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| Section | Weight | Objectives |
|---|---|---|
| SQL and Database Integration | 10% | - Relational database concepts
|
| Data Acquisition and Preprocessing | 30% | - Data cleaning and validation
|
| Data Exploration and Statistical Analysis | 25% | - Exploratory data analysis
|
| Data Modeling and Machine Learning Basics | 20% | - Supervised learning fundamentals
|
| Data Visualization and Communication | 15% | - Visualization with Matplotlib and Seaborn
|
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NEW QUESTION # 46
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)
Answer: A,B
NEW QUESTION # 47
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)
Answer: B,C
NEW QUESTION # 48
Which element is essential to justify a conclusion drawn from a dataset?
Answer: A
NEW QUESTION # 49
What is the main reason to remove duplicate records before performing analysis?
Answer: A
NEW QUESTION # 50
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)
Answer: B,D
NEW QUESTION # 51
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