Search test library by skills or roles
⌘ K

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:

  • Machine Learning Techniques
  • Analytics with R or Python
  • Data Manipulation
  • Regression Analysis
  • Predictive Modeling
  • Data Visualization
  • Exploratory Data Analysis
  • Statistics
  • Data Cleansing

9 reasons why
9 reasons why

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



Reason #1

Tests for on-the-job skills

The Data Science Assessment Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Ability to apply probability concepts and principles in data analysis
  • Ability to analyze and interpret statistical data
  • Ability to implement machine learning algorithms and techniques
  • Ability to visualize and present data effectively
  • Ability to perform data analysis and exploration using R or Python
  • Ability to manipulate and transform data efficiently
  • Ability to understand and apply statistical concepts in regression analysis
  • Ability to clean and preprocess data for analysis
  • Ability to develop predictive models for various data scenarios
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

These are just a small sample from our library of 10,000+ questions. The actual questions on this Data Science Assessment Test will be non-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:
 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

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, 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.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Data Science Assessment Test from your dashboard by entering their email address.

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

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

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
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 Data Science Assessment Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Demonstrate a strong understanding of probability theory and its applications in data science.
  • Apply statistical concepts and techniques to analyze and interpret data.
  • Utilize machine learning algorithms and models to solve real-world problems.
  • Create visually appealing data visualizations to effectively communicate insights.
  • Employ R or Python programming languages for data analytics and manipulation.
  • Conduct comprehensive exploratory data analysis to gain insights and identify patterns.
  • Demonstrate proficiency in data manipulation techniques to clean and preprocess data.
  • Apply regression analysis to develop predictive models and make accurate predictions.
  • Possess advanced skills in data cleansing to ensure data quality and integrity.
  • Leverage predictive modeling techniques for making data-driven decisions.

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?

  • Machine Learning Techniques:

    Machine learning techniques refer to the algorithms and methods used to train models that can automatically learn and improve from data without being explicitly programmed. This skill should be measured in the test as it is a fundamental component of data science, enabling data scientists to develop predictive models and make data-driven decisions.

  • Data Visualization:

    Data visualization involves creating visual representations of data to effectively communicate insights and patterns. This skill should be measured in the test as it is essential for data scientists to present complex data in a meaningful and understandable way, facilitating better decision-making and communication.

  • Analytics with R or Python:

    Analytics with R or Python refers to using programming languages such as R or Python to perform data analysis, statistical modeling, and machine learning tasks. This skill should be measured in the test as it assesses a candidate's ability to apply programming skills in data science projects, demonstrating their proficiency in handling data and implementing analytics algorithms.

  • Exploratory Data Analysis:

    Exploratory data analysis involves examining and transforming data to understand its main characteristics, patterns, and relationships. This skill should be measured in the test as it showcases a candidate's ability to extract meaningful insights from raw data, identify potential issues, and generate hypotheses for further analysis.

  • Data Manipulation:

    Data manipulation refers to the process of transforming, reformatting, or cleansing data to make it suitable for analysis. This skill should be measured in the test as it assesses a candidate's proficiency in handling and preparing data, which is a crucial step in the data science workflow before performing analytics or modeling tasks.

  • Statistics:

    Statistics involves the collection, analysis, interpretation, presentation, and organization of data. This skill should be measured in the test as it tests a candidate's understanding and application of statistical concepts and techniques, which are essential for conducting robust and valid data analysis.

  • Regression Analysis:

    Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. This skill should be measured in the test as it evaluates a candidate's ability to perform regression analysis, which is widely used in predictive modeling and understanding the impact of variables on an outcome of interest.

  • Data Cleansing:

    Data cleansing involves identifying and correcting or removing errors, inconsistencies, or inaccuracies in datasets. This skill should be measured in the test as it assesses a candidate's capability to ensure data quality, which is crucial for obtaining reliable and accurate results in data analysis and modeling tasks.

  • Predictive Modeling:

    Predictive modeling is the process of developing and deploying mathematical models to predict future events or outcomes based on historical data. This skill should be measured in the test as it evaluates a candidate's ability to build predictive models using appropriate algorithms and evaluate their performance, which is a key component of many data science projects.

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Data Science Assessment Test to be based on.

    Probability distributions
    Hypothesis testing
    Central limit theorem
    Confidence intervals
    Linear regression
    Logistic regression
    Decision trees
    Random forests
    Support vector machines
    k-nearest neighbors
    Naive Bayes
    K-means clustering
    Hierarchical clustering
    Principal component analysis
    Data visualization techniques
    Data visualization libraries (e.g., Matplotlib, ggplot)
    Data exploration techniques
    Exploratory data analysis
    Data manipulation with R or Python
    Data cleaning techniques
    Missing data imputation
    Outlier detection
    Feature engineering
    Correlation analysis
    ANOVA
    Time series analysis
    A/B testing
    Model evaluation and validation
    Cross-validation techniques
    Feature selection methods
    Dimensionality reduction techniques
    Ensemble learning
    Overfitting and underfitting
    Regularization techniques
    Bias-variance tradeoff
    Data preprocessing
    Normalization
    Standardization
    One-hot encoding
    Data scaling
    Resampling methods
    Data splitting techniques
    Model evaluation metrics
    R-squared
    Mean squared error
    Accuracy
    Precision and recall
    F1 score
    ROC curve analysis
    Hyperparameter tuning
    Grid search
    Cross-validation hyperparameter tuning
    Model deployment
    API integration
    Model interpretation and explanation
    Interpretable machine learning models
    Shapley values

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

  • Data Scientist
  • Data Analyst
  • Machine Learning Engineer
  • Data Engineer
  • Business Analyst
  • Statistical Analyst
  • AI Engineer
  • Artificial Intelligence Roles

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 clustering algorithms for classification and segmentation analysis.
  • Apply time series analysis to forecast future trends and patterns.
  • Demonstrate knowledge of natural language processing algorithms and techniques.
  • Utilize feature selection and extraction techniques to improve model performance.
  • Employ dimensionality reduction methods for data visualization and analysis.
  • Apply ensemble learning techniques for improved model accuracy and performance.
  • Possess strong skills in data visualization using libraries such as Matplotlib and ggplot.
  • Utilize statistical testing and hypothesis testing to make data-driven decisions.
  • Employ data imputation techniques to handle missing values in datasets.
  • Apply cross-validation techniques to assess model performance and prevent overfitting.
  • Demonstrate expertise in handling imbalanced datasets using various techniques.
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

Data Science Hiring Test FAQs

What type of questions does the Data Science online test contain?

The Data science test evaluates the on-the-job skill level of candidates with scenario based questions focusing on the candidate's ability to:

  • Clean data and look for anomalies
  • Use train/test data and K-Fold cross validation to build robust models
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Read a confusion matrix
  • Understand the bias/variance tradeoff and overfitting
  • Use backward elimination, forward selection, and bidirectional elimination methods to create statistical models
  • Transform independent variables and derive new independent variables for modelling purposes
  • Check for multicollinearity
  • Understand and prevent model deterioration

How will the test be customized for senior data scientists?

In addition to the topics mentioned above, tests for senior data scientists also include questions on advanced topics like:

  • Advanced data manipulation to generate insights from large, unstructured datasets
  • Feature Engineering
  • Hyperparameter Tuning
  • Reinforcement Learning
  • Dimensionality Reduction
  • Advanced statistical analysis

Does the data science test evaluate data science aptitude or specific technologies?

The ready-to-use version of this test focuses on data science aptitude- Probability, Statistics & Machine Learning. If you are looking to test for specific technologies, you can request a custom version of this test.

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

customers across world
Join 1200+ companies in 75+ countries.
Try the most candidate friendly skills assessment tool today.
g2 badges
Ready to use the Adaface Data Science Assessment Test?
Ready to use the Adaface Data Science Assessment Test?
logo
40 min tests.
No trick questions.
Accurate shortlisting.
Terms Privacy Trust Guide

🌎 Pick your language

English Norsk Dansk Deutsche Nederlands Svenska Français Español Chinese (简体中文) Italiano Japanese (日本語) Polskie Português Russian (русский)
ada
Ada
● Online
Previous
Score: NA
Next
✖️