Prepare for and practice interview questions from Syngenta across topics like Analytics, Machine Learning, SQL and more.

Syngenta Interview Questions

Syngenta Interview Guides

Machine Learning

Write a function calculate_rmse to calculate the root mean squared error from y_pred and y_true

RMS Error

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

Bagging vs Boosting

What are the logistic and softmax functions? What is the difference between the two?

Softmax vs Logistic

How would you evaluate whether a decision tree is the right model and assess its performance before and after deployment

Decision Tree Evaluation

How does random forest generate the forest and why use it over logistic regression

Random Forest Explanation

Data Structures & Algorithms

Write a function moving_window to find the moving window average

Moving Window

Given a json string with nested objects, write a function that flattens all the objects to a single key-value dictionary.

Flatten JSON

Given a list of strings, write a function that returns the longest common prefix

Common Prefix

Write a function to return True if two strings are anagrams of each other and False if they are not

Valid Anagram

Write a function digit_accumulator that returns the sum of every digit in a string

Digit Accumulator

Last Transaction

Write a SQL query that creates a cumulative distribution of the number of comments per user with bin buckets of one

Cumulative Distribution

Given a users table, write a query to get the cumulative number of new users added by the day, with the total reset every month. 

Cumulative Reset

Subscription Retention

Write a SQL query to count transactions filtered by several criterias.

Count Transactions

Behavioral

Describing a data project and its challenges

Hurdles In Data Projects

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

How to present complex data insights with clarity and adaptability tailored to a specific audience

Presentations and Insights

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

Your Strengths and Weaknesses

Making data-driven insights actionable for those without technical expertise

Simple Explanations

Statistics

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

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

Explain what an unbiased estimator is and provide a layman-friendly example

Unbiased Estimator

When would you use metrics like the mean and median?

Best Measure

Write a function to calculate the cdf from a list pdf and round numbers to 3 digits

Density to Cumulative

Given the two tables, write a SQL query that creates a cumulative distribution of the number of comments per user. Assume bin buckets class intervals of one.

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

<tr>
<td><code>neighborhood_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>sex</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<code>comments</code> table

<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>body</code></td>
<td>VARCHAR</td>
</tr>

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

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

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

<tr>
<td><code>cum_total</code></td>
<td>FLOAT</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>

Let’s say you work at an advertising firm. You have a table of users’ impressions of ad campaigns over time. Each <code>impression_id</code> consists of values of user engagement specified by <code>Excited</code>, <code>OK</code>, and <code>Bored</code>.

Write a query to find all users who were at some point “Excited” and have never been “Bored” with a campaign.

Example:

Input:

<code>ad_impressions</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>dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>campaign_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>impression_id</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>
Output:

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

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

Write a query to find all users that were at some point "Excited" and have never been "Bored" with a campaign.

Always Excited Users

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

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’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 you’re interning at a fintech startup that’s building a platform for online games of chance. During testing, one of your simulations flips a coin 10 times and gets 8 tails and 2 heads.

How would you determine if the coin is fair, and what statistical approach would you use to support your conclusion?

How would you assess if a coin is fair after observing 8 tails in 10 flips?

Fair Coin

Probability

Given a <code>users</code> table, write a query to get the cumulative number of new users added by the day, with the total reset every month. 

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

<table>
<thead>
<tr>
<th>DATE</th>
<th>INTEGER</th>
</tr>
</thead>

<tbody>
<tr>
<td>2020-01-01</td>
<td>5</td>
</tr>

<tr>
<td>2020-01-02</td>
<td>12</td>
</tr>

<tr>
<td>…</td>
<td>…</td>
</tr>

<tr>
<td>2020-02-01</td>
<td>8</td>
</tr>

<tr>
<td>2020-02-02</td>
<td>17</td>
</tr>

<tr>
<td>2020-02-03</td>
<td>23</td>
</tr>
</tbody>
</table>

Given a list of numbers <code>nums</code> and an integer <code>window_size</code>, write a function <code>moving_window</code> to find the moving window average.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1,2,3,4,5,6]
window_size = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def moving_window(input, window_size) -&gt; [1, 1.5, 2, 3, 4, 5]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>nums = [1,2,3,4,5,6]
window_size = 4
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def moving_window(input, window_size) -&gt; [1, 1.5, 2, 2.5, 3.5, 4.5]
</code></pre>

Given a <code>JSON</code> string with nested objects, write a function <code>flatten_json</code> that flattens all the objects to a single key-value dictionary. Do not use the library that actually performs this function.

Example:

Input:

<pre tabindex="0" class="chroma"><code>import json
json_str = json.dumps({&#39;a&#39;:{&#39;b&#39;:&#39;c&#39;, &#39;d&#39;:&#39;e&#39;}})
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def flatten_json(json_str) -&gt; json.dumps({&#39;a_b&#39;:&#39;c&#39;, &#39;a_d&#39;:&#39;e&#39;})
</code></pre>

Note: Input and output are in <code>string</code> format : use <code>json.dumps()</code> to convert python dictionary to string.

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>

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

A group of 100 students are participating in a coin-tossing challenge at a school event. Each student flips a fair coin once:
- If the coin lands on heads, the student wins and truthfully reports their win.
- If the coin lands on tails, the student flips again:
 - If the second flip is heads, the student lies and claims they won, even though they lost.
 - If the second flip is tails, the student truthfully reports their loss.

At the end of the game, 30 students report that they won. As an analyst, you are tasked with determining the expected number of students who actually won the game (i.e., got heads on their first flip).

Example:

Suppose only 8 students report winning. How many students would you expect to have truly won based on the rules above?

Note: You may assume each coin flip is independent and fair (probability of heads = 0.5).

Given win reports in a coin toss game with lying, estimate the true number of winners.

Secret Wins

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 two strings, write a function to return <code>True</code> if the strings are anagrams of each other and <code>False</code> if they are not.

Note: A word is not an anagram of itself.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;listen&#34;
string_2 = &#34;silent&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>True
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>string_1 = &#34;banana&#34;
string_2 = &#34;bandana&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>False
</code></pre>

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?

You are given a list of integers called <code>numbers</code>. Write a function to return any subset of <code>numbers</code> where the elements <code>sum</code> to <code>zero</code> and that does not contain the number <code>0</code>.

If there are no combinations of elements that sum to zero, return an empty list.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>numbers = [1,-2,6,7,1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def zero_sum(numbers) -&gt; [1,-2,1] # only possible combination
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>numbers = [0,0,1,3,6,-4,-1]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def zero_sum(numbers) -&gt; [1,3,-4]
</code></pre>

or

<pre tabindex="0" class="chroma"><code>def zero_sum(numbers) -&gt; [1,-1]
</code></pre>

or any other subset that sums to zero and doesn’t include <code>0</code>.

Write a function to return any subset of the input list where the elements sum to zero and that does not contain the number 0.

Sum to Zero

Let’s say we have an <code>annual_payments</code> table. Answer the following three questions via SQL queries and output them as a table with the answers to each question.

<ol>
<li>How many total transactions are in this table?</li>
<li>How many different users made transactions?</li>
<li>How many transactions listed as <code>&#34;paid&#34;</code> have an amount greater or equal to 100?</li>
<li>Which product made the highest revenue? (use only transactions with a <code>&#34;paid&#34;</code> status)</li>
</ol>

Example:

Input:

<code>annual_payments</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>amount</code></td>
<td>FLOAT</td>
</tr>

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

<tr>
<td><code>status</code></td>
<td>VARCHAR</td>
</tr>

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

<tr>
<td><code>amount_refunded</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>product</code></td>
<td>VARCHAR</td>
</tr>

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

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

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

<tr>
<td><code>answer</code></td>
<td>TEXT</td>
</tr>
</tbody>
</table>

Write a function <code>calculate_rmse</code> to calculate the root mean squared error of a regression model. The function should take in two lists, one that represents the predictions <code>y_pred</code> and another with the target values <code>y_true</code>.

Input:

Example 1: 

<pre tabindex="0" class="chroma"><code>y_pred = [3,4,5]
y_true = [3,4,5]

def calculate_rmse(y_pred,y_true) -&gt; 0
</code></pre>

Example 2:

<pre tabindex="0" class="chroma"><code>y_pred = [3,4,5]
y_true = [5,4,3]

def calculate_rmse(y_pred,y_true) -&gt; 1.63
</code></pre>

Write a function, <code>search_list</code>, that returns a boolean indicating if the <code>target</code> value is in the <code>linked_list</code> or not.

You receive the head of the linked list, which is a dictionary with the following keys:
- <code>value</code>: contains the value of the node.
- <code>next</code>: contains the next node in the list, or <code>None</code>.

If the linked list is empty, you’ll receive <code>None</code> since there is no head node for an empty list.

Example:

Input:

<pre tabindex="0" class="chroma"><code>target = 2
linked_list = 3 -&gt; 2 -&gt; 5 -&gt; 6 -&gt; 8 -&gt; None
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>search_list(target, linked_list) -&gt; True
</code></pre>

Write a function that returns a boolean indicating if a value is in the linked list.

Search Linked List

You work at a ride-sharing company and are given a table containing information about the rides of users.

Write a query to get the duration (in minutes) of each ride longer than two hours. Sort the results by duration in descending order.

<code>rides</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>passenger_user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>start_dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>end_dt</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>distance</code></td>
<td>FLOAT</td>
</tr>

<tr>
<td><code>ride_region</code></td>
<td>VARCHAR</td>
</tr>

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

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

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

Write a query to get the duration in minutes of each ride longer than two hours, sorted descending

Time Difference

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

Given a list of integers, write a function <code>gcd</code> to find the greatest common denominator between them.

Example:

Input:

<pre tabindex="0" class="chroma"><code>int_list = [8, 16, 24]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def gcd(int_list) -&gt; 8
</code></pre>

Which of the following is true about the greatest common division (gcd) between three integers a, b, and c?

Which of the following is true about the greatest common division (gcd) between three integers a, b, and c?

Greatest Common Denominator Properties

Write a function gcd to find the greatest common denominator of a list of integers

Greatest Common Denominator

What are the logistic and softmax functions? What is the difference between the two?

What makes them useful for use in logistic regression?

In logistic regression, how is the predicted class determined based on the logistic function?

In logistic regression, how is the predicted class determined based on the logistic function?

Softmax vs Logistic II

What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

What is the key characteristic of the logistic function that makes it useful for mapping continuous values to a probability?

Softmax vs Logistic I

What is an unbiased estimator and can you provide an example for a layman to understand?

What makes an estimator of a population statistic unbiased?

Given a list of strings, write a function that returns their longest common prefix. For example, if you were given the strings <code>&#34;flowers&#34;</code>, <code>&#34;flow&#34;</code>, and <code>&#34;flight&#34;</code>, your function should return the string <code>&#34;fl&#34;</code>.

If the list of strings has no common prefix, return an empty string.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>strings = [&#34;flowers&#34;, &#34;flow&#34;, &#34;flight&#34;] 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>&#34;fl&#34;
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>strings = [&#34;showboat&#34;, &#34;showcase&#34;, &#34;shower&#34;]
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>&#34;show&#34;
</code></pre>

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

How would you explain the concept of linear regression to three different audiences: a child, a first-year college student, and a seasoned mathematician? Ensure your explanations are tailored to the understanding level of each audience.

Describe linear regression to various audiences with different levels of knowledge.

Explaining Linear Regression to Different Audiences

Say you have a dataset and you are asked to analyze it by looking at some metrics like the mean and median.

<ol>
<li>When would you use one measure over the other?</li>

<li>How do you calculate the confidence interval of each measure?</li>
</ol>

Let’s say that you’re a data scientist working for Facebook.

You’ve been asked to generate a machine learning model that can map the legal first name of a person to likely nicknames they might have.

How do you go about designing this model?

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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