UNIT-I:Comprehensive Data Science Questions: Overview, Process, and Ethics

Sample question from UNIT I, Unit II and Unit III

UNIT I

Data Science Overview

1. What is Data Science, and how does it differ from traditional data analysis?
2. Explain the evolution of Data Science. How has the role of data scientists changed over the years with the rise of big data and advanced machine learning techniques?
3. What are the different roles in Data Science? Describe the responsibilities of a Data Scientist, Data Analyst, and Data Engineer.
4. What are the key tools used in Data Science? Name some popular programming languages, libraries, and platforms used by Data Scientists.
5. What are some real-world applications of Data Science? Provide examples of how data science is applied in fields like healthcare, finance, and e-commerce.

Data Science Process Overview

6. Describe the Data Science process. What are the key stages involved in a typical data science project?
7. What does it mean to define goals in the Data Science process? How can clear goal definition impact the overall success of a data science project?
8. Explain the steps involved in retrieving and preparing data for analysis. Why is data preparation considered one of the most crucial stages of the Data Science process?
9. How does data exploration help in understanding a dataset? Describe common techniques used during the data exploration phase.
10. What is data modeling, and how does it fit into the Data Science process? Provide examples of different types of data models used in predictive analytics and machine learning.

Data Science Ethics

11. What ethical considerations should Data Scientists keep in mind when performing data analysis? How can a Data Scientist ensure they are doing good data science?
12. Who owns the data in a data science project, and what are the implications of data ownership on access, usage, and sharing?
13. What are the different aspects of privacy that a Data Scientist must value when working with sensitive data? How can privacy be protected in data science projects?
14. Explain the importance of informed consent in Data Science. How should Data Scientists ensure that they have obtained proper consent before using personal data in their analysis?
15. What are the Five Cs of Data Science, and how do they help ensure ethical data practices in a data science project?
16. Why is diversity and inclusion important in Data Science? How can biases in data and algorithms be mitigated to ensure fair outcomes for all groups?
17. What are some of the key future trends in Data Science? How might advancements in AI, automation, and cloud computing impact the role of Data Scientists in the coming years?