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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis Fundamentals | 20% | - Data Collection and Preparation
|
| Topic 2: Working with Data Using Python Libraries | 30% | - Data Visualization
|
| Topic 3: Python Programming for Data Analysis | 30% | - File Operations
|
| Topic 4: Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
|
1. When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)
A) on
B) ignore_index
C) dropna
D) suffixes
2. Which of the following is the most suitable data structure in Python for representing a tabular row with column names?
A) dictionary
B) set
C) list
D) tuple
3. Which data collection method is most appropriate when gathering opinions from a target audience at scale?
A) Web scraping
B) Sensor logs
C) Surveys
D) Transactional data
4. Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)
A) Use jargon-rich annotations
B) Add context through narrative or titles
C) Use raw code outputs for clarity
D) Focus on trend and summary metrics
5. Which operations are valid when working with Pandas Series?
(Choose two)
A) Using .columns to rename
B) Applying NumPy universal functions
C) Merging by index using merge()
D) Arithmetic vector operations
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: C,D | Question # 5 Answer: B,D |
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