Why you should use 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: Demonstreer een sterk begrip van de waarschijnlijkheidstheorie en de toepassingen ervan in data science. Pas statistische concepten en technieken toe om gegevens te analyseren en te interpreteren. Gebruik machine learning-algoritmen en modellen om problemen op het gebied van real-world op te lossen. Creëer visueel aantrekkelijke gegevensvisualisaties om inzichten effectief te communiceren. Gebruik R- of Python -programmeertalen voor data -analyse en manipulatie. Voer uitgebreide verkennende gegevensanalyse uit om inzichten te krijgen en patronen te identificeren. Toont vaardigheid in gegevensmanipulatietechnieken om gegevens te reinigen en preprocess. Pas regressieanalyse toe om voorspellende modellen te ontwikkelen en nauwkeurige voorspellingen te doen. Bezit geavanceerde vaardigheden in gegevensreiniging om de gegevenskwaliteit en integriteit te waarborgen. Leverage voorspellende modelleringstechnieken voor het nemen van gegevensgestuurde beslissingen.

Data Science Assessment Test

Q: What topics are covered in the Data Science Assessment Test?

Technieken voor machine learning Data visualisatie Analytics met R of Python Verkennende gegevensanalyse Data manipulatie Statistieken Regressie analyse Data-opschoning Voorspellende modellering

About the test:

De beoordelingstest voor gegevenswetenschappen evalueert de vaardigheid van een kandidaat in statistieken, waarschijnlijkheid, lineaire en niet-lineaire regressiemodellen en hun vermogen om gegevens te analyseren en python/ r te benutten om inzichten uit de gegevens te extraheren.

Covered skills:

Technieken voor machine learning
Analytics met R of Python
Data manipulatie
Regressie analyse
Voorspellende modellering

Data visualisatie
Verkennende gegevensanalyse
Statistieken
Data-opschoning

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9 reasons why

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

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:

Mogelijkheid om waarschijnlijkheidsconcepten en principes toe te passen in gegevensanalyse
Mogelijkheid om statistische gegevens te analyseren en te interpreteren
Mogelijkheid om machine learning -algoritmen en technieken te implementeren
Mogelijkheid om gegevens effectief te visualiseren en te presenteren
Mogelijkheid om gegevensanalyse en verkenning uit te voeren met behulp van R of Python
Mogelijkheid om gegevens efficiënt te manipuleren en te transformeren
Vermogen om statistische concepten te begrijpen en toe te passen in regressieanalyse
Mogelijkheid om gegevens op te schonen en voor de preprocess voor analyse
Mogelijkheid om voorspellende modellen te ontwikkelen voor verschillende gegevensscenario's

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.

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

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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

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.
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: 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: 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?
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: 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

	🧐 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.
	Easy Gradient descent optimization Gradient Descent	2 mins Machine Learning	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: 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	2 mins Machine Learning	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: 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	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?
	Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	2 mins Machine Learning	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	2 mins Machine Learning	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: Based on the confusion matrix, what are the sensitivity and specificity of the model?
	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.
	Gradient descent optimization Gradient Descent	Machine Learning	Easy	2 mins	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: 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
	Less complex decision tree model Model Complexity Overfitting	Machine Learning	Medium	2 mins	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: 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
	n-gram generator	Machine Learning	Easy	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?
	Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Machine Learning	Easy	2 mins	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?
	Sensitivity and Specificity Confusion Matrix Model Evaluation	Machine Learning	Easy	2 mins	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: Based on the confusion matrix, what are the sensitivity and specificity of the model?
	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 met ruim 75% optimaliseren, waardoor kostbare tijd vrijkwam voor zowel de rekruteringsmanagers als ons talentacquisitieteam!

Brandon Lee, Hoofd Mensen, Love, Bonito

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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.

Reason #7

Detailed scorecards & benchmarks

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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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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:

Demonstreer een sterk begrip van de waarschijnlijkheidstheorie en de toepassingen ervan in data science.
Pas statistische concepten en technieken toe om gegevens te analyseren en te interpreteren.
Gebruik machine learning-algoritmen en modellen om problemen op het gebied van real-world op te lossen.
Creëer visueel aantrekkelijke gegevensvisualisaties om inzichten effectief te communiceren.
Gebruik R- of Python -programmeertalen voor data -analyse en manipulatie.
Voer uitgebreide verkennende gegevensanalyse uit om inzichten te krijgen en patronen te identificeren.
Toont vaardigheid in gegevensmanipulatietechnieken om gegevens te reinigen en preprocess.
Pas regressieanalyse toe om voorspellende modellen te ontwikkelen en nauwkeurige voorspellingen te doen.
Bezit geavanceerde vaardigheden in gegevensreiniging om de gegevenskwaliteit en integriteit te waarborgen.
Leverage voorspellende modelleringstechnieken voor het nemen van gegevensgestuurde beslissingen.

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?

Datavisualisatie:
gegevens Visualisatie omvat het maken van visuele weergaven van gegevens om inzichten en patronen effectief te communiceren. Deze vaardigheid moet in de test worden gemeten, omdat het essentieel is voor datawetenschappers om complexe gegevens op een zinvolle en begrijpelijke manier te presenteren, waardoor betere besluitvorming en communicatie wordt vergemakkelijkt.
analyses met R of Python: </h4 > <p> Analytics met R of Python verwijst naar het gebruik van programmeertalen zoals R of Python om gegevensanalyse, statistische modellering en machine learning -taken uit te voeren. Deze vaardigheid moet in de test worden gemeten, omdat het het vermogen van een kandidaat beoordeelt om programmeervaardigheden toe te passen in data science -projecten, het aantonen van hun vaardigheid bij het verwerken van gegevens en het implementeren van analyse -algoritmen. </p> <h4> verkennende gegevensanalyse: </h4> << P> Verkennende gegevensanalyse omvat het onderzoeken en transformeren van gegevens om de belangrijkste kenmerken, patronen en relaties te begrijpen. Deze vaardigheid moet in de test worden gemeten, omdat het het vermogen van een kandidaat weergeeft om zinvolle inzichten uit onbewerkte gegevens te extraheren, potentiële problemen te identificeren en hypothesen te genereren voor verdere analyse. </p> <h4> Gegevensmanipulatie:
Gegevensmanipulatie verwijst naar het proces van het transformeren, herformuleren of reinigen van gegevens om deze geschikt te maken voor analyse. Deze vaardigheid moet in de test worden gemeten, omdat het de vaardigheid van een kandidaat beoordeelt bij het omgaan en voorbereiden van gegevens, wat een cruciale stap is in de data science -workflow voordat analyse of modelleringstaken uitvoeren.
statistieken:
Statistieken omvatten de verzameling, analyse, interpretatie, presentatie en organisatie van gegevens. Deze vaardigheid moet in de test worden gemeten, omdat het het begrip en de toepassing van een kandidaat van statistische concepten en technieken test, die essentieel zijn voor het uitvoeren van robuuste en geldige gegevensanalyse.
regressieanalyse:
Regressieanalyse is een statistische techniek die wordt gebruikt om de relatie tussen een afhankelijke variabele en een of meer onafhankelijke variabelen te modelleren. Deze vaardigheid moet in de test worden gemeten, omdat het het vermogen van een kandidaat evalueert om regressieanalyse uit te voeren, dat op grote schaal wordt gebruikt bij voorspellende modellering en het begrijpen van de impact van variabelen op een uitkomst van 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.
Waarschijnlijkheidsverdelingen
Hypothesetesten
Centrale limietstelling
Betrouwbaarheidsintervallen
Lineaire regressie
Logistieke regressie
Beslissingsbomen
Willekeurige bossen
Ondersteuning van vectormachines
K-hemelse buren
Naïeve bayes
K-middelen clustering
Hiërarchische clustering
Hoofdcomponentanalyse
Datavisualisatietechnieken
Datavisualisatie -bibliotheken (bijv. Matplotlib, ggplot)
Data exploratietechnieken
Verkennende gegevensanalyse
Gegevensmanipulatie met R of Python
Gegevensreinigingstechnieken
Ontbrekende data -imputatie
Uitbijterdetectie
Feature Engineering
Correlatie analyse
ANOVA
Tijdreeksanalyse
A/B -testen
Modelevaluatie en validatie
Kruisvalidatietechnieken
Functieselectie methoden
Dimensionaliteitsreductietechnieken
Ensemble leren
Overfitting en onderbroken
Regularisatietechnieken
Bias-variantie afweging
Gegevens voorbewerking
Normalisatie
Standaardisatie
One-hot codering
Gegevensschaling
Resampling -methoden
Gegevenssplitsingtechnieken
Modelevaluatiemetrieken
R-kwadraat
Gemiddelde vierkante fout
Nauwkeurigheid
Precisie en terugroepen
F1 -score
ROC -curve -analyse
Hyperparameterafstemming
Grid Search
Cross-validatie Hyperparameter afstemming
Modelimplementatie
API -integratie
Modelinterpretatie en uitleg
Interpreteerbare modellen voor machine learning
Shapley -waarden

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

Data scientist
Data -analist
Ingenieur van machine learning
Data Engineer
Bedrijfsanalist
Statistische analist
AI -ingenieur
Kunstmatige intelligentie -rollen

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

Gebruik clusteringalgoritmen voor classificatie- en segmentatie -analyse.
Pas tijdreeksanalyse toe om toekomstige trends en patronen te voorspellen.
Toon kennis van algoritmen en technieken voor natuurlijke taalverwerking.
Gebruik functieselectie en extractietechnieken om de modelprestaties te verbeteren.
Gebruikmethoden voor dimensionaliteit reductiemethoden voor datavisualisatie en analyse.
Pas ensemble -leertechnieken toe voor verbeterde modelnauwkeurigheid en prestaties.
Bezit sterke vaardigheden in datavisualisatie met behulp van bibliotheken zoals Matplotlib en GGPlot.
Gebruik statistische testen en hypothesetesten om gegevensgestuurde beslissingen te nemen.
Maak gebruik van data -imputatietechnieken om ontbrekende waarden in datasets af te handelen.
Pas kruisvalidatietechnieken toe om de modelprestaties te beoordelen en overfitting te voorkomen.
Demonstreer expertise in het omgaan met onevenwichtige datasets met behulp van verschillende technieken.

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Bekijk Sample Scorecard

De rekruteringsmanagers waren van mening dat ze door de technische vragen die ze tijdens de panelgesprekken stelden, konden zien welke kandidaten beter scoorden, en onderscheidden ze zich met degenen die niet zo goed scoorden. Zij zijn zeer tevreden met de kwaliteit van de kandidaten op de shortlist van de Adaface-screening.

85%

Vermindering van de screeningstijd

Bekijk volledige case study

Data Science Hiring Test Veelgestelde vragen

Wat voor soort vragen bevat de Data Science online test?

De Data Science-test evalueert het vaardigheidsniveau op de werkvloer van kandidaten met op scenario's gebaseerde vragen die zich richten op het vermogen van de kandidaat om:

Gegevens opschonen en op zoek gaan naar afwijkingen
Gebruik trein-/testgegevens en K-Fold-kruisvalidatie om robuuste modellen te bouwen
Maak voorspellingen met behulp van lineaire regressie, polynomiale regressie en multivariate regressie
Classificeer gegevens met behulp van K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes en PCA
Lees een verwarringsmatrix
Begrijp de afweging tussen bias en variantie en overfitting
Gebruik achterwaartse eliminatie, voorwaartse selectie en bidirectionele eliminatiemethoden om statistische modellen te creëren
Transformeer onafhankelijke variabelen en leid nieuwe onafhankelijke variabelen af voor modelleringsdoeleinden
Controleer op multicollineariteit
Begrijp en voorkom modelverslechtering

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
Functietechniek
Hyperparameterafstemming
Versterkend leren
Dimensionaliteitsreductie
Geavanceerde statistische analyse

Evalueert de data science-test de data science-vaardigheid of specifieke technologieën?

De kant-en-klare versie van deze test richt zich op data science-vaardigheid: waarschijnlijkheid, statistiek en machinaal leren. Als u specifieke technologieën wilt testen, 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.