CS3352 Foundations of Data Science Previous Year Question Papers - Anna University
Access Anna University Foundations of Data Science (CS3352) previous year question papers on LearnSkart for smarter semester exam preparation. This Anna University PYQ page offers year-wise Anna University exam papers aligned with Regulation 2021, so students can understand recurring questions, important units, and expected marking schemes. You can view every CS3352 Foundations of Data Science question paper online and use free PDF download options for focused revision before internal and semester exams.
2024
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2024 - CSE-AM-2024-CS 3352-Foundation of data science-984182742-50898.pdf
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2024 - CSE-ND-2024-CS 3352-Foundation of data science -259993648-20250604161745 (7).pdf
2023
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2023 - CSE-ND-2023-CS 3352-Foundations of data science-54275522-20865.pdf
2022
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2022 - CSE-ND-2022-CS 3352-Foundations of data science-202681014-ND22CS (9).pdf
Important Questions - CS3352 Foundations of Data Science
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.
Additional Resources
How to Use These Question Papers
- Unit-Wise Preparation: Complete Unit I for fundamentals, dedicate 50% of time to Unit II-III (statistics and regression are high-weight). Unit IV-V focus on practical Python implementation.
- Statistical Calculations: Master z-score conversions, correlation coefficient calculation, and regression line derivation. Practice these calculations manually before coding solutions.
- Python Practice: Implement NumPy and Pandas operations practically. Work with real datasets from Unit IV-V for hands-on experience with data wrangling and visualization.
- Visualization Skills: Practice creating different plot types (line, scatter, histogram) using Matplotlib and Seaborn. Understand when to use each visualization type for different data patterns.
- Time Management: Allocate 60-90 minutes per statistical problem; practice Part B solutions under timed conditions with Python implementation.
Frequently Asked Questions about CS3352 Foundations of Data Science
Which topics in CS3352 have the highest weightage in exams?
Data description and statistics (Unit II-III), Python libraries for data wrangling (Unit IV), and data visualization (Unit V) together account for 60% of exam marks. Unit I provides foundational concepts. Questions combine theoretical understanding with practical Python implementation.
How should I approach z-score and normal distribution questions in CS3352?
Understand z-score formula: (x - mean) / standard deviation. Practice converting between original scores and z-scores. Master standard normal distribution table usage. Apply normal distribution properties to real-world datasets. These statistical concepts appear with 13-15 marks in Unit II.
What is the best strategy for correlation and regression questions in CS3352?
Calculate correlation coefficient manually using formula. Draw scatter plots to visualize relationships. Derive least squares regression line: y = a + bx. Understand regression towards mean concept. Distinguish between correlation (relationship strength) and causation (cause-effect). Practice with real datasets.
How can I master NumPy and Pandas operations in CS3352?
Practice NumPy aggregations (sum, mean, std), fancy indexing for selective element access, and universal functions. For Pandas: understand DataFrame structure, indexing techniques, groupby operations, and hierarchical indexing. Implement these operations with real data. Unit IV emphasizes hands-on Python practice.
What visualization techniques are most important in CS3352?
Master Matplotlib line plots, scatter plots, histograms. Understand when to use each: line for trends over time, scatter for relationships, histogram for distributions. Learn Seaborn for advanced visualizations. Practice creating subplots and multi-panel figures. Error bars and density plots appear regularly in Unit V.
How should I handle the data science project charter question in CS3352?
Understand project charter components: objectives, scope, stakeholders, success criteria. Explain why defining clear research goals is crucial. Discuss data preparation and exploration importance. These foundational concepts in Unit I link theory to practical project execution in CS3352 exams.