✅ 1. Explain the key fundamentals of Exploratory Data Analysis (EDA), comparing it with classical and Bayesian analysis.
This topic has appeared repeatedly and comprehensively covers the core themes of Unit I, making it the most likely 16‑mark question to be asked in the upcoming exam.
Let me know if you want the Top 5 questions or a concise note summary for revision.
✅ 1. Explain the fundamentals of Exploratory Data Analysis (EDA), comparing it with classical and Bayesian analysis.
✅ 2. Explain data transformation techniques in EDA, including merging databases, reshaping and pivoting.
These two questions have appeared consistently in previous examinations and closely map to the core Unit I topics—making them the most likely 16‑mark questions in your upcoming exam.
✅ 1. Explain the fundamentals of Exploratory Data Analysis (EDA), comparing it with classical and Bayesian analysis.
✅ 2. Explain data transformation techniques in EDA, including merging databases, reshaping, and pivoting.
✅ 3. Discuss the significance and role of visual aids (plots, charts) in EDA to interpret and communicate data insights.
These three questions are most likely to appear in the upcoming exam from Unit I, based on their frequency and importance in past assessments.
✅ 1. Explain the fundamentals of Exploratory Data Analysis (EDA), comparing it with classical and Bayesian analysis.
✅ 2. Explain data transformation techniques in EDA, including merging databases, reshaping, and pivoting.
✅ 3. Discuss the significance and role of visual aids in EDA (plots, charts) for interpreting data.
✅ 4. Describe the software tools used in EDA (e.g., Python, R, Excel, Tableau) and their relevance.
✅ 5. What is the significance of EDA in data science and how does it help in making sense of data?
These five questions are highly likely to appear in the upcoming exam, as they consistently align with syllabus highlights and exam-related resources.
✅ 1. Explain the fundamentals of Exploratory Data Analysis (EDA), comparing it with classical and Bayesian analysis.
✅ 2. Explain data transformation techniques in EDA, including merging databases, reshaping, and pivoting.
✅ 3. Discuss the significance and role of visual aids (plots, charts) in EDA for interpreting and communicating insights.
✅ 4. Describe the software tools used in EDA (e.g., Python, R, Excel, Tableau) and their applications.
✅ 5. Explain the significance of EDA in data science and how it helps make sense of data.
✅ 6. What is protocol layering? (Erratum: data preprocessing layer approach in EDA context)
✅ 6. Explain the stages of EDA: data cleaning, exploration, modeling, and communication.
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✅ 7. Describe common data cleaning tasks such as handling missing values, outlier detection, and duplicates.
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✅ 8. Compare exploratory data analysis with confirmatory (classical) analysis and Bayesian analysis.
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✅ 9. Explain visualisation techniques such as histograms, boxplots, scatterplots, heat maps in EDA.
✅ 10. Discuss data transformation techniques like normalization, scaling, encoding categorical data.
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Let me know if you’d like short revision notes, or the Top 3 rephrased for practice.
UNIT I
EXPLORATORY DATA ANALYSIS
EDA fundamentals - Understanding data science - Significance of EDA - Making sense of data - Comparing EDA with classical and Bayesian analysis - Software tools for EDA - Visual aids for EDA - Data transformation techniques - Merging database - Reshaping and pivoting - Transformation techniques refer CCS346 EXPLORATORY DATA ANALYSIS previous year question paper and give me one important 16 question that has 100% possiblikllty on upcming exam no explanation please give top 10 most high priority on unit 1 and list no based on proirity And no of times repeated in previous year question papers