ANU UG/Degree 4th Sem(Y23) Data Visualization Using Python Unit Wise Important Questions

  ANU UG/Degree 4th Sem(Y23) Data Visualization using Python Unit Wise Important Questions are now available, these questions are very important for your semester exams. These questions are prepared by top qualified faculty. Read these questions for good marks.

 Unit 1
Introduction: Introduction to Data Science, Exploratory Data Analysis and Data Science Process. Motivation for using Python for Data Analysis, Introduction of Python Jupyter Notebook. Essential Python Libraries: NumPy, pandas, matplotlib, SciPy, scikit-learn, statsmodels, seaborn.

Short Answer Questions

1. What is Data Science and what are its key components?
2. Define Exploratory Data Analysis (EDA) and explain its role in the Data Science process.
3. What motivates the use of Python for Data Analysis, and what advantages does it offer over other programming languages?
4. Describe the purpose and key features of Jupyter Notebook in Python-based Data Science workflows.
5. List at least three essential Python libraries used for data analysis and briefly state the primary function of each.
6. How do libraries such as NumPy, pandas, matplotlib, SciPy, scikit-learn, statsmodels, and seaborn contribute to the overall Data Science process?

Long Answer Questions

1. Explain the Data Science process in detail, emphasizing the role of Exploratory Data Analysis (EDA) in transforming raw data into actionable insights.
2. Discuss the motivation behind using Python for Data Analysis, highlighting specific features and advantages that make it a preferred choice in the Data Science community.
3. Describe how Jupyter Notebook enhances the workflow of a data scientist. Include examples of its practical applications and benefits in managing and presenting data analysis projects.
4. Compare and contrast the functionalities of essential Python libraries such as NumPy and pandas. How do these libraries interact in a typical data analysis project to manage and manipulate data?
5. Analyze the importance of visualization libraries like matplotlib and seaborn in Exploratory Data Analysis. Provide examples of how these tools help in uncovering patterns and trends in data.
6. Evaluate the roles of machine learning libraries such as scikit-learn and statsmodels within the Data Science process. Discuss how they are used to build predictive models and contribute to data-driven decision making.

 Unit 2
Getting Started with Pandas: Arrays and vectorized conputation, Introduction to pandas Data Structures, Essential Functionality, Summarizing and Computing Descriptive Statistics. Data Loading, Storage and File Formats. Reading and Writing Data in Text Format, Web Scraping, Binary Data Formats, Interacting with Web APIs, Interacting with Databases Data Cleaning and Preparation. Handling Missing Data, Data Transformation, String Manipulation

Short Answer Questions

1. What is vectorized computation in Pandas, and why is it advantageous for data processing?
2. What are the primary data structures provided by Pandas, and how do they differ from traditional arrays?
3. How does Pandas support summarizing and computing descriptive statistics on data?
4. What file formats can Pandas read and write, and why is this versatility important for data loading and storage?
5. What are some common methods used in Pandas for handling missing data?
6. Which Pandas functions are commonly used for string manipulation during data cleaning?

Long Answer Questions

1. Explain the concept of vectorized computation in Pandas and discuss its benefits over iterative approaches when processing large datasets.
2. Describe the main data structures in Pandas (such as Series and DataFrame), highlighting their features and use cases in data analysis.
3. Discuss the essential functionalities of Pandas for summarizing data, including the use of functions like describe(), mean(), and other statistical methods.
4. Evaluate the methods available in Pandas for reading and writing data in various file formats (e.g., text files, binary formats, web APIs, databases) and explain their importance in data analysis workflows.
5. Analyze how web scraping and interaction with web APIs can be integrated into a Pandas-based data analysis pipeline, including the challenges and benefits.
6. Discuss the approaches and techniques in Pandas for data cleaning and preparation, focusing on handling missing data, performing data transformation, and executing string manipulations.

Unit 3
Data Wrangling: Hierarchical Indexing, Combining and Merging Data Sets Reshaping and Pivoting. Data Visualization matplotlib: Basics of matplotlib, plotting with pandas and seaborn, other python visualization tools. Advanced categorical and numeric plots.

Short Answer Questions

1. What is hierarchical indexing in Pandas, and how does it simplify complex data structures?
2. How do combining and merging datasets differ in Pandas, and when should each method be used?
3. What is the purpose of reshaping and pivoting data, and how do these operations differ?
4. What are the basic functionalities of matplotlib for data visualization in Python?
5. How can Pandas be used for plotting data, and what benefits does it offer when integrated with matplotlib or seaborn?
6. Name one advanced categorical plot and one advanced numeric plot available through Python visualization tools like seaborn.

Long Answer Questions

1. Explain the concept of hierarchical indexing in Pandas, including its creation, benefits, and practical applications in data wrangling.
2. Discuss the various methods for combining and merging datasets in Pandas. Compare these techniques and provide examples of scenarios where each method is most effective.
3. Describe the processes of reshaping and pivoting data in Pandas. Include a discussion on when and why these operations are necessary for data analysis.
4. Provide a comprehensive overview of matplotlib, outlining its basic features, common use cases, and how it integrates with Pandas for effective data visualization.
5. Analyze how visualization libraries such as Pandas plotting, matplotlib, and seaborn complement each other. Include examples of how each tool can be used to generate insightful visualizations.
6. Evaluate the techniques for creating advanced categorical and numeric plots using Python visualization tools. Discuss best practices, challenges, and how these advanced plots enhance data interpretation.

Unit 4
Data Aggregation and Group operations: Group by Mechanics, Data aggregation, General split-apply-combine, Pivot tables and cross tabulation
Time Series Data Analysis: Date and Time Data Types and Tools, Time series Basics, date Ranges, Frequencies and Shifting, Time Zone Handling, Periods and Periods Arithmetic, Resampling and Frequency conversion, Moving Window Functions.

Short Answer Questions

1. What is the 'split-apply-combine' strategy in data aggregation, and how does it work?
2. How does the "group by" mechanism function in Pandas for data aggregation?
3. Define pivot tables and cross tabulation, and explain their roles in summarizing data.
4. What are the main date and time data types used in Python for time series analysis?
5. How do resampling and frequency conversion techniques help in analyzing time series data?
6. What is the purpose of moving window functions in time series analysis?

Long Answer Questions

1. Explain in detail the process of data aggregation using group operations in Pandas, including the 'split-apply-combine' strategy, and discuss its significance in data analysis.
2. Discuss the use of pivot tables and cross tabulation for data summarization. Compare their functionalities, and provide examples of practical applications.
3. Describe the various date and time data types and tools available in Python for time series analysis, and explain how they contribute to handling time-based data.
4. Analyze the concepts of date ranges, frequencies, and shifting in time series data analysis. How do these techniques facilitate the exploration of temporal patterns?
5. Explain the methods for handling time zones, working with periods, and performing period arithmetic in Python. Why are these aspects critical in time series analysis?
6. Evaluate the techniques of resampling, frequency conversion, and moving window functions in time series data analysis. How do these methods enhance the understanding of trends and fluctuations in data?

 Unit 5 Advanced Pandas:
Categorical Data: cleaning data and visualization techniques, Advanced GroupBy methods ,Use Techniques for Method Chaining

Short Answer Questions

1. What is categorical data in Pandas, and why is it important in data analysis?
2. What are common techniques for cleaning categorical data using Pandas?
3. How do visualization techniques assist in interpreting categorical data?
4. What are some advanced GroupBy methods in Pandas, and how do they enhance data aggregation?
5. Define method chaining in Pandas and explain its advantages.
6. How can advanced GroupBy methods be integrated with method chaining to streamline data analysis?

Long Answer Questions

1. Discuss the significance of categorical data in Pandas and detail the techniques used for cleaning and preparing this type of data.
2. Explain various visualization methods for categorical data in Pandas, providing examples of how these visualizations can uncover meaningful insights.
3. Describe the advanced GroupBy methods available in Pandas, including multi-level grouping and custom aggregation functions, with illustrative examples.
4. Analyze the concept of method chaining in Pandas, discussing its syntax, benefits, and how it contributes to writing more readable and efficient code.
5. Compare basic and advanced GroupBy operations in Pandas, and evaluate how integrating these with method chaining can optimize data aggregation tasks.
6. Critically examine a case study or scenario where advanced GroupBy methods combined with method chaining improved the overall data analysis process.