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| Section | Weight | Objectives |
|---|---|---|
| SQL and Database Integration | 10% | - SQL querying
|
| Data Acquisition and Preprocessing | 30% | - Data cleaning and validation
|
| Data Exploration and Statistical Analysis | 25% | - Descriptive statistics
|
| Data Visualization and Communication | 15% | - Visualization principles and best practices
|
| Data Modeling and Machine Learning Basics | 20% | - Model performance and optimization
|
1. How would you extract the last three rows of a DataFrame df using position-based indexing?
A) df.tail(3)
B) df.iloc[-3:]
C) df[-3]
D) df.loc[-3:]
2. Which characteristics are typically associated with supervised learning algorithms?
(Choose two)
A) They work only with numerical features
B) They are useful for regression and classification tasks
C) They automatically detect outliers
D) They require labeled training data
3. Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)
A) Slicing
B) Index-based iteration
C) Key-value mapping
D) Append
4. Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?
A) python install pandas
B) install pip pandas
C) python get pandas
D) pip install pandas
5. Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?
A) df.dropna()
B) df.rename()
C) df.sort_values()
D) df.replace()
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: B,D | Question # 3 Answer: A,D | Question # 4 Answer: D | Question # 5 Answer: D |
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