Search test library by skills or roles
⌘ K

Data Science Assessment 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.

Get started for free
Preview questions

Screen candidates with a 35 mins test

Test duration:  35 mins
Difficulty level:  Moderate
Availability:  Ready to use
Questions:
  • 6 Probability MCQs
  • 6 Statistics MCQs
  • 6 Machine Learning MCQs
Covered skills:
Machine Learning Techniques
Data Visualization
Analytics with R or Python
Exploratory Data Analysis
Data Manipulation
Statistics
Regression Analysis
Data Cleansing
Predictive Modeling
Get started for free
Preview questions

Use Adaface tests trusted by recruitment teams globally

Adaface is used by 1500+ businesses in 80 countries.

Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

Amazon Morgan Stanley Vodafone United Nations HCL PayPal Bosch WeWork Optimum Solutions Deloitte NCS Sokrati J&T Express Capegemini

Use the Data Science Test to shortlist qualified candidates

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
Get started for free
Preview questions

Screen candidates with the highest quality 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 15,000+ 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

Test candidates on core Data Science Hiring Test topics

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.

Get started for free
Preview questions

Make informed decisions with actionable reports and benchmarks

View sample scorecard

Screen candidates in 3 easy steps

Pick a test from over 500+ tests

The Adaface test library features 500+ tests to enable you to test candidates on all popular skills- everything from programming languages, software frameworks, devops, logical reasoning, abstract reasoning, critical thinking, fluid intelligence, content marketing, talent acquisition, customer service, accounting, product management, sales and more.

Invite your candidates with 2-clicks

Make informed hiring decisions

Get started for free
Preview questions

Try the most advanced candidate assessment platform

ChatGPT Protection

Non-googleable Questions

Web Proctoring

IP Proctoring

Webcam Proctoring

MCQ Questions

Coding Questions

Typing Questions

Personality Questions

Custom Questions

Ready-to-use Tests

Custom Tests

Custom Branding

Bulk Invites

Public Links

ATS Integrations

Multiple Question Sets

Custom API integrations

Role-based Access

Priority Support

GDPR Compliance


Pick a plan based on your hiring needs

The most advanced candidate screening platform.
14-day free trial. No credit card required.

From
$15
per month (paid annually)
love bonito

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

Brandon
love bonito

It's very easy to share assessments with candidates and for candidates to use. We get good feedback from candidates about completing the tests. Adaface are very responsive and friendly to deal with.

Kirsty Wood, Human Resources, WillyWeather

Brandon
love bonito

We were able to close 106 positions in a record time of 45 days! Adaface enables us to conduct aptitude and psychometric assessments seamlessly. My hiring managers have never been happier with the quality of candidates shortlisted.

Amit Kataria, CHRO, Hanu

Brandon
love bonito

We evaluated several of their competitors and found Adaface to be the most compelling. Great library of questions that are designed to test for fit rather than memorization of algorithms.

Swayam Narain, CTO, Affable

Brandon

Have questions about the Data Science Hiring Test?

What roles can I use the Data Science Test for?

Here are few roles for which we recommend this test:

  • Data Scientist
  • Data Analyst
  • Machine Learning Engineer
  • Data Engineer
  • Business Analyst
  • Statistical Analyst
  • AI Engineer
  • Artificial Intelligence Roles
Can I combine the Data Science test with SQL questions?

Yes, you can request a custom test that includes both Data Science and SQL questions. For more details about our SQL assessment, please visit SQL Online Test.

How to use the Data Science Assessment Test in my hiring process?

We recommend using the test as a pre-screening tool. Add the assessment link in your job post or invite candidates via email. Adaface helps you identify skilled candidates early in the process.

What are the main Data Science-related tests?
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.

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.

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.

Can I get a free trial?

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

What is the Data Science Assessment Test?

The Data Science Assessment Test evaluates candidates on their knowledge of data science concepts, covering skills such as Machine Learning techniques, Data Visualization, and Predictive Modeling. Recruiters use this test to efficiently screen applicants and identify top talent.

What skills are assessed for senior data scientist roles?

Senior roles are evaluated on advanced competencies including clustering algorithms, time series analysis, natural language processing, feature selection and extraction, ensemble learning, data imputation, and handling imbalanced datasets.

Can I test Data Science and Machine Learning together in a test?

Yes, you can combine Data Science and Machine Learning questions into a single test. For more information, visit Machine Learning Assessment 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.

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.

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.

What is the cost of using this test?

You can check out our pricing plans.

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 1500+ companies in 80+ 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
ada
Ada
● Online
Previous
Score: NA
Next
✖️