¿Cómo ayuda la prueba de ciencia de datos a los reclutadores a evaluar a los candidatos?

La prueba en línea de Data Science ayuda a los reclutadores y gerentes de contratación a identificar candidatos calificados de un grupo de currículums, y ayuda a tomar decisiones objetivas de contratación. Reduce la sobrecarga administrativa de entrevistar a demasiados candidatos y ahorra un costoso tiempo de ingeniería al filtrar candidatos no calificados. Las pantallas de prueba de ciencia de datos de Adaface, los candidatos para las habilidades típicas buscan en un científico de datos: Experiencia con Tools R, Python y Técnicas de aprendizaje automático Capacidad para limpiar y preparar datos para el análisis Experiencia en la construcción de modelos robustos de aprendizaje automático Comprensión de los modelos de regresión lineal y no lineal Experiencia con análisis de datos exploratorios y análisis de regresión Experiencia con herramientas de visualización de datos Los reclutadores y los gerentes de contratación pueden utilizar las ideas generadas a partir de esta evaluación para identificar a los mejores candidatos para el papel. Las características anti-trato le permiten sentirse cómodo realizando evaluaciones en línea. La prueba de ciencia de datos es ideal para ayudar a los reclutadores a identificar qué candidatos tienen las habilidades técnicas para que funcionen bien en el trabajo.

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

La prueba en línea de Data Science ayuda a los reclutadores y gerentes de contratación a identificar candidatos calificados de un grupo de currículums, y ayuda a tomar decisiones objetivas de contratación. Reduce la sobrecarga administrativa de entrevistar a demasiados candidatos y ahorra un costoso tiempo de ingeniería al filtrar candidatos no calificados.

Las pantallas de prueba de ciencia de datos de Adaface, los candidatos para las habilidades típicas buscan en un científico de datos:

Experiencia con Tools R, Python y Técnicas de aprendizaje automático
Capacidad para limpiar y preparar datos para el análisis
Experiencia en la construcción de modelos robustos de aprendizaje automático
Comprensión de los modelos de regresión lineal y no lineal
Experiencia con análisis de datos exploratorios y análisis de regresión
Experiencia con herramientas de visualización de datos

Los reclutadores y los gerentes de contratación pueden utilizar las ideas generadas a partir de esta evaluación para identificar a los mejores candidatos para el papel. Las características anti-trato le permiten sentirse cómodo realizando evaluaciones en línea. La prueba de ciencia de datos es ideal para ayudar a los reclutadores a identificar qué candidatos tienen las habilidades técnicas para que funcionen bien en el trabajo.

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.

Estas son solo una pequeña muestra de nuestra biblioteca de más de 10,000 preguntas. Las preguntas reales sobre esto Data Science Assessment Test no se puede obtener.

🧐 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

Con Adaface, pudimos optimizar nuestro proceso de detección inicial en más del 75%, ¡liberando un tiempo precioso tanto para los gerentes de contratación como para nuestro equipo de adquisición de talentos!

Brandon Lee, Jefe de personas, 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?

Aprendizaje supervisado

Aprendizaje sin supervisión

Matriz de confusión

Descenso de gradiente

Estadísticas

Precisión

Clasificación

filtración colaborativa

Recuerdo

selección de características

Probabilidad

filtración colaborativa

K-Nearest Vecin (K-NN)

Aprendizaje automático

Bayes ingenuos

Procesamiento de datos

valores propios

Árboles de clasificación y regresión (carrito)

Sistemas de recomendación

Valor p

Visualización de datos

Redes neuronales

Numpy

Regresión lineal

ROC

Distrbución exponencial

Árboles de decisión

Pandas

Límite de decisión

Distribución normal

Ajuste de curvas

Distribución binomial

Bisagro

Teorema de Bayes

distribución de veneno

Scikit Learn

Distribución cauchy

Multicolinealidad

Valores atípicos

Métodos de reducción de dimensiones

analisis multivariable

Álgebra lineal

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Los gerentes de contratación consideraron que a través de las preguntas técnicas que hicieron durante las entrevistas del panel, pudieron decir qué candidatos tuvieron mejores puntajes y se diferenciaron con aquellos que no obtuvieron puntajes también. Ellos son altamente satisfecho con la calidad de los candidatos preseleccionados con la proyección de Adaface.

85%

Reducción en el tiempo de detección

Ver estudio de caso completo

Preguntas frecuentes

¿Qué tipo de preguntas contiene la prueba en línea de ciencia de datos?

La prueba de ciencia de datos evalúa el nivel de habilidad de los candidatos en el trabajo con preguntas basadas en escenarios centrados en la capacidad del candidato para:

Limpie los datos y busque anomalías
Use datos de trenes/pruebas y validación cruzada K-Fold para crear modelos robustos
Hacer predicciones utilizando regresión lineal, regresión polinomial y regresión multivariada
Clasifique los datos utilizando la agrupación de K-means, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes y PCA
Leer una matriz de confusión
Comprender la compensación de sesgo/varianza y el sobreajuste
Use la eliminación hacia atrás, la selección hacia adelante y los métodos de eliminación bidireccional para crear modelos estadísticos
Transformar variables independientes y derivar nuevas variables independientes para fines de modelado
Verifique la multicolinealidad
Comprender y prevenir el deterioro del modelo

¿Cómo se personalizará la prueba para los científicos de datos senior?

Además de los temas mencionados anteriormente, las pruebas para los científicos de datos senior también incluyen preguntas sobre temas avanzados como:

Manipulación de datos avanzada para generar ideas a partir de conjuntos de datos grandes y no estructurados
Ingeniería de características
Tuning de hiperparameter
Aprendizaje reforzado
Reducción de dimensionalidad
Análisis estadístico avanzado

¿La prueba de ciencia de datos evalúa la aptitud de la ciencia de datos o tecnologías específicas?

La versión lista para usar de esta prueba se centra en la aptitud de la ciencia de datos: probabilidad, estadísticas y aprendizaje automático. Si está buscando probar tecnologías específicas, puede solicitar una versión personalizada de esta prueba.

¿Puedo combinar múltiples habilidades en una evaluación personalizada?

Si, absolutamente. Las evaluaciones personalizadas se configuran en función de la descripción de su trabajo e incluirán preguntas sobre todas las habilidades imprescindibles que especifique.

¿Tiene alguna característica anti-trato o procuración en su lugar?

Tenemos las siguientes características anti-trate en su lugar:

Preguntas no postradas
Procuración de IP
Procedor web
Procedores de cámara web
Detección de plagio
navegador seguro

Lea más sobre las funciones de procuración.

¿Cómo interpreto los puntajes de las pruebas?

Lo principal a tener en cuenta es que una evaluación es una herramienta de eliminación, no una herramienta de selección. Una evaluación de habilidades está optimizada para ayudarlo a eliminar a los candidatos que no están técnicamente calificados para el rol, no está optimizado para ayudarlo a encontrar el mejor candidato para el papel. Por lo tanto, la forma ideal de usar una evaluación es decidir un puntaje umbral (generalmente del 55%, lo ayudamos a comparar) e invitar a todos los candidatos que obtienen un puntaje por encima del umbral para las próximas rondas de la entrevista.

¿Para qué nivel de experiencia puedo usar esta prueba?

Cada evaluación de AdaFace está personalizada para su descripción de trabajo/ persona candidata ideal (nuestros expertos en la materia elegirán las preguntas correctas para su evaluación de nuestra biblioteca de más de 10000 preguntas). Esta evaluación se puede personalizar para cualquier nivel de experiencia.

¿Cada candidato tiene las mismas preguntas?

Sí, te hace mucho más fácil comparar los candidatos. Las opciones para las preguntas de MCQ y el orden de las preguntas son aleatorizados. Tenemos características anti-trato/procuración en su lugar. En nuestro plan empresarial, también tenemos la opción de crear múltiples versiones de la misma evaluación con cuestiones de niveles de dificultad similares.