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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What are the key components that should be included in a data science portfolio?
💡 Hint: Think about what would demonstrate your skills effectively.
Question 2
Easy
Why is documentation important in a project?
💡 Hint: Consider the role of communication in data science.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the minimum number of projects to include in a data science portfolio?
- 1
- 2-3
- 4-5
💡 Hint: Think about quality versus quantity.
Question 2
True or False: Dashboards are mandatory components of a data science portfolio.
- True
- False
💡 Hint: Recall the flexibility in building portfolios.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have completed a project predicting customer churn. Outline a presentation plan for your portfolio that includes project documentation, code sharing, and EDA.
💡 Hint: Consider the flow of information that caters to both a technical and non-technical audience.
Question 2
Devise a strategy for enriching a data science portfolio beyond project examples. Discuss at least three strategies.
💡 Hint: Think about engaging with the data science community and continuing learning.
Challenge and get performance evaluation