CS3352 Foundations of Data Science Previous Year Question Papers - Anna University

UNIT I: INTRODUCTION TO DATA SCIENCE

Part A (2 Marks)

• Define Data Science and list its primary benefits.
• What is a Project Charter and why is it important?
• List the different facets of data (structured, unstructured).
• What are the steps involved in data cleansing?

Part B (13/15 Marks)

• Explain the Data Science Process with a neat diagram.
• Describe Data Preparation and Data Exploration stages.
• Discuss the role of Data Mining and Data Warehousing.
• Explain the importance of defining research goals in a data science project.

UNIT II: DESCRIBING DATA

Part A (2 Marks)

• Differentiate Discrete and Continuous variables.
• Define Mean, Median, and Mode.
• What is a z-score and how is it calculated?
• Differentiate Histogram and Bar Graph.

Part B (13/15 Marks)

• Explain Frequency Distributions with examples.
• Describe properties of Normal Distribution.
• Explain measures of variability (Range, Variance, Standard Deviation).
• Convert z-scores to original scores and vice versa.

UNIT III: DESCRIBING RELATIONSHIPS

Part A (2 Marks)

• Define Correlation and its types.
• What is Correlation Coefficient (r)?
• Define Least Squares Regression Line.
• Differentiate Correlation and Causation.

Part B (13/15 Marks)

• Explain calculation of Correlation Coefficient.
• Discuss Linear Regression and interpretation.
• Explain Standard Error of Estimate.
• Describe Multiple Regression and regression towards mean.

UNIT IV: PYTHON LIBRARIES FOR DATA WRANGLING

Part A (2 Marks)

• Compare Python List and NumPy Array.
• What is Fancy Indexing?
• Define Pandas DataFrame and Series.
• How to handle missing data in Pandas?

Part B (13/15 Marks)

• Explain NumPy Aggregations and ufuncs with examples.
• Discuss Pandas data manipulation (indexing, selection, grouping).
• Explain Hierarchical Indexing.
• Describe creation and use of Pivot Tables in Pandas.

UNIT V: DATA VISUALIZATION

Part A (2 Marks)

• What is the purpose of Matplotlib?
• Differentiate plt.plot() and plt.scatter().
• What are Subplots?
• Define 3D data visualization.

Part B (13/15 Marks)

• Explain Line, Scatter, and Histogram plots using Matplotlib.
• Compare Seaborn with Matplotlib.
• Explain error bars and density/contour plots.
• Describe 3D plotting and its applications.

Most Repeated / High-Weight Questions

Data Science process and project charter, z-scores and normal distribution, correlation coefficient calculation, linear regression, NumPy and Pandas data manipulation, Matplotlib visualization.