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Python for Data Science Test

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Type

Role Specific Skills

Time

10 minutes

Level

Medium

Questions

10

About the Test

Python is the programming language of choice for data scientists. Although it wasn’t the first primary programming language, its popularity has grown throughout the years.
This test looks at candidates’ understanding and abilities in Data Analysis, Machine Learning, Data Visualization, and Data Manipulation.

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Skills Measured

  • Basic Python
  • Data Analysis
  • Machine Learning
  • Data Visualization
  • Data Manipulation

Roles

  • Data Scientists
  • Python programmers
  • Data Analysts
  • Data Engineer
  • Machine Learning Engineer

Hire the best, every time, anywhere


4.9

Customer Satisfaction

Testlify helps you identify the best talent from anywhere in the world, with a seamless experience that candidates and hiring teams love every step of the way.

1

Basic Python

Basic Python is a foundational skill for data science professionals. It involves understanding the core concepts and syntax of the Python programming language, including data types, variables, loops, and control structures.

2

Data Analysis

Data analysis uses statistical and analytical techniques to extract insights and meaning from data. In data science, data analysts use tools such as NumPy, Pandas, and SciPy to perform tasks such as importing, cleaning, preparing, and calculating summary statistics.

3

Machine Learning

Machine learning is a subfield of data science that involves using algorithms and statistical models to allow a system to learn and improve automatically from data, without being explicitly programmed. In Python, data science professionals can use libraries such as scikit-learn and TensorFlow to build and train machine learning models.

4

Data Visualization

Data visualization is the process of creating visual representations of data to facilitate understanding and communication of insights. In Python, data science professionals can use libraries such as Matplotlib, Seaborn, and Plotly to create a wide range of static and interactive visualizations.

5

Data Manipulation

Data manipulation is the process of cleaning, transforming, and reshaping data to prepare it for analysis. In Python, data science professionals can use tools such as Pandas to perform tasks such as filtering, aggregating, and pivoting data.

The test is created by a subject-matter expert

Testlify’s skill tests are designed by experienced SMEs (subject matter experts). We evaluate these experts based on specific metrics such as expertise, capability, and their market reputation. Prior to being published, each skill test is peer-reviewed by other experts and then calibrated based on insights derived from a significant number of test-takers who are well-versed in that skill area. Our inherent feedback systems and built-in algorithms enable our SMEs to refine our tests continually.

View sample score cards

Top five hard skills interview questions for Python for Data Science

1. Can you explain the concept of lambda functions in Python and give an example of how you would use them in a data science project?
Why this Matters?

Lambda functions are a powerful feature in Python that can make code more concise and readable. A data science professional who can effectively use lambda functions will likely be more efficient and productive.

What to listen for?

Look for the candidate to demonstrate an understanding of how lambda functions work, and give an example of how they could be used in a data science context, such as in filtering or transforming data.

2. How would you use NumPy and Pandas libraries to manipulate and analyze data in Python?
Why this Matters?

NumPy and Pandas are two essential libraries in the Python data science ecosystem. A candidate who is proficient with these libraries will likely be well-equipped to handle a wide variety of data manipulation and analysis tasks.

What to listen for?

Look for the candidate to demonstrate an understanding of key NumPy and Pandas functions, such as indexing and slicing data, applying functions to data, and joining and grouping data.

3. How would you use machine learning algorithms to build a predictive model using scikit-learn library?
Why this Matters?

Building predictive models is a core skill for many data science roles. A candidate who is proficient with scikit-learn and machine learning algorithms will be able to effectively build models to make predictions or draw insights from data.

What to listen for?

Look for the candidate to demonstrate an understanding of key machine learning concepts, such as classification and regression, as well as an ability to explain how different algorithms work and when to use them.

4. How would you use Matplotlib and Seaborn libraries to create visualizations in Python?
Why this Matters?

Visualizing data is a critical step in data analysis, and Matplotlib and Seaborn are two of the most popular visualization libraries in Python. A candidate who can effectively use these libraries will be able to create compelling and informative visualizations.

What to listen for?

Look for the candidate to demonstrate an understanding of key visualization concepts, such as choosing appropriate chart types and customizing chart appearance, as well as an ability to explain how different types of charts can be used to reveal insights in different types of data.

5. Can you walk me through a time-series analysis project you worked on, and explain the techniques you used to analyze and visualize the data?
Why this Matters?

Time-series analysis is a common task in many data science roles, particularly in finance, marketing, and other industries where data changes over time. A candidate who has experience with time-series analysis will likely be well-equipped to handle a wide variety of data analysis tasks.

What to listen for?

Look for the candidate to describe a specific project they worked on and how they approached the data analysis process, including any challenges they faced and how they overcame them. Listen for an ability to explain the key concepts and techniques used in time-series analysis, such as trend analysis, seasonal decomposition, and forecasting.

Frequently Asked Questions for Python for Data Science

Python is the programming language of choice for data scientists. Although it wasn’t the first primary programming language, its popularity has grown. This assessment evaluates and administers candidates during the hiring process to assess their familiarity with or proficiency in using Python for data science tasks.

This test can help you identify individuals that have prior experience in Python and know how to utilize Python for Data Science. This test looks at candidates’ understanding and abilities in Data Analysis, Machine Learning, Data Visualization, and Data Manipulation.

  • Data Scientists
  • Python programmers
  • Data Analysts
  • Data Engineer
  • Machine Learning Engineer

  • Basic Python
  • Data Analysis
  • Machine Learning
  • Data Visualization
  • Data Manipulation

  • Writing and testing code using the Python programming language and related frameworks and libraries.
  • Debugging and troubleshooting code to resolve issues and improve performance.
  • Collaborating with cross-functional teams to understand project requirements and design solutions.
  • Maintaining and updating existing codebases.

Frequently Asked Questions (FAQs)

Want to know more about Testlify? Here are answers to the most commonly asked questions about our company.

These are ready-made tests, existing in our test library, created by subject matter experts. We have 200+ such skills tests covering various skills from programming skills to DevOps, including Aptitude and Personality testing.

You can go to the ‘Test Library’ tab and search for tests from the Job Role or Test Type drop-down menu.

Currently, we do not offer any sample tests. However, when you select a test, there would be a few practice questions before the start of the actual test, which would give you a fair idea of how the entire test would look.

Our tests typically take between 25-30 minutes to complete.

In case you need to test for a unique skill-set or specialized experience, you can add your own questions and customize the test to suit your requirements.

We’ve put a lot of effort into ensuring a conducive test environment. A stable internet connection, an internet browser with cookies and Java-Script enabled is all that is required!
However, for a smooth test-taking process, we recommend the following browsers:
– Google Chrome
– Internet Expl
– Mozilla Firefox

Yes, our tests are compatible with almost all modern mobile devices (including tablets) that have a webcam installed.

Yes, our tests are EEOC (Equal Employment Opportunity Commission) compliant and are whetted by subject matter experts, thus having high reliability (test consistency) and validity (accuracy of the test).

Yes! We integrate with popular recruiting tools such as Greenhouse, Lever, GoodTime, and many more.

We are GDPR compliant and take data security very seriously. You have complete ownership of your data. All your data is safe and secure, and we do not expose it to any third party.

Testlify is an excellent tool for assessing candidates’ soft and hard skills. The founder and customer success team were helpful during onboarding and demonstrated a commitment to continuously improving the platform.
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Founder, ExpHire
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Meir Shachar
CEO at PowerLinx
With Testlify, we were able to optimize our initial screening process by upwards of 75%.
We saved a tremendous amount
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President, Arch Advisory Group
Testlify has revolutionized our hiring process by streamlining the screening stage. It has saved us countless hours by allowing us to shortlist the right candidates.
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