Descriptive Statistics Part-3 | 38/100 Days of Python Algo Trading

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Congratulations on reaching this stage!

If you’re here, it means you’re truly dedicated to mastering Python. Directly below, you’ll find a quiz designed to help you reinforce what you’ve just learned. This isn’t just about recalling facts; it’s about deeply understanding the concepts. Take a moment to answer the questions and see how well you can apply the knowledge from the video. You’re doing great—every question you tackle brings you one step closer to becoming a Python expert!

Day 38: Descriptive Statistics Part- 3

1. What is the primary purpose of Exploratory Data Analysis (EDA) in algorithmic trading?
2. Which of the following best describes univariate analysis?
3. Why is EDA important before building a trading algorithm?
4. What is the first step in the EDA process?
5. How can you visualize the distribution of a single variable in a trading dataset?
6. What is the purpose of bivariate analysis in EDA?
7. How does feature engineering contribute to the performance of trading algorithms?
8. Which pandas function is used to compute summary statistics for numerical columns?
9. What is the role of data cleaning in the EDA process?
10. Which visualization technique is most appropriate for showing the relationship between two continuous variables?
11. What does a box plot reveal about a trading dataset?
12. How can you detect outliers in a trading dataset during EDA?
13. Why is it important to understand the correlation between variables in a trading dataset?
14. What is the purpose of using histograms in univariate analysis?
15. How can feature engineering help in improving model accuracy?
16. What is the significance of using scatter plots in bivariate analysis?
17. How do you handle missing data during the EDA process?
18. What is the impact of scaling features on the performance of a trading algorithm?
19. Which technique is used to reduce the dimensionality of a dataset in feature engineering?
20. What is the importance of understanding data distribution in EDA?

 

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