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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: SQL and Database Integration | 10% | - Python-database connectivity
|
| Topic 2: Data Exploration and Statistical Analysis | 25% | - Inferential statistics
|
| Topic 3: Data Acquisition and Preprocessing | 30% | - Data preparation with Pandas and NumPy
|
| Topic 4: Data Visualization and Communication | 15% | - Data storytelling and reporting
|
| Topic 5: Data Modeling and Machine Learning Basics | 20% | - Model performance and optimization
|
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NEW QUESTION # 120
What is the primary role of the pandas library in the Python data science ecosystem?
Answer: D
NEW QUESTION # 121
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?
Answer: A
NEW QUESTION # 122
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?
Answer: C
NEW QUESTION # 123
What is the primary purpose of using annotations in a data visualization?
Answer: A
NEW QUESTION # 124
What is the benefit of using the __name__ == "__main__" construct in Python scripts?
Answer: D
NEW QUESTION # 125
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