Foundations of Data Science: Important Part B Questions

Python for Data Science

Chapter 3: Foundations of Data Science

Important Part B Questions

1. Explain how will you define the research goals during the data analysis process?

2. What is project charter? Explain.

3. Explain what is meant by data retrieval in the data science process.

4. Why is data retrieval considered an important step in data science?

5. What are the main sources of data used in data science projects?

6. What challenges might a data scientist face during the data retrieval stage?

7. Why should data quality checks be performed during the data retrieval stage?

8. What is data preparation in the context of data science? Explain its importance.

9. State the main components of data preparation and briefly describe each.

10. What is data cleaning? Why is it important in data preparation?

11. Why is data preparation considered one of the most time‒consuming steps in data science?

12. List and explain five common types of data errors that can occur in datasets.

13. Describe any four common types of data transformation with suitable examples.

14. What is data combining (data integration)? Explain with an example.

15. Why is it necessary to combine data from different sources during data preparation?

16. What is model building in the data science process? Explain its purpose.

17. List and explain the three main steps in the model‒building process.

18. What is the purpose of the "Present Findings" stage in the data science process?

19. Why is it important to present your findings clearly after building a model?

20. What is meant by model automation, explain with example. Why is it necessary?

21. Give examples of soft skills that are helpful when presenting data science results.

22. What is data mining? Explain its purpose in simple words.

23. List and explain the main steps in the data mining process.

24. Name and explain any three common techniques used in data mining.

25. Write any four advantages of data mining.

26. What are the main challenges of data mining?

27. Write the main purpose of a data warehouse.

28. List and explain the key characteristics of a data warehouse.

29. What is the ETL process? Describe its three stages briefly.

30. Explain the term metadata in the context of a data warehouse.

31. Explain the difference between data mining and data warehousing.