Hoe helpt de data science -test recruiters kandidaten te screenen?

De online test online test helpt recruiters en aanwervingsmanagers gekwalificeerde kandidaten te identificeren uit een pool van cv's en helpt bij het nemen van objectieve aanwervingsbeslissingen. Het vermindert de administratieve overhead van het interviewen van te veel kandidaten en bespaart dure technische tijd door ongekwalificeerde kandidaten uit te filteren. De ADAFACE Data Science -testschermen kandidaten voor de typische vaardigheden die recruiters zoeken in een datawetenschapper: Expertise met R -tools, python en machine learning -technieken Mogelijkheid om gegevens op te ruimen en voor te bereiden op analyse Ervaring met het bouwen van robuuste modellen voor machine learning Inzicht in lineaire en niet-lineaire regressiemodellen Ervaring met verkennende gegevensanalyse en regressieanalyse Expertise met data visualisatiehulpmiddelen De inzichten die zijn gegenereerd uit deze beoordeling kunnen worden gebruikt door recruiters en het inhuren van managers om de beste kandidaten voor de rol te identificeren. Anti-lietfuncties stellen u in staat om comfortabel te zijn met het online uitvoeren van beoordelingen. De data science -test is ideaal om recruiters te helpen identificeren welke kandidaten de technische vaardigheden hebben om goed te doen op het werk.

Data Science Assessment Test

About the test:

The data science assessment test evaluates a candidate's proficiency in statistics, probability, linear & non-linear regression models and their ability to analyze data and leverage Python/ R to extract insights from the data.

Covered skills:

Data Science
Data Visualization
Analytics with R or Python
Data Manipulation
Regression Analysis

Machine Learning Techniques
Data Science Aptitude
Exploratory Data Analysis
Statistics

See all covered skills

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9 reasons why

Adaface Data Science Assessment Test is the most accurate way to shortlist Data Scientists

Reason #1

Tests for on-the-job skills

De online test online test helpt recruiters en aanwervingsmanagers gekwalificeerde kandidaten te identificeren uit een pool van cv's en helpt bij het nemen van objectieve aanwervingsbeslissingen. Het vermindert de administratieve overhead van het interviewen van te veel kandidaten en bespaart dure technische tijd door ongekwalificeerde kandidaten uit te filteren.

De ADAFACE Data Science -testschermen kandidaten voor de typische vaardigheden die recruiters zoeken in een datawetenschapper:

Expertise met R -tools, python en machine learning -technieken
Mogelijkheid om gegevens op te ruimen en voor te bereiden op analyse
Ervaring met het bouwen van robuuste modellen voor machine learning
Inzicht in lineaire en niet-lineaire regressiemodellen
Ervaring met verkennende gegevensanalyse en regressieanalyse
Expertise met data visualisatiehulpmiddelen

De inzichten die zijn gegenereerd uit deze beoordeling kunnen worden gebruikt door recruiters en het inhuren van managers om de beste kandidaten voor de rol te identificeren. Anti-lietfuncties stellen u in staat om comfortabel te zijn met het online uitvoeren van beoordelingen. De data science -test is ideaal om recruiters te helpen identificeren welke kandidaten de technische vaardigheden hebben om goed te doen op het werk.

Reason #2

No trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface ->

Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

Dit zijn slechts een klein monster uit onze bibliotheek met meer dan 10.000 vragen. De werkelijke vragen hierover Data Science Assessment Test zal niet-googelbaar zijn.

🧐 Question
Medium Amazon electronics product feedback	Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend? A: Remove the test samples with missing detailed feedback text fields B: Generate synthetic data to fill in missing fields C: Use an algorithm that handles missing data better than decision trees D: Fill in the missing detailed feedback text field with the summary of feedback field.
Easy Fraud detection model Logistic Regression	Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?
Medium Rox's decision tree classifier Decision Tree Classifier	Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation? A. The AUC is high, and the small nodes are all very pure- the model looks accurate. B. The tree might be overfitting- try fitting shallower trees and using an ensemble method. C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability. D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
Medium n-gram generator	Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
Medium Green or red balls	Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?
Hard Square points and Circle	Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter
Easy Frequency distribution	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

	🧐 Question	🔧 Skill
	Medium Amazon electronics product feedback	2 mins Data Science	Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend? A: Remove the test samples with missing detailed feedback text fields B: Generate synthetic data to fill in missing fields C: Use an algorithm that handles missing data better than decision trees D: Fill in the missing detailed feedback text field with the summary of feedback field.
	Easy Fraud detection model Logistic Regression	2 mins Data Science	Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?
	Medium Rox's decision tree classifier Decision Tree Classifier	2 mins Data Science	Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation? A. The AUC is high, and the small nodes are all very pure- the model looks accurate. B. The tree might be overfitting- try fitting shallower trees and using an ensemble method. C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability. D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
	Medium n-gram generator	2 mins Machine Learning	Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Medium Green or red balls	2 mins Probability	Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?
	Hard Square points and Circle	3 mins Probability	Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter
	Easy Frequency distribution	3 mins Statistics	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Amazon electronics product feedback	Data Science	Medium	2 mins	Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend? A: Remove the test samples with missing detailed feedback text fields B: Generate synthetic data to fill in missing fields C: Use an algorithm that handles missing data better than decision trees D: Fill in the missing detailed feedback text field with the summary of feedback field.
	Fraud detection model Logistic Regression	Data Science	Easy	2 mins	Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?
	Rox's decision tree classifier Decision Tree Classifier	Data Science	Medium	2 mins	Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation? A. The AUC is high, and the small nodes are all very pure- the model looks accurate. B. The tree might be overfitting- try fitting shallower trees and using an ensemble method. C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability. D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.
	n-gram generator	Machine Learning	Medium	2 mins	Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Green or red balls	Probability	Medium	2 mins	Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?
	Square points and Circle	Probability	Hard	3 mins	Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter
	Frequency distribution	Statistics	Easy	3 mins	Solve
Convert the following into an ordinary frequency distribution: - 5 users gave less than 3 rating - 12 users gave less than 6 rating - 25 users gave less than 9 ratings - 33 users get less than 12 ratings

Reason #4

1200+ customers in 75 countries

Met Adaface konden we ons eerste screeningproces optimaliseren met meer dan 75%, waardoor we kostbare tijd vrijmaken voor zowel wervingsmanagers als ons talent -acquisitieteam!

Brandon Lee, Hoofd van mensen, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Data Science Assessment Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Data Science Assessment Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

Reason #7

Detailed scorecards & comparative results

Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

Reason #9

Advanced Proctoring

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What roles can I use the Data Science Assessment Test for?

Data Scientist
Data Science Engineer
Data Analyst
Machine Learning Engineer
Statistician

What topics are covered in the Data Science Assessment Test?

Leren onder toezicht

Zonder toezicht leren

Verwarringmatrix

Gradiëntafkomst

Statistieken

Nauwkeurigheid

Classificatie

samenwerkingsfiltering

Herinneren

functieselectie

Waarschijnlijkheid

samenwerkingsfiltering

K-hemelse buurman (K-NN)

Machine Learning

Naïeve bayes

Datamining

eigenwaarden

Classificatie en regressiebomen (kar)

Aanbevelingssystemen

P-waarde

Data visualisatie

Neurale netwerken

Numpy

Lineaire regressie

ROC

Exponentiële verdeling

Beslissingsbomen

Panda's

Beslissingsgrens

Normale verdeling

Curve Fitting

Binomiale verdeling

Scipy

Baye's stelling

Poisson -verdeling

Scikit Learn

Cauchy -verdeling

Multicollineariteit

Uitbijters

Dimensie -reductiemethoden

multivariate analyse

Lineaire algebra

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De aanwervingsmanagers vonden dat door de technische vragen die ze tijdens de interviews van het panel stelden, ze konden vertellen welke kandidaten betere scores hadden, en onderscheidden zich met degenen die niet zo goed scoor. Zij zijn zeer tevreden met de kwaliteit van kandidaten genomineerd met de ADAFACE -screening.

85%

Vermindering van de screeningstijd

Bekijk volledige case study

FAQ's

Wat voor soort vragen bevat de online test met data science?

De data science-test evalueert het on-the-job vaardigheidsniveau van kandidaten met scenario-gebaseerde vragen die gericht zijn op het vermogen van de kandidaat om:

Schone gegevens en zoek afwijkingen
Gebruik trein-/testgegevens en k-voudige kruisvalidatie om robuuste modellen te bouwen
Doe voorspellingen met behulp van lineaire regressie, polynoomregressie en multivariate regressie
Gegevens classificeren met behulp van K-Means-clustering, ondersteunende vectormachines (SVM), KNN, beslissingsbomen, naïeve Bayes en PCA
Lees een verwarringmatrix
Begrijp de afweging van de voorkeur/variantie en overfitting
Gebruik achterwaartse eliminatie, voorwaartse selectie en bidirectionele eliminatiemethoden om statistische modellen te maken
transformeer onafhankelijke variabelen en leid nieuwe onafhankelijke variabelen af voor modelleringsdoeleinden
Controleer op multicollineariteit
Begrijp en voorkom het model verslechtering

Hoe wordt de test aangepast voor senior datawetenschappers?

Naast de hierboven genoemde onderwerpen, bevatten tests voor senior datawetenschappers ook vragen over geavanceerde onderwerpen zoals:

Geavanceerde gegevensmanipulatie om inzichten te genereren uit grote, ongestructureerde datasets
Feature Engineering
Hyperparameter -afstemming
Versterking leren
Dimensionaliteitsvermindering
Geavanceerde statistische analyse

Evalueert de data science -test de aanleg van data science of specifieke technologieën?

De kant-en-klare versie van deze test richt zich op de waarschijnlijkheid van gegevenswetenschappen, statistieken en machine learning. Als u wilt testen op specifieke technologieën, kunt u een aangepaste versie van deze test aanvragen.

Kan ik meerdere vaardigheden combineren in één aangepaste beoordeling?

Ja absoluut. Aangepaste beoordelingen zijn opgezet op basis van uw functiebeschrijving en bevatten vragen over alle must-have vaardigheden die u opgeeft.

Heeft u functies tegen latere of proctoring op hun plaats?

We hebben de volgende anti-cheating-functies op zijn plaats:

Niet-googelbare vragen
IP Proctoring
Web Proctoring
Webcam Proctoring
Plagiaatdetectie
Beveilig browser

Lees meer over de Proctoring -functies.

Hoe interpreteer ik testscores?

Het belangrijkste om in gedachten te houden is dat een beoordeling een eliminatietool is, geen selectietool. Een vaardighedenbeoordeling is geoptimaliseerd om u te helpen kandidaten te elimineren die niet technisch gekwalificeerd zijn voor de rol, het is niet geoptimaliseerd om u te helpen de beste kandidaat voor de rol te vinden. Dus de ideale manier om een beoordeling te gebruiken is om een drempelscore te bepalen (meestal 55%, wij helpen u benchmark) en alle kandidaten uit te nodigen die boven de drempel scoren voor de volgende interviewrondes.

Voor welk ervaringsniveau kan ik deze test gebruiken?

Elke ADAFACE -beoordeling is aangepast aan uw functiebeschrijving/ ideale kandidaatpersonage (onze experts van het onderwerp zullen de juiste vragen kiezen voor uw beoordeling uit onze bibliotheek van 10000+ vragen). Deze beoordeling kan worden aangepast voor elk ervaringsniveau.

Krijgt elke kandidaat dezelfde vragen?

Ja, het maakt het veel gemakkelijker voor u om kandidaten te vergelijken. Opties voor MCQ -vragen en de volgorde van vragen worden gerandomiseerd. We hebben anti-cheating/proctoring functies. In ons bedrijfsplan hebben we ook de optie om meerdere versies van dezelfde beoordeling te maken met vragen over vergelijkbare moeilijkheidsniveaus.