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Data Scientist Interview Questions

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Data Scientist Interview Questions

Lors d’un entretien pour le poste de data scientist, les employeurs vont poser des questions leur permettant d’évaluer vos compétences en modélisation des données, résolution des problèmes et programmation. Soyez préparé à répondre à des questions générales testant vos connaissances en statistiques et en science des données. Vous devez également être prêt à répondre à des questions ouvertes permettant de tester votre créativité, vos compétences en communication et votre éducation formelle en modélisation des données et en programmation.

Questions d'entretien d'embauche fréquentes pour un data scientist (H/F) et comment y répondre

Question 1

Question 1 : Quelles techniques de modélisation des données préférez-vous et pourquoi ?

How to answer
Comment répondre : La transformation des données en informations compréhensibles et exploitables est un élément critique du métier de data scientist. Cette question permet aux employeurs de comprendre vos compétences en modélisation des données et votre cursus. Répertoriez et détaillez les techniques de modélisation des données que vous préférez, notamment leurs avantages comme leur facilité d’utilisation, leur flexibilité, etc.
Question 2

Question 2 : Comment détectez-vous les faux comptes Instagram utilisés pour escroquer les clients ?

How to answer
Comment répondre : Ce type de question permet à un employeur de tester vos compétences en résolution des problèmes. Lorsque vous répondez à des questions ouvertes comme celle-ci, n’hésitez pas à demander des précisions sur ces dernières et à utiliser un tableau pour présenter vos compétences en programmation et création de graphiques. Partagez votre processus de réflexion lorsque vous résolvez le problème.
Question 3

Question 3 : Décrivez des situations qui requièrent une liste, un uplet ou un ensemble sur Python.

How to answer
Comment répondre : Les intervieweurs posent ce type de question pour tester vos compétences en programmation sur Python. Révisez les rudiments de Python comme les listes, les uplets et les ensembles avant votre entretien. Vous devez être en mesure d’expliquer à quel moment et de quelle manière chaque outil est utilisé par les data scientists.

54,205 data scientist interview questions shared by candidates

1) Provided a table with user_id and dates they visited platform, find the top 100 users with the longest continuous streak of visiting the platform as of yesterday. 2) Provided a table with page_id, event timestamp and a flag for a state (which is on/off), find the number of pages that are currently on.
avatar

Data Scientist

Interviewed at Meta

3.6★
Apr 29, 2020

1) Provided a table with user_id and dates they visited platform, find the top 100 users with the longest continuous streak of visiting the platform as of yesterday. 2) Provided a table with page_id, event timestamp and a flag for a state (which is on/off), find the number of pages that are currently on.

There is a table that tracks every time a user turns a feature on or off, with columns user_id, action ("on" or "off), date, and time. How many users turned the feature on today? How many users have ever turned the feature on? In a table that tracks the status of every user every day, how would you add today's data to it?
avatar

Data Scientist

Interviewed at Meta

3.6★
Mar 29, 2017

There is a table that tracks every time a user turns a feature on or off, with columns user_id, action ("on" or "off), date, and time. How many users turned the feature on today? How many users have ever turned the feature on? In a table that tracks the status of every user every day, how would you add today's data to it?

They asked probability question: 1) The probability that item an item at location A is 0.6 , and 0.8 at location B. What is the probability that item would be found on Amazon website. 2). I have table 1, with 1million records, with ID, AGE (column names) , Table 2 with 100 records with ID and Salary then the interviewer gave me the following SQL script SELECT A.ID,A.AGE,B.SALARY FROM TABLE 1 A LEFT JOIN TABLE 2 B ON A.ID = B.ID + WHERE B.SALARY > 50000 ( HE ASKED TO MODIFY THIS LINE OF QUERY) How many records would be returned? 3. Give a csv file with ID, and Quantity columns, 50million records and size of data is 2gig, write a program in any language of your choice to aggregate the QUANTITY column.
avatar

Data Scientist

Interviewed at Amazon

3.5★
Oct 27, 2016

They asked probability question: 1) The probability that item an item at location A is 0.6 , and 0.8 at location B. What is the probability that item would be found on Amazon website. 2). I have table 1, with 1million records, with ID, AGE (column names) , Table 2 with 100 records with ID and Salary then the interviewer gave me the following SQL script SELECT A.ID,A.AGE,B.SALARY FROM TABLE 1 A LEFT JOIN TABLE 2 B ON A.ID = B.ID + WHERE B.SALARY > 50000 ( HE ASKED TO MODIFY THIS LINE OF QUERY) How many records would be returned? 3. Give a csv file with ID, and Quantity columns, 50million records and size of data is 2gig, write a program in any language of your choice to aggregate the QUANTITY column.

Given the following data: Table: searches Columns: date STRING date of the search, search_id INT the unique identifier of each search, user_id INT the unique identifier of the searcher, age_group STRING ('<30', '30-50', '50+'), search_query STRING the text of the search query Sample Rows: date | search_id | user_id | age_group | search_query -------------------------------------------------------------------- '2020-01-01' | 101 | 9991 | '<30' | 'justin bieber' '2020-01-01' | 102 | 9991 | '<30' | 'menlo park' '2020-01-01' | 103 | 5555 | '30-50' | 'john' '2020-01-01' | 104 | 1234 | '50+' | 'funny cats' Table: search_results Columns: date STRING date of the search action, search_id INT the unique identifier of each search, result_id INT the unique identifier of the result, result_type STRING (page, event, group, person, post, etc.), clicked BOOLEAN did the user click on the result? Sample Rows: date | search_id | result_id | result_type | clicked -------------------------------------------------------------------- '2020-01-01' | 101 | 1001 | 'page' | TRUE '2020-01-01' | 101 | 1002 | 'event' | FALSE '2020-01-01' | 101 | 1003 | 'event' | FALSE '2020-01-01' | 101 | 1004 | 'group' | FALSE Over the last 7 days, how many users made more than 10 searches? You notice that the number of users that clicked on a search result about a Facebook Event increased 10% week-over-week. How would you investigate? How do you decide if this is a good thing or a bad thing? The Events team wants to up-rank Events such that they show up higher in Search. How would you determine if this is a good idea or not?
avatar

Data Scientist

Interviewed at Meta

3.6★
Apr 23, 2021

Given the following data: Table: searches Columns: date STRING date of the search, search_id INT the unique identifier of each search, user_id INT the unique identifier of the searcher, age_group STRING ('<30', '30-50', '50+'), search_query STRING the text of the search query Sample Rows: date | search_id | user_id | age_group | search_query -------------------------------------------------------------------- '2020-01-01' | 101 | 9991 | '<30' | 'justin bieber' '2020-01-01' | 102 | 9991 | '<30' | 'menlo park' '2020-01-01' | 103 | 5555 | '30-50' | 'john' '2020-01-01' | 104 | 1234 | '50+' | 'funny cats' Table: search_results Columns: date STRING date of the search action, search_id INT the unique identifier of each search, result_id INT the unique identifier of the result, result_type STRING (page, event, group, person, post, etc.), clicked BOOLEAN did the user click on the result? Sample Rows: date | search_id | result_id | result_type | clicked -------------------------------------------------------------------- '2020-01-01' | 101 | 1001 | 'page' | TRUE '2020-01-01' | 101 | 1002 | 'event' | FALSE '2020-01-01' | 101 | 1003 | 'event' | FALSE '2020-01-01' | 101 | 1004 | 'group' | FALSE Over the last 7 days, how many users made more than 10 searches? You notice that the number of users that clicked on a search result about a Facebook Event increased 10% week-over-week. How would you investigate? How do you decide if this is a good thing or a bad thing? The Events team wants to up-rank Events such that they show up higher in Search. How would you determine if this is a good idea or not?

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