Why you should use Data Science Assessment Test?

The Prueba de evaluación de la ciencia de datos 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: Demostrar una fuerte comprensión de la teoría de la probabilidad y sus aplicaciones en la ciencia de datos. Aplicar conceptos y técnicas estadísticas para analizar e interpretar datos. Utilice algoritmos y modelos de aprendizaje automático para resolver problemas del mundo real. Cree visualizaciones de datos visualmente atractivas para comunicar de manera efectiva las ideas. Emplea lenguajes de programación R o Python para análisis de datos y manipulación. Realice un análisis de datos exploratorios integrales para obtener información e identificar patrones. Demuestre competencia en las técnicas de manipulación de datos para limpiar y preprocesar datos. Aplique el análisis de regresión para desarrollar modelos predictivos y hacer predicciones precisas. Poseer habilidades avanzadas en la limpieza de datos para garantizar la calidad y la integridad de los datos. Aproveche las técnicas de modelado predictivo para tomar decisiones basadas en datos.

Data Science Assessment Test

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

Técnicas de aprendizaje automático Visualización de datos Análisis con R o Python Análisis exploratorio de datos Manipulación de datos Estadísticas Análisis de regresión Limpieza de datos Modelado predictivo

About the test:

La prueba de evaluación de la ciencia de datos evalúa la competencia de un candidato en estadísticas, probabilidad, modelos de regresión lineal y no lineal y su capacidad para analizar datos y aprovechar Python/ R para extraer información de los datos.

Covered skills:

Técnicas de aprendizaje automático
Análisis con R o Python
Manipulación de datos
Análisis de regresión
Modelado predictivo

Visualización de datos
Análisis exploratorio de datos
Estadísticas
Limpieza de datos

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9 reasons why

Adaface Data Science Test is the most accurate way to shortlist Científico de datoss

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:

Capacidad para aplicar conceptos y principios de probabilidad en el análisis de datos
Capacidad para analizar e interpretar datos estadísticos
Capacidad para implementar algoritmos y técnicas de aprendizaje automático
Capacidad para visualizar y presentar datos de manera efectiva
Capacidad para realizar análisis de datos y exploración utilizando R o Python
Capacidad para manipular y transformar datos de manera eficiente
Capacidad para comprender y aplicar conceptos estadísticos en el análisis de regresión
Capacidad para limpiar y preprocesar datos para el análisis
Capacidad para desarrollar modelos predictivos para varios escenarios de datos

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

Estas son solo una pequeña muestra de nuestra biblioteca de más de 10,000 preguntas. Las preguntas reales sobre esto Prueba de evaluación de la ciencia de datos 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.
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

Con Adaface, pudimos optimizar nuestro proceso de selección inicial en más de un 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

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Prueba de evaluación de la ciencia de datos 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 Prueba de evaluación de la ciencia de datos 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 Prueba de evaluación de la ciencia de datos 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:

Demostrar una fuerte comprensión de la teoría de la probabilidad y sus aplicaciones en la ciencia de datos.
Aplicar conceptos y técnicas estadísticas para analizar e interpretar datos.
Utilice algoritmos y modelos de aprendizaje automático para resolver problemas del mundo real.
Cree visualizaciones de datos visualmente atractivas para comunicar de manera efectiva las ideas.
Emplea lenguajes de programación R o Python para análisis de datos y manipulación.
Realice un análisis de datos exploratorios integrales para obtener información e identificar patrones.
Demuestre competencia en las técnicas de manipulación de datos para limpiar y preprocesar datos.
Aplique el análisis de regresión para desarrollar modelos predictivos y hacer predicciones precisas.
Poseer habilidades avanzadas en la limpieza de datos para garantizar la calidad y la integridad de los datos.
Aproveche las técnicas de modelado predictivo para tomar decisiones basadas en datos.

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?

Técnicas de aprendizaje automático:
Las técnicas de aprendizaje automático se refieren a los algoritmos y métodos utilizados para entrenar modelos que pueden aprender y mejorar automáticamente de los datos sin ser programados explícitamente. Esta habilidad debe medirse en la prueba, ya que es un componente fundamental de la ciencia de datos, lo que permite a los científicos de datos desarrollar modelos predictivos y tomar decisiones basadas en datos.
Visualización de datos:
Datos La visualización implica la creación de representaciones visuales de datos para comunicar efectivamente ideas y patrones. Esta habilidad debe medirse en la prueba, ya que es esencial que los científicos de datos presenten datos complejos de una manera significativa y comprensible, facilitando una mejor toma de decisiones y comunicación.
Análisis con R o Python: </H4 > <p> Analytics con R o Python se refiere a usar lenguajes de programación como R o Python para realizar análisis de datos, modelado estadístico y tareas de aprendizaje automático. Esta habilidad debe medirse en la prueba, ya que evalúa la capacidad de un candidato para aplicar habilidades de programación en proyectos de ciencia de datos, demostrando su competencia en el manejo de datos e implementación de algoritmos de análisis. P> El análisis de datos exploratorios implica examinar y transformar datos para comprender sus principales características, patrones y relaciones. Esta habilidad debe medirse en la prueba, ya que muestra la capacidad de un candidato para extraer ideas significativas de datos sin procesar, identificar problemas potenciales y generar hipótesis para un análisis posterior. </p> <h4> Manipulación de datos:
La manipulación de datos se refiere al proceso de transformación, reformateo o limpieza de datos para que sea adecuado para el análisis. Esta habilidad debe medirse en la prueba, ya que evalúa el dominio de un candidato en el manejo y la preparación de datos, que es un paso crucial en el flujo de trabajo de la ciencia de datos antes de realizar análisis de análisis o modelado. <p> Las estadísticas implican la recopilación, análisis, interpretación, presentación y organización de datos. Esta habilidad debe medirse en la prueba, ya que prueba la comprensión de un candidato y la aplicación de conceptos y técnicas estadísticas, que son esenciales para realizar un análisis de datos robusto y válido.
Análisis de regresión:
El análisis de regresión es una técnica estadística utilizada para modelar la relación entre una variable dependiente y una o más variables independientes. Esta habilidad debe medirse en la prueba, ya que evalúa la capacidad de un candidato para realizar un análisis de regresión, que se usa ampliamente en el modelado predictivo y la comprensión del impacto de las variables en un resultado de interés. La limpieza de datos H4> <p> implica identificar y corregir o eliminar errores, inconsistencias o inexactitudes en conjuntos de datos. Esta habilidad debe medirse en la prueba, ya que evalúa la capacidad de un candidato para garantizar la calidad de los datos, lo cual es crucial para obtener resultados confiables y precisos en el análisis de datos y las tareas de modelado.
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 Prueba de evaluación de la ciencia de datos to be based on.
Distribuciones de probabilidad
Evaluación de la hipótesis
Teorema del límite central
Intervalos de confianza
Regresión lineal
Regresión logística
Árboles de decisión
Bosques aleatorios
Máquinas de vectores de soporte
K-Nearsest Vecinos
Bayes ingenuos
Clúster K-means
Agrupación jerárquica
Análisis de componentes principales
Técnicas de visualización de datos
Bibliotecas de visualización de datos (por ejemplo, matplotlib, ggplot)
Técnicas de exploración de datos
Análisis exploratorio de datos
Manipulación de datos con R o Python
Técnicas de limpieza de datos
Imputación de datos faltante
Detección atípica
Ingeniería de características
Análisis de correlación
Anova
Análisis de series temporales
Prueba A/B
Evaluación y validación del modelo
Técnicas de validación cruzada
Métodos de selección de características
Técnicas de reducción de dimensionalidad
Aprendizaje de conjunto
Sobrecargado y poco atajón
Técnicas de regularización
Compensación de sesgo-varianza
Preprocesamiento de datos
Normalización
Estandarización
Codificación de un solo estado
Escala de datos
Métodos de remuestreo
Técnicas de división de datos
Métricas de evaluación del modelo
R-cuadrado
Error medio cuadrado
Exactitud
Precisión y retiro
Puntaje F1
Análisis de la curva ROC
Ajuste de hiperparameter
Búsqueda de redes
Ajuste de hiperparámetro de validación cruzada
Despliegue de modelo
Integración de API
Interpretación y explicación del modelo
Modelos de aprendizaje automático interpretable
Valores de Shapley

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

Científico de datos
Analista de datos
Ingeniero de aprendizaje automático
Ingeniero de datos
Analista de negocios
Analista estadístico
Ingeniero de IA
Roles de inteligencia artificial

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

Utilice algoritmos de agrupación para el análisis de clasificación y segmentación.
Aplicar análisis de series de tiempo para pronosticar tendencias y patrones futuros.
Demostrar conocimiento de los algoritmos y técnicas de procesamiento del lenguaje natural.
Utilice técnicas de selección y extracción de características para mejorar el rendimiento del modelo.
Emplear métodos de reducción de dimensionalidad para la visualización y análisis de datos.
Aplicar técnicas de aprendizaje de conjunto para mejorar la precisión y el rendimiento del modelo.
Poseer fuertes habilidades en la visualización de datos utilizando bibliotecas como Matplotlib y GGPLOT.
Utilice pruebas estadísticas y pruebas de hipótesis para tomar decisiones basadas en datos.
Emplear técnicas de imputación de datos para manejar los valores faltantes en los conjuntos de datos.
Aplique técnicas de validación cruzada para evaluar el rendimiento del modelo y evitar el sobreajuste.
Demostrar experiencia en el manejo de conjuntos de datos desequilibrados utilizando diversas técnicas.

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Los gerentes de contratación sintieron que a través de las preguntas técnicas que hicieron durante las entrevistas del panel, pudieron decir qué candidatos tenían mejores puntajes y diferenciarse de aquellos que no obtuvieron tan buenos puntajes. Ellos son altamente satisfecho con la calidad de los candidatos preseleccionados con la selección de Adaface.

85%

Reducción en el tiempo de detección

Ver estudio de caso completo

Data Science Hiring Test Preguntas frecuentes

¿Qué tipo de preguntas contiene el test online de Ciencia de Datos?

#VALUE!

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

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

Manipulación avanzada de datos para generar conocimientos a partir de grandes conjuntos de datos no estructurados.
Ingeniería de funciones
Ajuste de hiperparámetros
Aprendizaje reforzado
Reducción de dimensionalidad
Análisis estadístico avanzado

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

La versión lista para usar de esta prueba se centra en la aptitud para la ciencia de datos: probabilidad, estadística y aprendizaje automático. Si desea realizar pruebas para 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.