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| Section | Weight | Objectives |
|---|---|---|
| Data Analysis Fundamentals | 20% | - Introduction to Data Analysis
|
| Working with Data Using Python Libraries | 30% | - NumPy Fundamentals
|
| Python Programming for Data Analysis | 30% | - Control Flow and Functions
|
| Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
|
1. 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()
2. What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?
A) Connect → Execute → Commit → Close
B) Commit → Connect → Execute → Close
C) Execute → Connect → Commit → Close
D) Connect → Commit → Execute → Close
3. Which term best describes the process of combining customer data from multiple systems into a single unified dataset?
A) Data warehousing
B) Data integration
C) Data binning
D) Data normalization
4. Why might using a pie chart to represent category proportions be less effective than a bar chart?
A) Pie charts cannot be created using Seaborn
B) Bar charts are limited to one variable only
C) Pie charts display individual data points more clearly
D) Human perception is less accurate at judging angles than lengths
5. Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)
A) Resetting the index after filtering
B) Converting the DataFrame to a Series
C) Ensuring consistent data types
D) Dropping column headers
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: A,C |
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