Google Data Scientist Interview Questions (Step-by-Step Solutions!)
Are you preparing for a Google Data Scientist interview? Congratulations!
A data scientist role at Google is one of the most sought-after positions in the tech industry. This is because data scientists at Google are responsible for some of the most important projects at the company, such as developing algorithms to improve search results, improving Google Maps, and developing new features for products like Gmail and YouTube.
To land a data scientist role at Google, you will need to ace the interview process. In this article, we will go over some of the most common questions asked in a Google Data Scientist interview, along with step-by-step solutions to help you prepare.
Overview of the Data Science Interview
The data science interview process at Google is divided into two parts:
Part 1: The first part of the interview process is designed to test your technical skills. You will be asked questions about SQL, statistics, product, analysis, and machine learning similar to product management and data analyst interviews.
Part 2: The second part of the interview process is designed to assess your problem-solving abilities. You will be asked questions about real-world data sets and will be expected to come up with solutions to problems that data scientists typically face.
Now that we have an overview of the interview process, let’s dive into some specific questions you may be asked in each part of the interview.
Questions You May Be Asked in Part 1 of the Interview
In the first part of the interview, you will be asked questions about your technical skills. Here are some examples of questions you may be asked:
1. What is SQL?
2. How would you calculate the median in SQL?
3. What is a decision tree?
4. How would you use a decision tree to predict whether or not a customer will churn?
5. What is gradient boosting?
6. How would you use gradient boosting to improve the accuracy of a machine learning model?
Solutions to Google Data Scientist Interview Questions Part 1
Now that we’ve gone over some examples of questions you may be asked in a Google Data Scientist interview, let’s look at some specific solutions to these questions.
1. What is SQL?
SQL (Structured Query Language) is a programming language that is used to manipulate and query data stored in databases. SQL can be used to add, delete, and update data in a database, as well as to retrieve data from a database.
2. How would you calculate the median in SQL?
To calculate the median in SQL, you would use the AVG() function. This function takes all of the values in a column and calculates the average value.
3. What is a decision tree?
A decision tree is a type of machine learning model that is used to predict the value of a target variable. Decision trees are created by splitting the data set into smaller and smaller subsets, until each subset contains only one data point.
4. How would you use a decision tree to predict whether or not a customer will churn?
To use a decision tree to predict whether or not a customer will churn, you would need to train the model on data that includes information about customers who have already churned. Once the model is trained, you can then use it to predict whether or not new customers will churn.
5. What is gradient boosting?
Gradient boosting is a type of machine learning algorithm that is used to improve the accuracy of a machine learning model. Gradient boosting works by training a series of weak models, and then combining the predictions of these models to create a final prediction.
6. How would you use gradient boosting to improve the accuracy of a machine learning model?
To use gradient boosting to improve the accuracy of a machine learning model, you would need to train a series of weak models on different subsets of the data. You can then combine the predictions of these models to create a final prediction that is more accurate than the predictions of any individual model.
Questions You May Be Asked in Part 2 of the Interview
In the second part of the interview, you will be asked questions about your problem-solving abilities. Here are some examples of questions you may be asked:
1. Given a data set, how would you go about finding the most important factors that contribute to customer churn?
2. How would you develop a machine learning model to predict whether or not a customer will purchase a product?
3. You are given a data set that contains information about when users click on ads. How would you use this data to optimize ad campaigns?
4. You are given a data set that contains transaction data from an eCommerce website. How would you use this data to increase conversion rates?
5. Write a query to find out the third-highest mountain name for each country. Please make sure to order the country in ASC order.
Solutions to Google Data Scientist Interview Questions Part 2
1. Given a data set, how would you go about finding the most important factors that contribute to customer churn?
There are many ways to find the most important factors that contribute to customer churn. One way would be to use a decision tree to identify the most important features. Another way would be to use a technique like gradient boosting to find the most important features.
2. How would you develop a machine learning model to predict whether or not a customer will purchase a product?
To develop a machine learning model to predict whether or not a customer will purchase a product, you would need to train the model on data that includes information about customers who have already purchased the product. Once the model is trained, you can then use it to predict whether or not new customers will purchase the product.
3. You are given a data set that contains information about when users click on ads. How would you use this data to optimize ad campaigns?
There are many ways to use this data to optimize ad campaigns. One way would be to use the data to create a model that predicts when users are more likely to click on ads. Another way would be to use the data to segment users into groups based on their likelihood of clicking on ads.
4. You are given a data set that contains transaction data from an eCommerce website. How would you use this data to increase conversion rates?
There are many ways to use this data to increase conversion rates. One way would be to use the data to create a model that predicts which users are more likely to purchase products. Another way would be to use the data to segment users into groups based on their likelihood of purchasing products.
5. Write a query to find out the third-highest mountain name for each country. Please make sure to order the country in ASC order.
Table: mountains
+---------------------+------+-------------+
|name |height|country |
+---------------------+------+-------------+
|Denalli |20310 |United States|
|Saint Elias |18008 |United States|
|Foraker |17402 |United States|
|Pico de Orizab |18491 |Mexico |
|Popocatépetl |17820 |Mexico |
|Iztaccihuatl |17160 |Mexico |
+---------------------+------+-------------+
Output
+-------------+------------+
|country |name |
+-------------+------------+
|Mexico |Iztaccihuatl|
|United States|Foraker |
+-------------+------------+
Solution
SELECT "country", "name"
FROM (
SELECT "country", "name", RANK() OVER (PARTITION BY "country" ORDER BY "height" DESC) as "rank"
FROM mountains
) as m
WHERE "rank" = 3
ORDER BY country ASCWe do a subquery because we want to calculate the rank for each mountain in each country before filtering for just the third highest mountain. In the subquery, we use the RANK() function to give each mountain a rank within its country, with the highest mountain having a rank of 1.
Then, in the outer query, we filter for only those mountains that have a rank of 3. Finally, we order the results by country in ascending order.
Tips for Acing Your Google Data Scientist Interview
1. Be prepared to answer questions about your technical skills. The first part of the interview will likely be focused on your technical skills, so you should be prepared to answer questions about SQL, machine learning, and data analysis.
2. Be prepared to solve problems. The second part of the interview will be focused on your problem-solving abilities. You should be prepared to solve problems that are based on real-world data sets.
3. Practice your interviewing skills. In addition to practicing your technical skills, you should also practice your interviewing skills. This means being able to clearly and concisely communicate your thoughts and ideas.
4. Be yourself. The best way to ace any interview is to simply be yourself. Google is looking for candidates who are smart, creative, and passionate.
Final recap for the Google Data Science Interview
These are just some examples of questions you may be asked in the second part of the Google Data Scientist interview. Be sure to come up with your own solutions to these questions before the interview so that you will be prepared to impress the interviewer. Don’t forget to also read our recent article covering the three latest Google SQL Interview questions and check out our free and paid plans on app.bigtechinterviews.com if you’re looking to learn and/or pass the SQL interview. Google Data Scientists are among the best in the world at what they do, so you will need to be at the top of your game to land this job. Good luck!
Want to practice real SQL interview questions? We’ve analyzed over 50,000 interviews from pre-IPO to Fortune 500 companies at Big Tech Interviews (BTI) to curate an exclusive list of the latest SQL interview questions and solutions so you can ace your next interview!
