Prepare for and practice interview questions from FanDuel across topics like SQL, Takehome, A/B Testing and more.

FanDuel Interview Questions

FanDuel Interview Guides

Statistics

Compute Deviation

Time on FB Distribution

P-value to a Layman

Compute Variance

Write a function to bootstrap the confidence interface for a list of integers

Bootstrapping Confidence Intervals

Product Sense & Metrics

Group Success

WAU vs Open Rates

Google Maps Improvement

Unified Inbox

Stories Success

Machine Learning

Loan Model

Spam Classifier 

Decision Tree Evaluation

How would you build a model or algorithm to generate respawn locations for an online third person shooter game like Halo?

Video Game Respawn Model

Build a k Nearest Neighbors classification model from scratch.

KNN From Scratch

Data Structures & Algorithms

This problem involves identifying duplicate numbers in a list of integers. The function should return a list of the duplicate numbers.

Find Duplicate Numbers in a List

Create a function that converts each integer in the list into its corresponding Roman numeral representation

Integer to Roman

Write a function to return the value of the nearest node that is a parent to both nodes.

Nearest Common Ancestor

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

Tower of Hanoi

Analytics

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

Marketing Channel Metrics

How would you present the performance of each subscription to an executive?

Analyzing Churn Behavior

Let’s say that you're in charge of an e-commerce D2C business that sells socks. What business health metrics would you care?

D2C Socks e-Commerce

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

Evaluating Revenue Decline

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

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

Write a function <code>compute_deviation</code> that takes in a list of dictionaries with a key and list of integers and returns a dictionary with the standard deviation of each list.

Note: This should be done without using the NumPy built-in functions.

Example:

Input:

<pre tabindex="0" class="chroma"><code>input = [
 {
 &#39;key&#39;: &#39;list1&#39;,
 &#39;values&#39;: [4,5,2,3,4,5,2,3],
 },
 {
 &#39;key&#39;: &#39;list2&#39;,
 &#39;values&#39;: [1,1,34,12,40,3,9,7],
 }
]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code> output = {&#39;list1&#39;: 1.12, &#39;list2&#39;: 14.19}
</code></pre>

Let’s say that you’re a data scientist on the engagement team. A product manager comes up to you and says that the weekly active users metric is up 5% but email notification open rates are down 2%.

What would you investigate to diagnose what’s happening?

Email Open Rate

One of your favorite sports teams is playing at a local stadium, but you waited until the last minute to buy a ticket.

You can buy a scalped (second-hand) ticket for $50, which has a 20% chance of not working. If the scalped ticket doesn’t work, you’ll have to buy a box office ticket for $70 at the stadium.

<ol>
<li>How much do you expect to pay to go to the sports game?</li>
<li>How much money should you set aside for the game?</li>
</ol>

Scalped Ticket

Probability

Given that $X$ and $Y$ are independent random variables with normal distributions, what is the mean and variance of the distribution of $2X - Y$ when the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the mean of the distribution of $2X - Y$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the mean of the distribution of $2X - Y$?

Distribution of 2X - Y I

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the variance of the distribution of $2X - Y$?

Given that $X$ and $Y$ are independent random variables with normal distributions, and the corresponding distributions are $X\sim \mathcal{N}(3, 4)$ and $Y \sim \mathcal{N}(1, 4)$, what is the variance of the distribution of $2X - Y$?

Distribution of 2X - Y II

Distribution of 2X - Y

How would you measure the success of Facebook Groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Considering the purpose of Facebook Groups as a platform for connecting users through shared interests or real-life relationships, which metrics would be appropriate to measure the success of these groups?

Blizzard Entertainment operates a global network of games and services, each generating massive volumes of real-time gameplay and infrastructure events, everything from player actions in Overwatch to server status updates for World of Warcraft. You are tasked with building a centralized system to reliably ingest, process, and distribute these events across the company for analytics, live operations, and player-facing features.

How would you architect the data ingestion and processing pipeline for this event system?

Design a scalable event data pipeline for real-time, multi-application ingestion and processing.

Centralized Event Ingestion

Data Pipelines

You are given a binary tree of unique positive numbers. Each node in the binary tree is represented as a dictionary with the following keys:

<ul>
<li><code>data</code>: integer value stored in the node</li>
<li><code>left</code>: reference to the left child (or <code>None</code>)</li>
<li><code>right</code>: reference to the right child (or <code>None</code>)</li>
</ul>

<pre tabindex="0" class="chroma"><code>node = {
 &#34;data&#34;: 6,
 &#34;left&#34;: {
 &#34;data&#34;: 3,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 },
 &#34;right&#34;: {
 &#34;data&#34;: 9,
 &#34;left&#34;: {...},
 &#34;right&#34;: {...}
 }
}
</code></pre>

Given two node values as input (<code>value1</code> and <code>value2</code>), write a function to return the value of the nearest common ancestor (lowest node in the tree that has both nodes as descendants).

Note: If one of the nodes doesn’t exist in the tree, return <code>-1</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code># Diagram of the binary tree
&#39;&#39;&#39;
 6 
 / \ 
 3 9 
 / \
 2 11
 / \
 5 8
&#39;&#39;&#39;
value1 = 8
value2 = 2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>common_ancestor(root,value1,value2) -&gt; 3
</code></pre>

Explanation:

<ul>
<li>Ancestors of <code>8</code>: <code>11 → 3 → 6</code></li>
<li>Ancestors of <code>2</code>: <code>3 → 6</code></li>
<li>Common ancestors: <code>3</code> and <code>6</code></li>
<li>The nearest common ancestor is <code>3</code>.</li>
</ul>

You’re an analyst at Ubisoft Gaming. Leadership wants to track Weekly Active Paying Users (WAPU), players who record at least one game session and at least one in-app purchase in the same calendar week.

Write a SQL query that returns, for each week in the dataset:

<ol>
<li><code>week_start</code>- the Monday of that calendar week 
</li>
<li><code>wapu</code>- the number of distinct users who qualify as WAPU in that week 
</li>
</ol>

Note: Assume all timestamps are in UTC. Output should be ordered by <code>week_start</code> ascending.

<h3>Schema:</h3>

Input:

<code>game_sessions</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>session_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>session_start</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>country</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>purchases</code> table

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

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

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

<tr>
<td>purchase_time</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>amount</td>
<td>DECIMAL(10,2)</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>week_start</td>
<td>TIMESTAMP</td>
</tr>

<tr>
<td>wapu</td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

<h3>Example:</h3>

Input:

<code>game_sessions</code> table

<table>
<thead>
<tr>
<th>user_id</th>
<th>session_id</th>
<th>session_start</th>
<th>country</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>101</td>
<td>2023-10-02</td>
<td>USA</td>
</tr>

<tr>
<td>2</td>
<td>102</td>
<td>2023-10-03</td>
<td>USA</td>
</tr>

<tr>
<td>1</td>
<td>103</td>
<td>2023-10-04</td>
<td>USA</td>
</tr>

<tr>
<td>3</td>
<td>104</td>
<td>2023-10-05</td>
<td>USA</td>
</tr>
</tbody>
</table>
<code>purchases</code> table

<table>
<thead>
<tr>
<th>purchase_id</th>
<th>user_id</th>
<th>purchase_time</th>
<th>amount</th>
</tr>
</thead>

<tbody>
<tr>
<td>201</td>
<td>1</td>
<td>2023-10-02</td>
<td>5.99</td>
</tr>

<tr>
<td>202</td>
<td>2</td>
<td>2023-10-03</td>
<td>10.00</td>
</tr>

<tr>
<td>203</td>
<td>1</td>
<td>2023-10-04</td>
<td>2.99</td>
</tr>

<tr>
<td>204</td>
<td>3</td>
<td>2023-10-05</td>
<td>7.49</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td>week_start</td>
<td>2023-10-02</td>
</tr>

<tr>
<td>wapu</td>
<td>3</td>
</tr>
</tbody>
</table>
Explanation:

All sessions and purchases occur during the same calendar week:

<ul>
<li>Week start: Monday 2023-10-02</li>
</ul>

Users who qualify as WAPU (≥1 session AND ≥1 purchase in the same week):

<ul>
<li>User 1 - sessions on Oct 2 &amp; 4; purchases on Oct 2 &amp; 4</li>
<li>User 2 - session on Oct 3; purchase on Oct 3</li>
<li>User 3 - session on Oct 5; purchase on Oct 5</li>
</ul>

Total WAPU = 3 users

How do you calculate weekly active paying users (WAPU) by joining gameplay and purchase activity by week?

Track Your Most Valuable Gamers

Let’s say that you’re the PM on Google Maps.

<ol>
<li>How would you improve Google Maps?</li>

<li>What metrics would you check to see if your feature improvements are successful?</li>
</ol>

Given a table of <code>subscriptions</code>, write a query to get the retention rate of each monthly cohort for each <code>plan_id</code> for the three months after sign-up.

Order your output by <code>start_month</code>, <code>plan_id</code>, then <code>num_month</code>.

If an <code>end_date</code> is in the same month as <code>start_date</code> we say the subscription was not retained in the first month.

If the <code>end_date</code> occurs in the month after the month of <code>start_date</code>, the subscription was not retained in the second month. And so on for the third.

The <code>end_date</code> field is <code>NULL</code> if the user has not canceled.

<h3>Schema:</h3>

Input:

<code>subscriptions</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>start_date</code></td>
<td>DATETIME</td>
</tr>

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

<tr>
<td><code>plan_id</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

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

<tbody>
<tr>
<td><code>start_month</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>num_month</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>plan_id</code></td>
<td>VARCHAR</td>
</tr>

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

<h3>Example</h3>

Input:

<code>subscriptions</code> table

<table>
<thead>
<tr>
<th>user_id</th>
<th>start_date</th>
<th>end_date</th>
<th>plan_id</th>
</tr>
</thead>

<tbody>
<tr>
<td>1</td>
<td>2020-01-10 00:00:00</td>
<td>2020-03-31 23:59:59</td>
<td>plan1</td>
</tr>

<tr>
<td>2</td>
<td>2020-01-10 00:00:00</td>
<td>2020-02-01 23:59:59</td>
<td>plan1</td>
</tr>

<tr>
<td>3</td>
<td>2020-01-10 00:00:00</td>
<td>2020-07-31 23:59:59</td>
<td>plan1</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>start_month</th>
<th>num_month</th>
<th>plan_id</th>
<th>retained</th>
</tr>
</thead>

<tbody>
<tr>
<td>2020-01-01</td>
<td>1</td>
<td>plan1</td>
<td>1.0</td>
</tr>

<tr>
<td>2020-01-01</td>
<td>2</td>
<td>plan1</td>
<td>0.67</td>
</tr>

<tr>
<td>2020-01-01</td>
<td>3</td>
<td>plan1</td>
<td>0.33</td>
</tr>
</tbody>
</table>
Explanation:

<ul>
<li>Cohort: All subscriptions starting in January 2020.</li>
<li>Month 1 retention: All 3 users were active in the first month → 3⁄3 = 1.0</li>
<li>Month 2 retention: Only users 1 and 3 were active in Feb → 2⁄3 ≈ 0.67</li>
<li>Month 3 retention: Only user 3 was active in Mar → 1⁄3 ≈ 0.33</li>
</ul>

Subscription Retention

Write a function that takes an input as the number of tosses and a probability of heads and return a list of randomly generated results equal in length to the number of tosses. Each result represents the outcome of a coin toss, where ‘H’ represents heads and ’T’ represents tails.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>tosses = 5
probability_of_heads = 0.6
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>coin_toss(tosses, probability_of_heads) -&gt; [&#39;H&#39;, &#39;T&#39;, &#39;H&#39;, &#39;H&#39;, &#39;T&#39;]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>tosses = 3
probability_of_heads = 0.2
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>coin_toss(tosses, probability_of_heads) -&gt; [&#39;T&#39;, &#39;T&#39;, &#39;T&#39;]
</code></pre>

The output may vary due to the randomness of coin tosses.

Simulate a series of coin tosses given the number of tosses and the probability of getting heads.

Simulating Coin Tosses

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.

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

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

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

Given a list of integers, identify all the duplicate values in the list. Assume that the list can contain both positive and negative numbers, and the order of the list does not matter. A number is considered a duplicate if it appears more than once in the list. Return a list of the duplicate numbers.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 2, 3, 1, 2, 3]
</code></pre>

Output:

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

The numbers 1, 2, and 3 all appear more than once in the list, so they are considered duplicates.

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, -1, 2, 3, 3, -1]
</code></pre>

Output:

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

The numbers -1 and 3 both appear more than once in the list, so they are considered duplicates. Note that the order of the output does not matter.

Example 3:

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>find_duplicates(nums) -&gt; []
</code></pre>

None of the numbers in the list appear more than once, so there are no duplicates.

Given the integer list nums with length n, create a function that converts each integer in the list into its corresponding Roman numeral representation. The Roman numeral symbols and corresponding values are provided in the table below. The function must be able to handle integers up to 1000.

<table>
<thead>
<tr>
<th>Symbols</th>
<th>Values</th>
</tr>
</thead>

<tbody>
<tr>
<td>I</td>
<td>1</td>
</tr>

<tr>
<td>IV</td>
<td>4</td>
</tr>

<tr>
<td>V</td>
<td>5</td>
</tr>

<tr>
<td>IX</td>
<td>9</td>
</tr>

<tr>
<td>X</td>
<td>10</td>
</tr>

<tr>
<td>XL</td>
<td>40</td>
</tr>

<tr>
<td>L</td>
<td>50</td>
</tr>

<tr>
<td>XC</td>
<td>90</td>
</tr>

<tr>
<td>C</td>
<td>100</td>
</tr>

<tr>
<td>CD</td>
<td>400</td>
</tr>

<tr>
<td>D</td>
<td>500</td>
</tr>

<tr>
<td>CM</td>
<td>900</td>
</tr>

<tr>
<td>M</td>
<td>1000</td>
</tr>
</tbody>
</table>

<h3>Examples:</h3>

Input:

<pre tabindex="0" class="chroma"><code>nums = [1, 4, 179]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>[&#34;I&#34;, &#34;IV&#34;, &#34;CLXXIX&#34;]
</code></pre>

Let’s say you have a time series dataset grouped monthly for the past five years.

How would you find out if the difference between this month and the previous month was significant or not?

Significance Time Series

Forecasting & Time Series

Let’s say you’re working on a building a better search engine for Google. You build it and want to see if it serves better results than the existing one in production.

How would you determine which search engine performed better? Which metrics would you track?

Comparing Search Engines

A/B Testing

Let’s say we’re testing a new UI with the goal to increase conversion rates. We test it by giving the new UI to a random subset of users.

The test variant wins by 5% on the target metric. What would you expect to happen after the new UI is applied to all users? Will the metric actually go up by ~5%, more, or less?

Note: Assume there is no novelty effect.

New UI Effect

How would you build a model or algorithm to generate respawn locations for an online third person shooter game like Halo?

When designing an algorithm to generate respawn locations in an online game, what aspects must be considered to ensure long-term player engagement?

When designing an algorithm to generate respawn locations in an online game, what aspects must be considered to ensure long-term player engagement?

Video Game Respawn Model I

Why should respawn locations in a game like Halo not be constant?

Why should respawn locations in a game like Halo not be constant?

Video Game Respawn Model II

You are given an array of numerical values, bootstrap samples, and size for a confidence interval.

Write a function that performs bootstrap sampling on the given array and calculates the confidence interval based on the given size.

Note: The function should return a tuple containing the minimum and maximum values of the confidence interval rounded to the tenths place.

Example

Input:

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

Output

<pre tabindex="0" class="chroma"><code>bootstrap_conf_interval(values, 1000, 0.95) -&gt; (1.2, 4.8)
</code></pre>

In this case, the function returns a tuple indicating that we are 95% confident that the population parameter lies between 1.2 and 4.8 based on our bootstrap samples.

Note: Results may vary due to the randomness of bootstrap sampling.

Build a $k$ Nearest Neighbors classification model from scratch with the following conditions:

<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 NOT <code>scikit-learn</code></li>
</ul>

Example:

Input:

<pre tabindex="0" class="chroma"><code>k = 5
new_point = [0.5,-2,8]
print(data)
...
 Var1 Var2 Var3 Target
0 -3.279536 3.362223 2.847892 2
1 -0.791565 1.742475 2.151587 2
2 -0.785992 -0.938681 -0.459770 0
3 -1.068190 1.461051 0.127130 3
4 -0.367568 -0.870240 -0.225734 0
.. ... ... ... ...
95 -1.327175 1.971085 -0.690689 2
96 -3.203714 1.847649 0.778901 2
97 -0.587640 0.647458 2.094385 2
98 0.363644 -0.509795 2.514191 1
99 -0.673498 2.955285 2.102122 4

[100 rows x 4 columns]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def kNN(k, new_point, data) -&gt; 2
</code></pre>

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

What do you think the distribution of time spent per day on Facebook looks like? What metrics would you use to describe that distribution?

Write a function that outputs the (sample) variance given a list of integers.

Note: round the result to 2 decimal places.

Example:

Input:

<pre tabindex="0" class="chroma"><code>test_list = [6, 7, 3, 9, 10, 15]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>get_variance(test_list) -&gt; 13.89
</code></pre>

What is a confidence interval for a statistic? Why is it useful to know the confidence interval for a statistic and how do you calculate it?

What are confidence intervals and how are they useful

Confidence Interval Explanation

Tower of Hanoi is a fun puzzle game that consists of a set of <code>3</code> pegs (<code>A</code>, <code>B</code>, <code>C</code>) and <code>n</code> number of disks. You are required to move all disks from the source peg <code>A</code> to destination peg <code>C</code>, under the following conditions:

<ul>
<li>You can only move one disk for each action.</li>
<li>You can only move the disk onto the top of another peg; you cannot place it off to the side or under existing disks.</li>
<li>You cannot place a larger disk onto a smaller disk.</li>
</ul>

Initially, all disks are on the source <code>A</code> peg and sorted by decreasing width from bottom to the top (widest on the bottom, smallest on the top).

You must develop an algorithm to move all disks from <code>A</code> to the destination peg <code>C</code> with the optimal solution. Also, return every step made from the starting state to the final state.

<img src="https://i.ibb.co/my0Vm13/initail.png" alt="alt"/>

<img src="https://i.ibb.co/0Ftzs7D/final.png" alt="alt"/>

Example

Input:

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

Output:

<pre tabindex="0" class="chroma"><code>#return list of {&#34;A&#34;:[],&#34;B&#34;:[],&#34;C&#34;:[]} 
tower_of_hanoi_solver(n) -&gt; [
 {&#34;A&#34;: [1, 2, 3], &#34;B&#34;: [], &#34;C&#34;: []},
 {&#34;A&#34;: [2, 3], &#34;B&#34;: [], &#34;C&#34;: [1]},
 {&#34;A&#34;: [3], &#34;B&#34;: [2], &#34;C&#34;: [1]},
 {&#34;A&#34;: [3], &#34;B&#34;: [1, 2], &#34;C&#34;: []},
 {&#34;A&#34;: [], &#34;B&#34;: [1, 2], &#34;C&#34;: [3]},
 {&#34;A&#34;: [1], &#34;B&#34;: [2], &#34;C&#34;: [3]},
 {&#34;A&#34;: [1], &#34;B&#34;: [], &#34;C&#34;: [2, 3]},
 {&#34;A&#34;: [], &#34;B&#34;: [], &#34;C&#34;: [1, 2, 3]}
 ]
</code></pre>

What is the minimal number of steps needed to solve the Tower of Hanoi problem with n disks?

What is the minimal number of steps needed to solve the Tower of Hanoi problem with *n* disks?

Linear Data Structures

Let’s say that you’re in charge of Square’s small business division. 

The CEO wants to hire a customer success manager to help manage a new software product. Another executive thinks that it’s worth just instituting a free trial instead.

What would be your recommendation on utilizing a customer success manager versus just a free trial to get new or existing customers to use the new product?

Customer Success vs. Free Trial

Business Case

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

Let’s say that you’re in charge of an e-commerce D2C business that sells socks.

What business health metrics would you care about tracking on a company dashboard?

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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