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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: SQL and Database Integration | 10% | - Relational database concepts
|
| Topic 2: Data Modeling and Machine Learning Basics | 20% | - Model performance and optimization
|
| Topic 3: Data Visualization and Communication | 15% | - Data storytelling and reporting
|
| Topic 4: Data Acquisition and Preprocessing | 30% | - Data collection, integration and storage
|
| Topic 5: Data Exploration and Statistical Analysis | 25% | - Descriptive statistics
|
1. What is the function of the melt() operation in Pandas when reshaping wide-format data?
A) Converts column values into rows
B) Transposes the DataFrame
C) Combines multiple DataFrames
D) Sorts the DataFrame based on a column
2. What is the primary reason to use .head() and .tail() methods in pandas when exploring datasets?
A) To preview the first and last few rows of data
B) To delete missing values
C) To rename columns
D) To create visualizations
3. Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)
A) df.loc[:, 'col1']
B) df.loc[2:7, ['col1', 'col2']]
C) df.iloc[0:5, 'col2']
D) df['col1', 'col2']
4. What is one potential risk of dynamically constructing SQL queries using string concatenation in Python scripts?
A) Increased memory usage
B) Slower database performance
C) Exposure to SQL injection attacks
D) Missing foreign key constraints
5. What is the main purpose of validating a dataset before applying statistical analysis?
A) To visualize patterns more clearly
B) To ensure the dataset meets expected quality and structure
C) To generate more columns from the dataset
D) To sort the data alphabetically
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A,B | Question # 4 Answer: C | Question # 5 Answer: B |
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