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About the test:

数据科学评估测试评估了候选人在统计,概率,线性和非线性回归模型方面的熟练程度及其分析数据并利用Python/ R的能力,从而从数据中提取见解。

Covered skills:

  • 机器学习技术
  • 用R或Python分析
  • 数据操纵
  • 回归分析
  • 预测建模
  • 数据可视化
  • 探索性数据分析
  • 统计数据
  • 数据清理

9 reasons why
9 reasons why

Adaface Data Science Test is the most accurate way to shortlist 数据科学家s



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:

  • 能够在数据分析中应用概率概念和原理
  • 分析和解释统计数据的能力
  • 实施机器学习算法和技术的能力
  • 能够有效地可视化和呈现数据
  • 使用R或Python进行数据分析和探索的能力
  • 有效操纵和转换数据的能力
  • 在回归分析中理解和应用统计概念的能力
  • 能够清洁和预处理数据进行分析
  • 能够为各种数据方案开发预测模型
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

这些只是我们库中有10,000多个问题的一个小样本。关于此的实际问题 数据科学评估测试 将是不可行的.

🧐 Question

Medium

Amazon electronics product feedback
Solve
Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this. 
The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend?
A: Remove the test samples with missing detailed feedback text fields
B: Generate synthetic data to fill in missing fields
C: Use an algorithm that handles missing data better than decision trees
D: Fill in the missing detailed feedback text field with the summary of feedback field.

Easy

Fraud detection model
Logistic Regression
Solve
Your friend T-Rex is working on a logistic regression model for a bank, for a fraud detection usecase. The accuracy of the model is 98%. T-Rex's manager's concern is that 85% of fraud cases are not being recognized by the model. Which of the following will surely help the model recognize more than 15% of fraud cases?

Medium

Rox's decision tree classifier
Decision Tree Classifier
Solve
Your data science intern Rox was asked to create a decision tree classifier with 12 input variables. The tree used 7 of the 12 variables, and was 5 levels deep. Few nodes of the tree contain 3 data points. The area under the curve (AUC) is 0.86. As Rox's mentor, what is your interpretation?
A. The AUC is high, and the small nodes are all very pure- the model looks accurate.
B. The tree might be overfitting- try fitting shallower trees and using an ensemble method.
C. The AUC is high, so overall the model is accurate. It might not be well-calibrated, because the small nodes will give poor estimates of probability.
D. The tree did not split on all the input variables. We need a larger data set to get a more accurate model.

Easy

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

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

Medium

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

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

Easy

n-gram generator
Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
 image

Easy

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

Easy

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

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

Medium

Green or red balls
Solve
A bag contains 5 red balls, 6 yellow balls and 3 green balls. If two balls are picked at random, what is the probability that both are red or both are green in colour?

Hard

Square points and Circle
Solve
What is the probability that two uniformly random points in the square are such that center of the square lies in the circle formed by taking the points as diameter

Easy

Frequency distribution
Solve
Convert the following into an ordinary frequency distribution:

- 5 users gave less than 3 rating
- 12 users gave less than 6 rating
- 25 users gave less than 9 ratings
- 33 users get less than 12 ratings
 image
🧐 Question🔧 Skill

Medium

Amazon electronics product feedback

2 mins

Data Science
Solve

Easy

Fraud detection model
Logistic Regression

2 mins

Data Science
Solve

Medium

Rox's decision tree classifier
Decision Tree Classifier

2 mins

Data Science
Solve

Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
Solve

Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
Solve

Easy

n-gram generator

2 mins

Machine Learning
Solve

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
Solve

Medium

Green or red balls

2 mins

Probability
Solve

Hard

Square points and Circle

3 mins

Probability
Solve

Easy

Frequency distribution

3 mins

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

1200+ customers in 75 countries

customers in 75 countries
Brandon

借助 Adaface,我们能够将初步筛选流程优化达 75% 以上,为招聘经理和我们的人才招聘团队节省了宝贵的时间!


Brandon Lee, 人事主管, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment 数据科学评估测试 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 数据科学评估测试 from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

查看样本记分卡
Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Data Science Online Test

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

The 数据科学评估测试 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:

  • 证明对概率理论及其在数据科学中的应用有深刻的了解。
  • 应用统计概念和技术来分析和解释数据。
  • 利用机器学习算法和模型来解决现实世界中的问题。
  • 创建具有视觉吸引力的数据可视化,以有效地传达洞察力。
  • 使用R或Python编程语言进行数据分析和操作。
  • 进行全面的探索性数据分析,以获得见解并确定模式。
  • 证明熟练的数据操纵技术清洁和预处理数据。
  • 应用回归分析以开发预测模型并进行准确的预测。
  • 具有数据清洁方面的高级技能,以确保数据质量和完整性。
  • 利用用于做出数据驱动决策的预测建模技术。

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?

  • 数据可视化:

    数据可视化涉及创建数据的视觉表示,以有效地传达洞察力和模式。该技能应该在测试中衡量,因为数据科学家必须以有意义且可理解的方式介绍复杂的数据,从而促进更好的决策和沟通。

  • 与R或Python分析:</h4> </h4> > <p>用R或Python进行分析是指使用诸如R或Python之类的编程语言执行数据分析,统计建模和机器学习任务。该技能应在测试中衡量,因为它评估了候选人在数据科学项目中应用编程技能的能力,证明了他们在处理数据和实施分析算法方面的熟练程度。</p> <h4>探索性数据分析:</p> </h4> </h4> < P>探索性数据分析涉及检查和转换数据以了解其主要特征,模式和关系。该技能应在测试中衡量,因为它展示了候选人从原始数据中提取有意义的见解,确定潜在问题并产生假设以进行进一步分析的能力。</p> <h4>数据操纵:

    数据操作是指转换,重新格式化或清洁数据以使其适合分析的过程。该技能应在测试中衡量,因为它评估了候选人在处理和准备数据方面的熟练程度,这在执行分析或建模任务之前是数据科学工作流程的关键一步。

  • 统计信息:</p> </h4>:

    统计数据涉及数据的收集,分析,解释,演示和组织。该技能应在测试中测量,因为它测试了候选人对统计概念和技术的理解和应用,这对于进行健壮且有效的数据分析至关重要。

  • 回归分析:

    回归分析是一种统计技术,用于模拟因变量与一个或多个自变量变量之间的关系。该技能应在测试中衡量,因为它评估了候选人执行回归分析的能力,该能力广泛用于预测性建模和理解变量对感兴趣结果的影响。

  • 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 数据科学评估测试 to be based on.

    概率分布
    假设检验
    中心极限定理
    置信区间
    线性回归
    逻辑回归
    决策树
    随机森林
    支持向量机
    k-near最邻居
    天真的贝叶斯
    K-均值聚类
    分层聚类
    主成分分析
    数据可视化技术
    数据可视化库(例如matplotlib,ggplot)
    数据探索技术
    探索性数据分析
    用R或Python进行数据操作
    数据清洁技术
    缺少数据插补
    异常值检测
    功能工程
    相关分析
    方差分析
    时间序列分析
    A/B测试
    模型评估和验证
    交叉验证技术
    特征选择方法
    降低降低技术
    合奏学习
    过度拟合和不足
    正则化技术
    偏见变化权衡
    数据预处理
    正常化
    标准化
    单速编码
    数据扩展
    重采样方法
    数据拆分技术
    模型评估指标
    R平方
    平方误差
    准确性
    精确和回忆
    F1得分
    ROC曲线分析
    高参数调整
    网格搜索
    交叉验证高参数调整
    模型部署
    API集成
    模型解释和解释
    可解释的机器学习模型
    沙普利价值

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

  • 数据科学家
  • 数据分析师
  • 机器学习工程师
  • 数据工程师
  • 业务分析师
  • 统计分析师
  • AI工程师
  • 人工智能角色

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

  • 利用聚类算法进行分类和分割分析。
  • 将时间序列分析应用于预测未来趋势和模式。
  • 展示自然语言处理算法和技术的知识。
  • 利用特征选择和提取技术来提高模型性能。
  • 采用降低降低方法进行数据可视化和分析。
  • 应用合奏学习技术以提高模型的准确性和性能。
  • 使用Matplotlib和GgPlot等库具有强大的数据可视化技能。
  • 利用统计检验和假设检验来做出数据驱动的决策。
  • 采用数据插补技术来处理数据集中的缺失值。
  • 应用交叉验证技术来评估模型性能并防止过度拟合。
  • 使用各种技术在处理不平衡数据集方面展示专业知识。
Singapore government logo

招聘经理认为,通过小组面试中提出的技术问题,他们能够判断哪些候选人得分更高,并与得分较差的候选人区分开来。他们是 非常满意 通过 Adaface 筛选入围的候选人的质量。


85%
减少筛查时间

Data Science Hiring Test 常见问题解答

数据科学在线测试包含哪些类型的问题?

数据科学测试通过基于场景的问题评估候选人的在职技能水平,重点关注候选人的能力:

  • 清理数据并查找异常情况
  • 使用训练/测试数据和 K-Fold 交叉验证来构建稳健的模型
  • 使用线性回归、多项式回归和多元回归进行预测
  • 使用 K 均值聚类、支持向量机 (SVM)、KNN、决策树、朴素贝叶斯和 PCA 对数据进行分类
  • 读取混淆矩阵
  • 了解偏差/方差权衡和过度拟合
  • 使用后向淘汰、前向选择和双向淘汰方法创建统计模型
  • 转换自变量并导出新的自变量以用于建模目的
  • 检查多重共线性
  • 了解并防止模型恶化

如何为高级数据科学家定制测试?

除了上述主题之外,高级数据科学家的测试还包括高级主题的问题,例如:

  • 高级数据操作,从大型非结构化数据集中生成见解
  • 特征工程
  • 超参数调优
  • 强化学习
  • 降维
  • 高级统计分析

数据科学测试是否评估数据科学能力或特定技术?

该测试的即用型版本侧重于数据科学能力——概率、统计和机器学习。如果您希望测试特定技术,您可以请求此测试的自定义版本。

我可以将多个技能结合在一起,为一个自定义评估吗?

是的,一点没错。自定义评估是根据您的职位描述进行的,并将包括有关您指定的所有必备技能的问题。

您是否有任何反交换或策略功能?

我们具有以下反交易功能:

  • 不可解决的问题
  • IP策略
  • Web Protoring
  • 网络摄像头Proctoring
  • 窃检测
  • 安全浏览器

阅读有关[Proctoring功能](https://www.adaface.com/proctoring)的更多信息。

如何解释考试成绩?

要记住的主要问题是评估是消除工具,而不是选择工具。优化了技能评估,以帮助您消除在技术上没有资格担任该角色的候选人,它没有进行优化以帮助您找到该角色的最佳候选人。因此,使用评估的理想方法是确定阈值分数(通常为55%,我们为您提供基准测试),并邀请所有在下一轮面试中得分高于门槛的候选人。

我可以使用该测试的经验水平?

每个ADAFACE评估都是为您的职位描述/理想候选角色定制的(我们的主题专家将从我们的10000多个问题的图书馆中选择正确的问题)。可以为任何经验级别定制此评估。

每个候选人都会得到同样的问题吗?

是的,这使您比较候选人变得容易得多。 MCQ问题的选项和问题顺序是随机的。我们有[抗欺骗/策略](https://www.adaface.com/proctoring)功能。在我们的企业计划中,我们还可以选择使用类似难度级别的问题创建多个版本的相同评估。

我是候选人。我可以尝试练习测试吗?

不,不幸的是,我们目前不支持实践测试。但是,您可以使用我们的[示例问题](https://www.adaface.com/questions)进行练习。

使用此测试的成本是多少?

您可以查看我们的[定价计划](https://www.adaface.com/pricing/)。

我可以免费试用吗?

我刚刚搬到了一个付费计划。我如何要求自定义评估?

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