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About the test:

Data Science Assessment Test evaluerer en kandidats færdigheder i statistik, sandsynlighed, lineære og ikke-lineære regressionsmodeller og deres evne til at analysere data og udnytte Python/ R for at udtrække indsigt fra dataene.

Covered skills:

  • Maskinindlæringsteknikker
  • Analytics med R eller Python
  • Datamanipulation
  • Regressions analyse
  • Forudsigelig modellering
  • Datavisualisering
  • Undersøgende dataanalyse
  • Statistikker
  • Datarensning

9 reasons why
9 reasons why

Adaface Data Science Test is the most accurate way to shortlist Dataforskers



Reason #1

Tests for on-the-job skills

The Data Science Assessment Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Evne til at anvende sandsynlighedskoncepter og principper i dataanalyse
  • Evne til at analysere og fortolke statistiske data
  • Evne til at implementere maskinlæringsalgoritmer og teknikker
  • Evne til at visualisere og præsentere data effektivt
  • Evne til at udføre dataanalyse og efterforskning ved hjælp af R eller Python
  • Evne til at manipulere og transformere data effektivt
  • Evne til at forstå og anvende statistiske begreber i regressionsanalyse
  • Evne til at rengøre og forbehandle data til analyse
  • Evne til at udvikle forudsigelige modeller til forskellige datascenarier
Reason #2

No trick questions

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.

View sample questions

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.

How we design questions

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.

Easy

Gradient descent optimization
Gradient Descent
Solve
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network:
 image
Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights?

A: Constant learning rate of 0.01
B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99
C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99
D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs
E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs
F: Constant learning rate of 0.1

Medium

Less complex decision tree model
Model Complexity
Overfitting
Solve
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model:
 image
Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting?

A: max_depth=5, min_samples_split=2, min_samples_leaf=1
B: max_depth=None, min_samples_split=5, min_samples_leaf=5
C: max_depth=3, min_samples_split=2, min_samples_leaf=1
D: max_depth=None, min_samples_split=2, min_samples_leaf=1
E: max_depth=3, min_samples_split=10, min_samples_leaf=10
F; max_depth=5, min_samples_split=5, min_samples_leaf=5

Easy

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?
 image

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Solve
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Solve
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set.

After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results:
 image
Based on the confusion matrix, what are the sensitivity and specificity of the model?

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
 image
🧐 Question🔧 Skill

Medium

Amazon electronics product feedback

2 mins

Data Science
Solve

Easy

Fraud detection model
Logistic Regression

2 mins

Data Science
Solve

Medium

Rox's decision tree classifier
Decision Tree Classifier

2 mins

Data Science
Solve

Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
Solve

Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
Solve

Easy

n-gram generator

2 mins

Machine Learning
Solve

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
Solve

Medium

Green or red balls

2 mins

Probability
Solve

Hard

Square points and Circle

3 mins

Probability
Solve

Easy

Frequency distribution

3 mins

Statistics
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Amazon electronics product feedback
Data Science
Medium2 mins
Solve
Fraud detection model
Logistic Regression
Data Science
Easy2 mins
Solve
Rox's decision tree classifier
Decision Tree Classifier
Data Science
Medium2 mins
Solve
Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
Solve
Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
Solve
n-gram generator
Machine Learning
Easy2 mins
Solve
Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
Solve
Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
Solve
Green or red balls
Probability
Medium2 mins
Solve
Square points and Circle
Probability
Hard3 mins
Solve
Frequency distribution
Statistics
Easy3 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

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.

Science behind Adaface tests
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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

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.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Data Science Online Test

Why you should use Pre-employment Data Science Assessment Test?

The Data Science Assessment Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Demonstrer en stærk forståelse af sandsynlighedsteori og dens anvendelser inden for datavidenskab.
  • Anvend statistiske koncepter og teknikker til at analysere og fortolke data.
  • Brug maskinlæringsalgoritmer og modeller til at løse problemer i den virkelige verden.
  • Opret visuelt tiltalende datavisualiseringer for effektivt at kommunikere indsigt.
  • Anvend R- eller Python -programmeringssprog til dataanalyse og manipulation.
  • Foretag omfattende efterforskningsdataanalyse for at få indsigt og identificere mønstre.
  • Demonstrere færdigheder i datamanipulationsteknikker til rengørings- og forarbejdningsdata.
  • Anvend regressionsanalyse for at udvikle forudsigelige modeller og fremsætte nøjagtige forudsigelser.
  • Besidder avancerede færdigheder inden for rengøring af data for at sikre datakvalitet og integritet.
  • Udnyt forudsigelige modelleringsteknikker til at tage datadrevne beslutninger.

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Data Science Assessment Test?

  • Datavisualisering: </H4> <p> Data Visualisering involverer at skabe visuelle repræsentationer af data for effektivt at kommunikere indsigt og mønstre. Denne færdighed skal måles i testen, da det er vigtigt for dataforskere at præsentere komplekse data på en meningsfuld og forståelig måde, hvilket letter bedre beslutningstagning og kommunikation. </p> <h4> analyse med R eller Python: </h4 > <p> Analytics med R eller Python henviser til at bruge programmeringssprog såsom R eller Python til at udføre dataanalyse, statistisk modellering og maskinlæringsopgaver. Denne færdighed skal måles i testen, da den vurderer en kandidats evne til at anvende programmeringsevner i datavidenskabelige projekter, hvilket demonstrerer deres færdigheder i håndtering af data og implementering af analytiske algoritmer. </p> <h4> Exploratory Data Analyse: </h4> < P> Undersøgelsesdataanalyse involverer at undersøge og transformere data for at forstå dens vigtigste egenskaber, mønstre og forhold. Denne færdighed skal måles i testen, da den viser en kandidats evne til at udtrække meningsfuld indsigt fra rå data, identificere potentielle problemer og generere hypoteser til yderligere analyse. </p> <h4> datamanipulation:

    Datamanipulation henviser til processen med at transformere, omformatere eller rense data for at gøre det egnede til analyse. Denne færdighed skal måles i testen, da den vurderer en kandidats færdigheder i håndtering og forberedelse af data, som er et afgørende trin i datavidenskabsarbejdsgangen, før du udfører analyse eller modelleringsopgaver.

  • Statistik:

    Statistik involverer indsamling, analyse, fortolkning, præsentation og organisering af data. Denne færdighed skal måles i testen, da den tester en kandidats forståelse og anvendelse af statistiske koncepter og teknikker, som er vigtige for at udføre robuste og gyldige dataanalyse.

  • regressionsanalyse:

    Regressionsanalyse er en statistisk teknik, der bruges til at modellere forholdet mellem en afhængig variabel og en eller flere uafhængige variabler. Denne færdighed skal måles i testen, da den evaluerer en kandidats evne til at udføre regressionsanalyse, som er vidt brugt til forudsigelig modellering og forståelse af virkningen af ​​variabler på et resultat af interesse.

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Data Science Assessment Test to be based on.

    Sandsynlighedsfordelinger
    Hypotese testning
    Central Limit Theorem
    Tillidintervaller
    Lineær regression
    Logistisk regression
    Beslutningstræer
    Tilfældige skove
    Understøtt vektormaskiner
    K-nærmeste naboer
    Naive Bayes
    K-middel klynger
    Hierarkisk klynge
    Hovedkomponentanalyse
    Datavisualiseringsteknikker
    Datavisualiseringsbiblioteker (f.eks. Matplotlib, GGPLOT)
    Dataudforskningsteknikker
    Undersøgende dataanalyse
    Datamanipulation med R eller Python
    Datarensningsteknikker
    Manglende data til data
    Outlier -detektion
    Funktionsteknik
    Korrelationsanalyse
    ANOVA
    Tidsserieanalyse
    A/B -test
    Modelevaluering og validering
    Krydsvalideringsteknikker
    Metoder til valg af funktion
    Dimensionalitetsreduktionsteknikker
    Ensemble -læring
    Overfitting og underfitting
    Regulariseringsteknikker
    Bias-varians Tradeoff
    Dataforarbejdning
    Normalisering
    Standardisering
    En-hot-kodning
    Dataskalering
    Resamplingsmetoder
    Dataopdelingsteknikker
    Modelevalueringsmålinger
    R-kvadrat
    Gennemsnitlig kvadratfejl
    Nøjagtighed
    Præcision og tilbagekaldelse
    F1 score
    ROC -kurveanalyse
    Hyperparameterindstilling
    Gittersøgning
    Krydsvalideringshyperparameterindstilling
    Modelinstallation
    API -integration
    Modelfortolkning og forklaring
    Tolkbare maskinlæringsmodeller
    Shapley -værdier

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

  • Dataforsker
  • Dataanalytiker
  • Machine Learning Engineer
  • Dataingeniør
  • Business analytiker
  • Statistisk analytiker
  • AI Engineer
  • Kunstig intelligensroller

How is the Data Science Assessment Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Brug klynge -algoritmer til klassificering og segmenteringsanalyse.
  • Anvend tidsserieanalyse for at forudsige fremtidige tendenser og mønstre.
  • Demonstrer viden om naturlige sprogbehandlingsalgoritmer og teknikker.
  • Brug funktionsvalg og ekstraktionsteknikker til at forbedre modelydelsen.
  • Anvend dimensionalitetsreduktionsmetoder til datavisualisering og analyse.
  • Anvend ensemble -læringsteknikker til forbedret modelnøjagtighed og ydeevne.
  • Besidder stærke færdigheder i datavisualisering ved hjælp af biblioteker som Matplotlib og GGPLOT.
  • Brug statistisk test og hypotesetest for at tage datadrevne beslutninger.
  • Anvend data tiltagelsesteknikker til at håndtere manglende værdier i datasæt.
  • Anvend krydsvalideringsteknikker for at vurdere modelydelsen og forhindre overfitting.
  • Demonstrer ekspertise i håndtering af ubalancerede datasæt ved hjælp af forskellige teknikker.
Singapore government logo

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

Data Science Hiring Test 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.

Jeg er kandidat. Kan jeg prøve en øvelsestest?

Nej. Desværre understøtter vi ikke praksisforsøg i øjeblikket. Du kan dog bruge vores eksempler på spørgsmål til praksis.

Hvad er omkostningerne ved at bruge denne test?

Du kan tjekke vores prisplaner.

Kan jeg få en gratis prøve?

Ja, du kan tilmelde dig gratis og forhåndsvise denne test.

Jeg flyttede lige til en betalt plan. Hvordan kan jeg anmode om en brugerdefineret vurdering?

Her er en hurtig guide til hvordan man anmoder om en brugerdefineret vurdering på adaface.

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