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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.

54,277 data scientist interview questions shared by candidates

The interviewer asked about random forest and how it works. When I said that each decision tree in the forest considers a random subset of features, he disrespectfully interrupted me and told me that I am wrong. Then he scolded me for giving the "wrong" answer.
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Data Scientist

Interviewed at Fractal

4.1★
Jul 12, 2018

The interviewer asked about random forest and how it works. When I said that each decision tree in the forest considers a random subset of features, he disrespectfully interrupted me and told me that I am wrong. Then he scolded me for giving the "wrong" answer.

Q: Given a function with inputs --an array with N randomly sorted numbers, and an int K, return output in an array with the K largest numbers. Q: 1. How does GMM/HMM work 2. Name some dimensional reduction method; I said PCA and we talked a bit about how PCA works and what's the physical intuiation 3. How K-means work, what kind of distance metric would you choose, what if different features have different dynamic range 4. How GMM works (EM algorithm)
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Applied Scientist

Interviewed at Amazon

3.5★
Mar 9, 2016

Q: Given a function with inputs --an array with N randomly sorted numbers, and an int K, return output in an array with the K largest numbers. Q: 1. How does GMM/HMM work 2. Name some dimensional reduction method; I said PCA and we talked a bit about how PCA works and what's the physical intuiation 3. How K-means work, what kind of distance metric would you choose, what if different features have different dynamic range 4. How GMM works (EM algorithm)

They asked a lot of questions about my take-home project; in particular wanted to know about the reasons that I took the approach that I did. I could tell they were coming more from a statistical and economics background for the most part; while I'm more of an engineering and machine-learning hacker type standpoint. They also had a lot of "ambiguous" questions; by that, I mean questions about ambiguous business situations I might encounter in this position. Wanted to know how well I would do with ambiguous questions I might get from business leaders.
avatar

Data Scientist

Interviewed at Pluralsight

2.9★
Mar 5, 2017

They asked a lot of questions about my take-home project; in particular wanted to know about the reasons that I took the approach that I did. I could tell they were coming more from a statistical and economics background for the most part; while I'm more of an engineering and machine-learning hacker type standpoint. They also had a lot of "ambiguous" questions; by that, I mean questions about ambiguous business situations I might encounter in this position. Wanted to know how well I would do with ambiguous questions I might get from business leaders.

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