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Data Scientist Interview Questions

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Data Scientist Interview Questions

In un colloquio per Data scientist, ti verranno poste domande volte a verificare le tue capacità di data modeling, risoluzione di problemi e programmazione. Preparati a rispondere a domande di carattere generale che valutano la tua conoscenza della statistica e della scienza dei dati. Dovresti inoltre prepararti a rispondere a domande aperte mirate a testare la tua creatività, le tue doti comunicative e la tua formazione nella programmazione e modellazione dei dati.

Domande tipiche dei colloqui per Data scientist e come rispondere

Question 1

Domanda 1: Quali tecniche di data modeling preferisci e perché?

How to answer
Come rispondere: Trasformare i dati in informazioni comprensibili e fruibili è un aspetto fondamentale del lavoro di Data scientist. Con questa domanda i datori di lavoro vogliono capire il tuo backgruond e valutare le tue capacità di data modeling. Elenca e illustra le tecniche di data modeling che preferisci, includendo vantaggi come semplicità d'uso, flessibilità, ecc.
Question 2

Domanda 2: Come rilevi gli account Instagram fasulli utilizzati per raggirare i consumatori?

How to answer
Come rispondere: Domande come questa permettono ai selezionatori di testare le tue capacità di risolvere i problemi. Quando rispondi a domande aperte di questo tipo, non esitare a chiedere chiarimenti o a usare lavagne per dimostrare le tue abilità nel tracciare diagrammi e usare codici. Condividi il tuo processo di pensiero mentre elabori il problema.
Question 3

Domanda 3: Descrivi quali circostanze richiedono una lista, una tupla o un set in Python.

How to answer
Come rispondere: I selezionatori ti porranno domande come questa per testare le tue abilità di programmazione in Python. Ripassa gli elementi fondamentali di Python, come liste, tuple e set prima del colloquio. Dovrai essere in grado di spiegare quando e come ogni strumento deve essere usato da un Data scientist.

54,330 data scientist interview questions shared by candidates

Codility: find maximum value on a column (something that in pandas would be super easy). Though, I wasn't allowed to use pandas; some coding test that required a priority queue to be solved; question was about a cleaning robot going around a 2D matrix always turning right, and try to tell what will be the covered cells by the robot. Tech screening: linear model, with categorical values, and missing values.
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Data Scientist

Interviewed at Toptal

3.8★
Nov 18, 2021

Codility: find maximum value on a column (something that in pandas would be super easy). Though, I wasn't allowed to use pandas; some coding test that required a priority queue to be solved; question was about a cleaning robot going around a 2D matrix always turning right, and try to tell what will be the covered cells by the robot. Tech screening: linear model, with categorical values, and missing values.

Why BetterUp? Mainly behavioral interview questions . The recruiters did a great job of prepping me through process and before each interview. There were no real surprises in terms of what the interviewers were assessing.
avatar

Behavioral Scientist

Interviewed at BetterUp

3.2★
Sep 16, 2021

Why BetterUp? Mainly behavioral interview questions . The recruiters did a great job of prepping me through process and before each interview. There were no real surprises in terms of what the interviewers were assessing.

Given a table that each day shows who was active in the system and a table that tracks ongoing user status, write a procedure that will take each day's active table and pass it into the ongoing daily tracking table. Possible states are: * user stayed (yesterday yes, today yes) * user churned (yesterday yes, today no) * user revived (yesterday no, today yes) * user new (yesterday null, today yes) Note: you'll want to spot and account for the undefined state.
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Data Scientist

Interviewed at Meta

3.5★
Jul 31, 2016

Given a table that each day shows who was active in the system and a table that tracks ongoing user status, write a procedure that will take each day's active table and pass it into the ongoing daily tracking table. Possible states are: * user stayed (yesterday yes, today yes) * user churned (yesterday yes, today no) * user revived (yesterday no, today yes) * user new (yesterday null, today yes) Note: you'll want to spot and account for the undefined state.

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