Skip to content

Prompts for a Job Interview Question

Prompt

1
2
3
4
5
6
7
Create a list of interview questions for a data scientist role.
The role uses extensive use of Python data science libraries.
Focus on skills using a knowledge graph.
Ask questions about creating and maintaining a data catalog.
Ask questions about metadata management.
Ask questions about using GitHub.
Ask questions about the Plotly data visualization library.

1
Return the results as a single raw Markdown file

Response

Data Scientist Interview Questions

Python Data Science Libraries

  1. General Proficiency: Can you describe your experience with Python data science libraries such as Pandas, NumPy, and SciPy? How have you applied these in past projects?
  2. Practical Application: Provide an example of a complex data manipulation task you've accomplished using Pandas.
  3. Problem-Solving: How would you handle large datasets in Python that don't fit into memory?

Knowledge Graph Skills

  1. Concept Understanding: What is a knowledge graph, and how is it relevant in data science?
  2. Implementation Experience: Describe a project where you implemented a knowledge graph. What challenges did you face and how did you overcome them?
  3. Tools and Technologies: What tools or libraries have you used for building or interacting with knowledge graphs in Python?

Data Catalog Creation and Maintenance

  1. Fundamentals: Can you explain what a data catalog is and its importance in data management?
  2. Experience: Describe your experience in creating and maintaining a data catalog. What tools or platforms did you use?
  3. Best Practices: What are some best practices for ensuring the accuracy and reliability of a data catalog?

Metadata Management

  1. Understanding Metadata: How do you define metadata in the context of data science, and why is it important?
  2. Practical Application: Can you describe a scenario where metadata management was crucial in your project? How did you handle it?
  3. Challenges: What are the most common challenges in metadata management, and how do you address them?

GitHub Usage

  1. Basic Usage: How do you use GitHub for version control in your data science projects?
  2. Collaboration: Can you discuss a time when you used GitHub for collaborative development on a data science project?
  3. Advanced Features: Are you familiar with advanced GitHub features like actions, projects, or workflows? Can you provide examples of how you've used them?

Plotly Data Visualization Library

  1. Library Proficiency: How have you used Plotly in your past data science projects? Provide specific examples.
  2. Comparative Analysis: How does Plotly compare to other visualization tools or libraries you have used?
  3. Advanced Visualization: Describe the most complex visualization you have created using Plotly. What were the challenges and how did you resolve them?