Hvordan hjælper datavidenskabstesten med at rekruttere screente kandidater?

Data Science Online Test hjælper rekrutterere og ansættelsesledere med at identificere kvalificerede kandidater fra en pulje af CV og hjælper med at tage objektive ansættelsesbeslutninger. Det reducerer den administrative omkostning ved at interviewe for mange kandidater og sparer dyre teknisk tid ved at filtrere ukvalificerede kandidater. ADAFACE Data Science Test Screens kandidater til de typiske færdigheder, som rekrutterere ser efter hos en datavidenskabsmand: Ekspertise med R -værktøjer, Python og maskinlæringsteknikker Evne til at rengøre og forberede data til analyse Oplev opbygning af robuste maskinlæringsmodeller Forståelse af lineære og ikke-lineære regressionsmodeller Erfaring med efterforskende dataanalyse og regressionsanalyse Ekspertise med datavisualiseringsværktøjer Den indsigt, der genereres fra denne vurdering, kan bruges af rekrutterere og ansættelsesledere til at identificere de bedste kandidater til rollen. Anti-cheating-funktioner giver dig mulighed for at være komfortabel med at gennemføre vurderinger online. Datavidenskabstesten er ideel til at hjælpe rekrutterere med at identificere, hvilke kandidater der har de tekniske færdigheder til at gøre godt på jobbet.

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

Data Science Online Test hjælper rekrutterere og ansættelsesledere med at identificere kvalificerede kandidater fra en pulje af CV og hjælper med at tage objektive ansættelsesbeslutninger. Det reducerer den administrative omkostning ved at interviewe for mange kandidater og sparer dyre teknisk tid ved at filtrere ukvalificerede kandidater.

ADAFACE Data Science Test Screens kandidater til de typiske færdigheder, som rekrutterere ser efter hos en datavidenskabsmand:

Ekspertise med R -værktøjer, Python og maskinlæringsteknikker
Evne til at rengøre og forberede data til analyse
Oplev opbygning af robuste maskinlæringsmodeller
Forståelse af lineære og ikke-lineære regressionsmodeller
Erfaring med efterforskende dataanalyse og regressionsanalyse
Ekspertise med datavisualiseringsværktøjer

Den indsigt, der genereres fra denne vurdering, kan bruges af rekrutterere og ansættelsesledere til at identificere de bedste kandidater til rollen. Anti-cheating-funktioner giver dig mulighed for at være komfortabel med at gennemføre vurderinger online. Datavidenskabstesten er ideel til at hjælpe rekrutterere med at identificere, hvilke kandidater der har de tekniske færdigheder til at gøre godt på jobbet.

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.

Dette er kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Data Science Assessment Test vil være ikke-gåbart.

🧐 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

Med Adaface var vi i stand til at optimere vores indledende screeningsproces med op til 75%, hvilket frigør dyrebar tid for både ansættelsesledere og vores talentindsamlingsteam!

Brandon Lee, Leder af mennesker, 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?

Overvåget læring

Uovervåget læring

Forvirringsmatrix

Gradient afstamning

Statistikker

Præcision

Klassifikation

Samarbejdsfiltrering

Minde om

Funktionsvalg

Sandsynlighed

Samarbejdsfiltrering

K-nærmeste nabo (K-NN)

Maskinelæring

Naive Bayes

Data mining

egenværdier

Klassificering og regressionstræer (vogn)

Anbefalingssystemer

P-værdi

Datavisualisering

Neurale netværk

Numpy

Lineær regression

Roc

Eksponentiel fordeling

Beslutningstræer

Pandas

Beslutningsgrænse

Normal fordeling

Kurve -montering

Binomial distribution

Scipy

Bayes sætning

Poisson distribution

Scikit lærer

Cauchy Distribution

Multikollinearitet

Outliers

Dimensionsreduktionsmetoder

Multivariat analyse

Lineær algebra

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De ansættelsesledere mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, kunne fortælle, hvilke kandidater der havde bedre scoringer, og differentierede med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af kandidater, der er på listen med Adaface -screeningen.

85%

Reduktion i screeningstid

Se den fulde casestudie

FAQS

Hvilken type spørgsmål indeholder datavidenskab online -test?

Data Science Test evaluerer det on-the-job-færdighedsniveau for kandidater med scenariebaserede spørgsmål med fokus på kandidatens evne til at:

Rene data og kig efter afvigelser
Brug tog/testdata og k-fold krydsvalidering til at opbygge robuste modeller
Foretag forudsigelser ved hjælp af lineær regression, polynomisk regression og multivariat regression
Klassificer data ved hjælp af K-Means Clustering, Support Vector Machines (SVM), KNN, beslutningstræer, Naive Bayes og PCA
Læs en forvirringsmatrix
Forstå bias/variansvejs og overfitting
Brug bagud eliminering, fremadrettet valg og tovejs elimineringsmetoder til at skabe statistiske modeller
Transform uafhængige variabler og udlede nye uafhængige variabler til modelleringsformål
Kontroller for multikollinearitet
Forstå og forhindre forringelse af modeller

Hvordan tilpasses testen til senior dataforskere?

Ud over de ovennævnte emner inkluderer tests for senior dataforskere også spørgsmål om avancerede emner som:

Avanceret datamanipulation for at generere indsigt fra store, ustrukturerede datasæt
Funktionsteknik
Hyperparameter -tuning
Forstærkningslæring
Dimensionalitetsreduktion
Avanceret statistisk analyse

Evaluerer datavidenskabstesten datavidenskabelig egnethed eller specifikke teknologier?

Den klar-til-brug-version af denne test fokuserer på datavidenskabelig egnethed-sandsynlighed, statistik og maskinlæring. Hvis du ønsker at teste for specifikke teknologier, kan du anmode om en brugerdefineret version af denne test.

Kan jeg kombinere flere færdigheder i en brugerdefineret vurdering?

Ja absolut. Brugerdefinerede vurderinger er oprettet baseret på din jobbeskrivelse og vil omfatte spørgsmål om alle must-have-færdigheder, du angiver.

Har du nogen anti-cheating eller proctoring-funktioner på plads?

Vi har følgende anti-cheating-funktioner på plads:

Ikke-gåbare spørgsmål
IP Proctoring
Webproctoring
Webcam Proctoring
Detektion af plagiering
Sikker browser

Læs mere om Proctoring Features.

Hvordan fortolker jeg testresultater?

Den primære ting at huske på er, at en vurdering er et elimineringsværktøj, ikke et udvælgelsesværktøj. En færdighedsvurdering er optimeret for at hjælpe dig med at eliminere kandidater, der ikke er teknisk kvalificerede til rollen, den er ikke optimeret til at hjælpe dig med at finde den bedste kandidat til rollen. Så den ideelle måde at bruge en vurdering på er at beslutte en tærskelværdi (typisk 55%, vi hjælper dig med benchmark) og inviterer alle kandidater, der scorer over tærsklen for de næste interviewrunder.

Hvilken oplevelsesniveau kan jeg bruge denne test til?

Hver Adaface -vurdering tilpasses til din jobbeskrivelse/ ideel kandidatperson (vores emneeksperter vælger de rigtige spørgsmål til din vurdering fra vores bibliotek på 10000+ spørgsmål). Denne vurdering kan tilpasses til ethvert erfaringsniveau.

Får hver kandidat de samme spørgsmål?

Ja, det gør det meget lettere for dig at sammenligne kandidater. Valgmuligheder for MCQ -spørgsmål og rækkefølgen af spørgsmål randomiseres. Vi har anti-cheating/proctoring funktioner på plads. I vores virksomhedsplan har vi også muligheden for at oprette flere versioner af den samme vurdering med spørgsmål om lignende vanskelighedsniveauer.