. This becomes the new tail of the linked list.</li>
<li><code>remove_head() -&gt; element</code> - removes the head and returns the value of the removed element.</li>
<li><code>remove_tail() -&gt; element</code> - removes the tail and returns the value of the removed element.</li>
<li><code>__contains__(item) -&gt; bool</code> - checks if the item is inside the list. Returns <code>True</code> if it is inside the list and returns <code>False</code> if not. This method is implemented to allow the use of Python’s <code>in</code> operator.</li>
<li><code>__getitem__(index)</code> - returns the element at the specified index. Raises an <code>IndexError</code> if the index is out of bounds. This method is implemented to allow the use of Python’s <code>[]</code> operator.</li>
<li><code>__len__()</code> - returns the length of the <code>SinglyLinkedList</code>.</li>
</ul>

Write a class 'SinglyLinkedList' that implements the interface required for a Singly Linked List.

Singly Linked List

Deep learning models are popular but have some drawbacks in that they’re expensive to train and maintain.

In some contexts, using other simpler models may make more sense. One possible alternative for classification problems are support vector machines (SVM).

<ul>
<li>When are SVMs preferable to deep learning models?</li>

<li>What are the pros and cons of using an SVM compared to deep or non-deep learning classification models (such as logistic regression)?</li>
</ul>

When you should consider using Support Vector Machine rather then Deep learning models

Support Vector Machines vs Deep Learning Models

Given the head of a singly linked list represented as a <code>ListNode</code>, and two zero-indexed positions <code>x</code> and <code>y</code>, write a function <code>swap_node</code> which swaps the positions of nodes <code>x</code> and <code>y</code> and returns the new head. Note that you cannot simply swap the values of these nodes, as these nodes do not have values. Instead, you must swap these two using pointer manipulation (i.e., modifying the <code>next</code> references of the involved nodes inside the list).

A <code>ListNode</code> is defined as:

<pre tabindex="0" class="chroma"><code>class ListNode:
 next: ListNode = None
</code></pre>

Example:

Input:

<pre tabindex="0" class="chroma"><code>linked_list = ListNode(0x01) -&gt; ListNode(0x02) -&gt; ListNode(0x03) 
# Here, imagine that &#39;0x0_&#39; refer to their memory locations.
head = 0x01
x = 1
y = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def swap_node(head, x, y) -&gt; ListNode(0x01) -&gt; ListNode(0x03) -&gt; ListNode(0x02)
</code></pre>

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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