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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Acquisition and Preprocessing | 30% | - Data collection, integration and storage
|
| Topic 2: Data Exploration and Statistical Analysis | 25% | - Exploratory data analysis
|
| Topic 3: Data Modeling and Machine Learning Basics | 20% | - Model performance and optimization
|
| Topic 4: SQL and Database Integration | 10% | - SQL querying
|
| Topic 5: Data Visualization and Communication | 15% | - Data storytelling and reporting
|
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)
Correct Answer: A,C 🗳️
Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)
Correct Answer: A,D 🗳️
What is the primary purpose of using a requirements.txt file in Python-based data projects?
Correct Answer: A 🗳️
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)
Correct Answer: B,C 🗳️
Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?
Correct Answer: C 🗳️
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