Foundations of Data Science: Two Marks Important Questions and Answers

Python for Data Science

Chapter 3: Foundations of Data Science

 

Two Marks Questions with Answers

 

1. What is data science?

Answer: Data science is the study of data to extract useful insights and knowledge using scientific methods, algorithms and tools especially using programming language like Python.

 

2. Who is data scientist ?

Answer: A data scientist is a professional who analyzes data using tools and techniques to find meaningful patterns and make predictions.

 

3. Name any two disciplines that are used in data science.

Answer: Data science uses several fields such as Statistics, Computer science, Mathematics and domain knowledge.

 

4. State any two applications of data science.

Answer:

1) Healthcare: Data science is used in healthcare to predict diseases early and to provide personalized treatment plans for the patients based on their medical history.

2) Retail: Retail companies use data science to understand customer buying behaviour and to manage inventory efficiently so that products are available when needed.

 

5. How can data science improve government decision‒making?

Answer: By analyzing public data, it helps in policy planning, resource allocation and predicting social trends.

 

6. List any two steps of the data science process.

Answer: Data collection and data preparation.

 

7. What happens in data retrieval step?

Answer: Data is collected from internal and external sources and some extend of quality check on data is performed.

 

8. Explain how what, why and how kewords are helpful in defining research goals?

Answer: Every project begins by answering three important questions :

1. What: What is the goal of the project?

2. Why: Why is the project important for the company?

3. How: How will the project be carried out

For example ‒ i) what is the project? "predict customer churn"(who will stop using the service). ii) Why to predict customer churn ‒ because we want to reduce the customer loss and increase profits. iii) How to carry out this ? ‒ By collecting customer data and building prediction model.

 

9. Why is defining research goals important?

Answer: They help all stakeholders agree on project objectives and success criteria.

 

10. What happens if the research goal is not clearly defined?

Answer: The entire analysis may go off track, leading to irrelevant insights and wasted effort.

 

11. What is project charter ?

Answer: A Project Charter is a short document that summarizes all key information about the project.

It acts as an agreement between the data science team and the client or management.

 

12. Why is data preparation so important in data science process ?

Answer: It cleans and transforms raw data into usable form for analysis.

 

13. Name three stages of data preparation.

Answer: The three stages of data preparation are ‒ Data cleaning, transformation and data combining.

 

14. What is data cleaning?

Answer: It is the process of identifying and correcting errors, missing values and inconsistencies in data.

 

15. What is meant by outlier?

Answer: It is an observation that is very different from other data points.

 

16. What is the difference between noise and anomalies?

Answer: Noise is random error in data; anomalies are unusual patterns that may indicate important

events.

 

17. Should outliers always be removed?

Answer: Not always‒ if they represent real rare events (like fraud), they should be studied, not deleted.

 

18. How does data wrangling differ from data cleaning?

Answer: Data cleaning fixes errors; wrangling also reshapes and transforms data for analysis.

 

19. What is data exploration ?

Answer: It is the process of analyzing data visually or statistically to understand its main characteristics.

 

20. Name one non‒graphical method of data exploration

Answer: Descriptive statistic in which mean, median and mode are calculated.

 

21. What is the purpose of EDA ?

Answer:

The purpose of EDA(Exploratory Data Analysis) is to identify patterns, trends and relationships in data before modeling.

 

22. Why is visualization crucial in EDA ?

Answer: Visualization is crucial because visual patterns often reveal trends, errors, or outliers that statistical summaries might miss.

 

23. What is model building in data science?

Answer: The process of creating mathematical or machine learning models to make predictions or classifications, is called model building.

 

24. List three main steps of model building

Answer:

1) Model selection

2) Model execution

3) Model evaluation.

 

25. What is meant by model diagnostic.

Ans: Checking whether the model's assumptions and performance are correct.

 

26. Why is presenting findings important?

Answer: Presenting findings step is important because it helps non‒technical users understand and use the insights effectively.

 

27. What are some tools used to present data findings ?

Answer: Tools like Power BI, Tableau, and Matplotlib help create clear and interactive visual reports.

 

28. What does automating a model mean?

Answer: Automating means Making the model run automatically to update results without manual work.

 

29. What are soft skills in data science ?

Answer: Communication and presentation skills used to explain results clearly.

 

30. What is data mining?

Answer: It is a process of discovering useful patterns and relationships from large datasets.

 

31. Name any two data mining techniques.

Answer: Classification and Clustering.

 

32. How does data mining differ from data warehousing?

Answer: Data warehousing stores and organizes data; data mining extracts patterns and insights from it.

 

33. What is association rule mining ?

Answer: Finding items that often occur together, like "people who buy bread also buy butter."

 

34. What is the relationship between data mining and data science ?

Answer: Data Mining is a part of Data Science focused on pattern discovery.

 

35. What is a data warehouse ?

Answer: Data warehouse is a large storage system that combines and stores data from many sources for analysis and reporting.

 

36. What is the main purpose of a data warehouse ?

Answer: To provide clean, organized, historical data for decision‒making.

 

37. Differentiate between data science and data mining ?

Answer: 

Data mining

1.It is the process of finding hidden patterns and relationships from a large datasets.

2.Data mining is used to discover usefulfor information from data.

3.The scope of data mining is narrow. It only focuses on extracting knowledge from data.

4.It is part of data science.

5.Techniques used for data mining clustering, classification and association rules.

6. It works mostly with structured data like spreadsheets, databases.

7.The outcome of any data mining process is patterns, trends, or rules found in the huge data.

8. Example ‒ A retail company finds that customers who buy bread often buy butter.

Data science

1. It is broader field that uses data to analyze, build models and solve real‒ world problems.

2. In data science process, the data is used predictions, automation and for decision making.

3. The scope of data science is wider. It includes collection of raw data, cleaning, data mining, modeling, visualization and deployment.

4. It is a broader field that includes data mining.

5. It uses machine learning, data mining, visualization and big data tools.

6.It works with both structured and unstructured data like text, image, audio.

7.The outcome of data science is predictive models, dashboards, reports and real‒time  applications.

8. Example Using this pattern the company recommendation system to suggest butter when the customer buys bread.

 

38. Give one advantage and one limitation of a data warehouse. 

Answer: Advantage: It supports faster and more accurate decision‒making.

Limitation: It requires high setup and maintenance cost.

39. Difference between Data Mining and Data Warehousing. 

Data warehousing

1.It is a process of collecting, transforming and Storing data from different sources to one central place.

2.It is used to organize and store data for easy access and reporting.

3.It is mainly data storage and management.

4.It used ETL process.

5.The outcome of data warehousing is organized, clean and historical data.

6.For example ‒ A company stores 5 years of sales, customer and product data in one system for reporting.

Data mining

1.It is the process of analysing data to find hidden pattern.

2.It is used to discover useful information or knowledge from stored data.

3It is mainly data analysis and pattern discovery.

4.It uses algorithms and statistical methods to analyze data.

5.The outcome of data mining is patterns, trends and predictions based on data.

6.For example The company analyzes this stored data to find that "customers who buy shoes often buy socks too."