Why you should use Data Science Assessment Test?

The Teste de avaliação de ciência de dados 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: Demonstrar um forte entendimento da teoria da probabilidade e suas aplicações na ciência de dados. Aplique conceitos e técnicas estatísticas para analisar e interpretar dados. Utilize algoritmos e modelos de aprendizado de máquina para resolver problemas do mundo real. Crie visualizações de dados visualmente atraentes para comunicar efetivamente insights. Empregar linguagens de programação R ou Python para análise de dados e manipulação. Realize uma análise abrangente de dados exploratórios para obter insights e identificar padrões. Demonstre proficiência em técnicas de manipulação de dados para limpar e pré -processar dados. Aplique a análise de regressão para desenvolver modelos preditivos e fazer previsões precisas. Possui habilidades avançadas na limpeza de dados para garantir a qualidade e a integridade dos dados. Aproveite as técnicas de modelagem preditiva para tomar decisões orientadas a dados.

Data Science Assessment Test

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

Técnicas de aprendizado de máquina Visualização de dados Análise com r ou python Análise exploratória de dados Manipulação de dados Estatisticas Análise de regressão Limpeza de dados Modelagem preditiva

About the test:

O teste de avaliação da ciência de dados avalia a proficiência de um candidato em estatísticas, probabilidade, modelos de regressão linear e não linear e sua capacidade de analisar dados e aproveitar o Python/ R para extrair insights dos dados.

Covered skills:

Técnicas de aprendizado de máquina
Análise com r ou python
Manipulação de dados
Análise de regressão
Modelagem preditiva

Visualização de dados
Análise exploratória de dados
Estatisticas
Limpeza de dados

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9 reasons why

Adaface Data Science Test is the most accurate way to shortlist Cientista de dadoss

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:

Capacidade de aplicar conceitos e princípios de probabilidade na análise de dados
Capacidade de analisar e interpretar dados estatísticos
Capacidade de implementar algoritmos e técnicas de aprendizado de máquina
Capacidade de visualizar e apresentar dados de maneira eficaz
Capacidade de executar a análise e exploração de dados usando R ou Python
Capacidade de manipular e transformar dados com eficiência
Capacidade de entender e aplicar conceitos estatísticos em análise de regressão
Capacidade de limpar e pré -processar dados para análise
Capacidade de desenvolver modelos preditivos para vários cenários de dados

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

Estes são apenas uma pequena amostra da nossa biblioteca de mais de 10.000 perguntas. As perguntas reais sobre isso Teste de avaliação de ciência de dados será não-googleable.

🧐 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

Com o Adaface, conseguimos otimizar nosso processo de seleção inicial em mais de 75%, liberando um tempo precioso tanto para os gerentes de contratação quanto para nossa equipe de aquisição de talentos!

Brandon Lee, Chefe de Pessoas, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Teste de avaliação de ciência de dados 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 Teste de avaliação de ciência de dados 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 Teste de avaliação de ciência de dados 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:

Demonstrar um forte entendimento da teoria da probabilidade e suas aplicações na ciência de dados.
Aplique conceitos e técnicas estatísticas para analisar e interpretar dados.
Utilize algoritmos e modelos de aprendizado de máquina para resolver problemas do mundo real.
Crie visualizações de dados visualmente atraentes para comunicar efetivamente insights.
Empregar linguagens de programação R ou Python para análise de dados e manipulação.
Realize uma análise abrangente de dados exploratórios para obter insights e identificar padrões.
Demonstre proficiência em técnicas de manipulação de dados para limpar e pré -processar dados.
Aplique a análise de regressão para desenvolver modelos preditivos e fazer previsões precisas.
Possui habilidades avançadas na limpeza de dados para garantir a qualidade e a integridade dos dados.
Aproveite as técnicas de modelagem preditiva para tomar decisões orientadas a dados.

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?

Visualização de dados:
Dados A visualização envolve a criação de representações visuais de dados para comunicar efetivamente insights e padrões. Essa habilidade deve ser medida no teste, pois é essencial para os cientistas de dados apresentarem dados complexos de maneira significativa e compreensível, facilitando uma melhor tomada de decisão e comunicação. > <p> Analytics com R ou Python refere -se ao uso de linguagens de programação, como R ou Python, para executar a análise de dados, modelagem estatística e tarefas de aprendizado de máquina. Essa habilidade deve ser medida no teste, pois avalia a capacidade de um candidato de aplicar habilidades de programação em projetos de ciências de dados, demonstrando sua proficiência em lidar com dados e implementar algoritmos de análise de análise. A análise de dados exploratórios envolve examinar e transformar dados para entender suas principais características, padrões e relacionamentos. Essa habilidade deve ser medida no teste, pois mostra a capacidade de um candidato de extrair insights significativos dos dados brutos, identificar possíveis problemas e gerar hipóteses para análise posterior.
manipulação de dados:
A manipulação de dados refere -se ao processo de transformação, reformatação ou limpeza de dados para torná -los adequados para análise. Essa habilidade deve ser medida no teste, pois avalia a proficiência de um candidato no manuseio e preparação de dados, que é uma etapa crucial no fluxo de trabalho da ciência de dados antes de executar tarefas de análise ou modelagem.
Estatísticas:
As estatísticas envolvem a coleta, análise, interpretação, apresentação e organização dos dados. Essa habilidade deve ser medida no teste, pois testa a compreensão e a aplicação de um candidato de conceitos e técnicas estatísticas, essenciais para a realização de análises de dados robustas e válidas.
Análise de regressão:
A análise de regressão é uma técnica estatística usada para modelar a relação entre uma variável dependente e uma ou mais variáveis independentes. Essa habilidade deve ser medida no teste, pois avalia a capacidade de um candidato de realizar a análise de regressão, que é amplamente utilizada na modelagem preditiva e na compreensão do impacto das variáveis no resultado de 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 Teste de avaliação de ciência de dados to be based on.
Distribuições de probabilidade
Testando hipóteses
Teorema do limite central
Intervalos de confiança
Regressão linear
Regressão logística
Árvores de decisão
Florestas aleatórias
Máquinas vetoriais de suporte
Vizinhos mais antigos
Baías ingénuas
Cluster de k-means
Agrupamento hierárquico
Análise do componente principal
Técnicas de visualização de dados
Bibliotecas de visualização de dados (por exemplo, matplotlib, ggplot)
Técnicas de exploração de dados
Análise exploratória de dados
Manipulação de dados com r ou python
Técnicas de limpeza de dados
Falta de imputação de dados
Detecção Outlier
Engenharia de recursos
Análise de correlação
ANOVA
Análise de séries temporais
Teste A/B
Avaliação e validação de modelos
Técnicas de validação cruzada
Métodos de seleção de recursos
Técnicas de redução da dimensionalidade
Aprendizado de conjunto
Excedente de ajuste e subjuste
Técnicas de regularização
Tradeoff de viés-variação
Pré -processamento de dados
Normalização
estandardização
Codificação única
Escala de dados
Métodos de reamostragem
Técnicas de divisão de dados
Métricas de avaliação de modelos
R-quadrado
Erro ao quadrado médio
Precisão
Precisão e recall
Pontuação F1
Análise da curva ROC
Ajuste hiperparâmetro
Pesquisa de grade
Ajuste hiperparâmetro de validação cruzada
Modelo de implantação
Integração da API
Interpretação e explicação do modelo
Modelos interpretáveis de aprendizado de máquina
Valores da Shapley

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

Cientista de dados
Analista de informações
Engenheiro de aprendizado de máquina
Engenheiro de dados
Analista de negócios
Analista estatístico
Engenheiro de IA
Papéis de inteligência 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

Utilize algoritmos de agrupamento para análise de classificação e segmentação.
Aplique a análise de séries temporais para prever tendências e padrões futuros.
Demonstre o conhecimento dos algoritmos e técnicas de processamento de linguagem natural.
Utilize técnicas de seleção e extração de recursos para melhorar o desempenho do modelo.
Empregar métodos de redução de dimensionalidade para visualização e análise de dados.
Aplique técnicas de aprendizado de conjunto para melhorar a precisão e o desempenho do modelo.
Possuir fortes habilidades na visualização de dados usando bibliotecas como Matplotlib e GGPlot.
Utilize testes estatísticos e testes de hipóteses para tomar decisões orientadas a dados.
Empregue técnicas de imputação de dados para lidar com valores ausentes nos conjuntos de dados.
Aplique técnicas de validação cruzada para avaliar o desempenho do modelo e evitar o ajuste excessivo.
Demonstre especialização em lidar com conjuntos de dados desequilibrados usando várias técnicas.

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Os gerentes de contratação sentiram que, por meio das perguntas técnicas feitas durante as entrevistas do painel, foram capazes de dizer quais candidatos tiveram melhores pontuações e diferenciaram aqueles que não tiveram pontuações tão boas. Eles são altamente satisfeito com a qualidade dos candidatos selecionados na triagem Adaface.

85%

Redução no tempo de triagem

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Data Science Hiring Test Perguntas frequentes

Que tipo de perguntas contém o teste online de Data Science?

O teste de ciência de dados avalia o nível de habilidade prática dos candidatos com perguntas baseadas em cenários com foco na capacidade do candidato de:

Limpe dados e procure anomalias
Use dados de treinamento/teste e validação cruzada K-Fold para construir modelos robustos
Faça previsões usando regressão linear, regressão polinomial e regressão multivariada
Classificar dados usando clustering K-Means, Support Vector Machines (SVM), KNN, árvores de decisão, Naive Bayes e PCA
Leia uma matriz de confusão
Compreender a compensação entre viés/variância e overfitting
Use métodos de eliminação retroativa, seleção direta e eliminação bidirecional para criar modelos estatísticos
Transformar variáveis independentes e derivar novas variáveis independentes para fins de modelagem
Verifique a multicolinearidade
Compreender e prevenir a deterioração do modelo

Como o teste será personalizado para cientistas de dados seniores?

Além dos tópicos mencionados acima, os testes para cientistas de dados seniores também incluem questões sobre tópicos avançados como:

Manipulação avançada de dados para gerar insights a partir de grandes conjuntos de dados não estruturados
Engenharia de recursos
Ajuste de hiperparâmetros
Aprendizagem por Reforço
Redução de dimensionalidade
Análise estatística avançada

O teste de ciência de dados avalia a aptidão em ciência de dados ou tecnologias específicas?

A versão pronta para uso deste teste concentra-se na aptidão em ciência de dados - Probabilidade, Estatística e Aprendizado de Máquina. Se você deseja testar tecnologias específicas, pode solicitar uma versão personalizada deste teste.

Posso combinar várias habilidades em uma avaliação personalizada?

Sim absolutamente. As avaliações personalizadas são configuradas com base na descrição do seu trabalho e incluirão perguntas sobre todas as habilidades obrigatórias que você especificar.

Você tem algum recurso anti-trapaça ou procurador?

Temos os seguintes recursos anti-trapaça:

Perguntas não-goleadas
IP Proctoring
Web Proctoring
Proctoring da webcam
Detecção de plágio
navegador seguro

Leia mais sobre os Recursos de Proctoring.

Como interpreto as pontuações dos testes?

O principal a ter em mente é que uma avaliação é uma ferramenta de eliminação, não uma ferramenta de seleção. Uma avaliação de habilidades é otimizada para ajudá -lo a eliminar os candidatos que não são tecnicamente qualificados para o papel, não é otimizado para ajudá -lo a encontrar o melhor candidato para o papel. Portanto, a maneira ideal de usar uma avaliação é decidir uma pontuação limite (normalmente 55%, ajudamos você a comparar) e convidar todos os candidatos que pontuam acima do limiar para as próximas rodadas da entrevista.

Para que nível de experiência posso usar este teste?

Cada avaliação do Adaface é personalizada para a descrição do seu trabalho/ persona do candidato ideal (nossos especialistas no assunto escolherão as perguntas certas para sua avaliação de nossa biblioteca de mais de 10000 perguntas). Esta avaliação pode ser personalizada para qualquer nível de experiência.

Todo candidato recebe as mesmas perguntas?

Sim, facilita muito a comparação de candidatos. As opções para perguntas do MCQ e a ordem das perguntas são randomizadas. Recursos anti-traking/proctoring no local. Em nosso plano corporativo, também temos a opção de criar várias versões da mesma avaliação com questões de níveis de dificuldade semelhantes.