Question Bank: R Programming.

Chapter 1. Introduction to Data Science.

Section 1: Introduction & Fundamentals

1.     Explain the primary differences between Data Science and Big Data.

2.     Differentiate between Data Science and Big Data. Provide at least two practical examples where they overlap.

3.     Define Data Science. Explain why it is considered an interdisciplinary field involving statistics, computer science, and domain expertise.

4.     Explain the "Facets of Data." Describe the differences between Structured, Unstructured, and Semi-structured data with one example for each.

5.     Discuss the "5 Vs" of Big Data. Briefly explain how Volume, Velocity, Variety, Veracity, and Value define the nature of modern data.

6.     Define Data Science and list the fundamental steps involved in a data science project.

7.     Describe the different "facets of data" that are encountered in the data science process.

Section 2: The Data Science Process

5.     Outline the Data Science Process. List the six standard stages of the data science life cycle in their correct order.

6.     Describe the "Discovery" phase. Why is it critical to understand the business requirements before starting any data analysis?

7.     Explain "Data Preparation." Why do data scientists spend nearly 80% of their time on cleaning and transforming raw data?

8.     What is Exploratory Data Analysis? Discuss its role in finding patterns and anomalies in a dataset before building a model.

9.     Describe the "Model Building" phase. Explain the difference between training a model and testing a model.

10.                        Discuss the importance of "Communicating Results." Why is data visualization (like charts and graphs) essential when presenting findings to stakeholders?

Section 3: Skills & Applications

11.                        Identify the "Technical Skills" needed for Data Science. List at least four skills (e.g., Programming in R/Python, Statistics, SQL, Machine Learning).

12.                        Explain the importance of "Soft Skills" in Data Science. How do curiosity, critical thinking, and storytelling help a data scientist succeed?

13.                        Discuss the use of Data Science in the Healthcare industry. Give examples of how it helps in disease prediction or drug discovery.

14.                        How do E-commerce companies use Big Data? Explain how recommendation engines (like "Customers who bought this also bought…") work to increase sales.

15.                        Explain the role of a "Data Engineer" vs. a "Data Scientist." How do their responsibilities differ within the data science process?

Chapter 2 – Getting Started with R

Section 1: Introduction, Features, and History

1.     Explain basic data types in R with examples.

2.     What is R and why is it popular in Data Science? Explain its primary purpose in statistical computing and data analysis.

3.     List and explain five key features of R. Discuss how it handles data and its graphical capabilities.

4.     Briefly describe the history and origin of R. Mention the creators and its relationship to the S language.

5.     What is the Comprehensive R Archive Network? Explain its role in the R ecosystem and how it supports users worldwide.

6.     Explain the difference between a Package and a Library in R. How do these two terms relate to one another?

7.     Demonstrate the use of a "for loop" in R with a simple programming example.

8.     Outline the history of R and list four key features that make it popular for data analysis.

 

Section 2: Data Types, Variables, and Operators

6.     List and briefly explain the five basic data types in R. Provide a simple example for each (e.g., Numeric, Logical, Character).

7.     What is the difference between the ‘Numeric’ and ‘Integer’ data types in R? Show how to explicitly define an integer using the L suffix.

8.     Discuss the rules for naming variables in R. Mention at least three valid and invalid naming conventions (e.g., use of dots, underscores, and numbers).

9.     Explain the different Assignment Operators in R. Compare the use of the leftward assignment (<-), equal sign (=), and rightward assignment (->).

10.                        Describe the Arithmetic Operators available in R. Provide examples of addition, subtraction, multiplication, division, and the modulus operator (%%).

11.                        Explain Relational Operators in R. Show how operators like ==, !=, <, and > are used to compare values.

12.                        Explain Arithmetic and Logical operators in R with a table of symbols and their functions.

13.                        Discuss Logical Operators in R. Explain the use of "AND" (&), "OR" (|), and "NOT" (!) with a simple truth-table example.

Section 3: Decision Making and Loops

13.                        Demonstrate the use of the if…else statement for decision-making in R.

14.                        Explain the if-else statement in R. Provide a simple code snippet that checks if a number is positive or negative.

15.                        What is the switch function in R? Describe how it works as an alternative to multiple if-else statements with an example.

16.                        Compare the for loop and the while loop in R. Explain when a programmer would choose one over the other for a repetitive task.

  Chapter 3 – Functions and R Objects

 Section 1: Functions and Strings

 

1.     Explain how to create a Vector and demonstrate how to access its specific elements.

2.     How are Strings handled in R? Explain the difference between the paste and paste0 functions with an example for each.

3.     Discuss the use of the nchar and toupper/tolower functions. Explain why these functions are important when working with character data.

4.     Distinguish between a Matrix and a Dataframe in terms of their structure and use.

5.     Explain the use of Arguments in a function. Discuss how to provide a default value to an argument with a simple example.

6.     Define a function in R and show the syntax for creating a user-defined function.

7.     What is a Function in R? Explain the basic syntax for creating a user-defined function and show how to call it.

Section 2: Basic R Objects

5.     Define a Vector in R. Explain how to create a vector using the c function and how to access its elements.

6.     Discuss Vector Arithmetic. Explain with an example how R performs operations (like addition) on two vectors of the same length.

7.     What is a List in R? Explain how a List differs from a Vector and show how to create a list that contains different data types.

8.     Explain how to create a List in R that contains different types of data elements.

9.     Describe how to access elements in a List. Show the difference between using single brackets “ and double brackets [].

Section 3: Matrices and Arrays

9.     Demonstrate how to create a Matrix and perform a simple addition operation on two matrices.

10.                        What is a Matrix in R? Explain the parameters data, nrow, and ncol inside the matrix function.

11.                        Explain Indexing in Matrices. Show how to access a specific row, a specific column, and a single element from a 3×3 matrix.

12.                        Define an Array in R. How does an Array differ from a Matrix? Explain the dim parameter used when creating an array.

Section 4: Factors and Data Frames

12.                        What are Factors in R? Explain why factors are useful for storing categorical data and how the levels function is used.

13.                        Define a Data Frame. Why is it the most commonly used object for data analysis? Explain how it differs from a Matrix.

14.                        How do you manipulate a Data Frame? Show how to add a new column to an existing data frame and how to filter rows based on a condition.

15.                        Discuss Object Inspection. Explain how the functions str, class, and summary help a programmer understand the structure of an R object.

  Chapter 4 – Data Interface & Visualization

 Section 1: Data Import and Export

1.     Demonstrate the command used to import a CSV file into the R environment.

2.     Explain the process of exporting a Data Frame to a CSV file. Describe the write.csv function and how to prevent R from saving row names.

3.     How can you read Excel files in R? Name the common library used (like readxl) and explain the read_excel function.

4.     Describe the method to read a plain Text (.txt) file. Explain the read.table function and the importance of the sep (separator) argument.

5.     What is the difference between read.csv and read.csv2? Explain when a developer would use one over the other based on the decimal separator used in different regions.

6.     Illustrate how to create a Pie Chart using the pie function with a basic dataset.

7.     Explain the purpose of a Scatter Plot and provide the syntax to generate one in R.

8.     How do you import a CSV file into R? Explain the read.csv function and mention the purpose of the header argument.

Section 2: Data Processing Techniques

6.     Explain how to subset a dataset in R. Provide a simple example of how to extract specific rows and columns from a large data frame.

7.     How do you handle missing values in a dataset? Explain the use of functions like is.na or na.omit to clean data before analysis.

8.     Discuss the process of sorting data in R. Explain how the order function is used to arrange a data frame based on a specific column.

Section 3: Charts and Graphs

9.     What is a Pie Chart? Explain the pie function and how to add custom labels and colors to represent different categories.

10.                        Describe the Bar Chart in R. Explain the barplot function and show how to create a horizontal bar chart instead of a vertical one.

11.                        Explain the purpose of a Boxplot. What are the five key statistical values it visualizes? Show the basic syntax of the boxplot function.

12.                        Define a Histogram. How is it different from a Bar Chart? Explain the hist function and the use of the breaks argument.

13.                        How do you create a Line Graph in R? Explain the plot function and how the type argument (like "l" or "o") changes the visualization.

14.                        What is a Scatter Plot? Explain how it helps in visualizing the relationship between two variables using the plot function.

15.                        How do you add titles and labels to a chart? Explain the use of parameters like main, xlab, ylab, and col within any R plotting function.