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| Section | Weight | Objectives |
|---|
| Introduction to Data and Data Analysis Concepts | 22.5% | - Data Ethics and Privacy
- 1. Recognize ethical considerations in data handling
- 2. Understand basic data privacy concepts
- Define and Classify Data
- 1. Explain how data becomes meaningful
- 2. Differentiate structured, semi-structured, and unstructured data
- 3. Classify data as quantitative or qualitative
- Data Analysis Process and Workflow
- 1. Describe the steps of the data analysis process
- 2. Explain the role of data cleaning and preparation
- 3. Identify common data sources and collection methods
|
| Working with Data and Performing Simple Analyses | 32.5% | - Data Cleaning and Transformation
- 1. Handle missing and inconsistent data
- 2. Perform basic data transformation (filtering, sorting, grouping)
- Data Analysis with Python Libraries
- 1. Utilize the collections module for specialized containers
- 2. Perform basic operations with NumPy arrays
- 3. Work with the datetime module for date/time data
- 4. Use the math and statistics modules for basic calculations
- Simple Analytical Techniques
- 1. Calculate descriptive statistics (mean, median, mode)
- 2. Identify basic patterns and trends in data
|
| Python Basics for Data Analysis | 32.5% | - Python Fundamentals
- 1. Implement control flow structures (loops, conditionals)
- 2. Use variables, data types, and basic operators
- 3. Define and use functions
- File Handling
- 1. Use the csv module for basic CSV operations
- 2. Read from and write to files (text, CSV)
- Data Structures
- 1. Work with lists, tuples, dictionaries, and sets
- 2. Apply common operations and methods to data structures
|
| Communicating Insights and Reporting | 12.5% | - Data Storytelling and Reporting
- 1. Structure insights as a narrative
- 2. Create clear and concise analytical reports
- 3. Present insights with visual and verbal techniques
- Data Visualization
- 1. Select appropriate visuals for different data types
- 2. Recognize common visualization types (bar, line, pie charts)
- 3. Interpret simple data visualizations
|
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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q22-Q27):
NEW QUESTION # 22
You are given the following list of daily step counts:
steps = [8230, 9020, 7640, 8760, 10020, 2546, 9817]
Your task is to calculate:
- the standard deviation of the step counts,
- the average rounded up to the nearest whole number, and
- the median of the step counts.
Which code snippet correctly performs all three tasks? Select the best answer.
import statistics
- A. import math
print(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics - B. import math
print(statistics.stdev(steps))
print(math.ceil(statistics.mean(steps)))
print(statistics.median(steps)) - C. import math
print(statistics.variance(steps))
print(math.ceil(sum(steps) / len(steps)))
print(math.floor(statistics.median(steps)))
import statistics - D. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics
Answer: B
Explanation:
It uses statistics.stdev() to compute the standard deviation, statistics.mean() to compute the average and math.ceil() to round it up to the nearest whole number, and statistics.median() to compute the median.
NEW QUESTION # 23
A Python script evaluates the expression len(" "). The programmer assumes it might be zero because it looks empty. What will the actual result be?
Answer: C
Explanation:
A space is still a valid character in a string. Therefore, the string contains one character, and len("
") returns 1 instead of 0.
NEW QUESTION # 24
You are given two lists representing daily page views and sign-ups on a website:
views = [120, 130, 128, 700, 115, 123, 119, 680, 122]
signups = [12, 15, 13, 50, 11, 14, 10, 55, 13]
You want to:
- remove outliers (views > 600), and
- calculate correlation between the cleaned lists.
Which code accomplishes this correctly? Select the best answer.
Answer: D
Explanation:
It converts the lists to NumPy arrays, builds a boolean mask based on the views threshold to remove outliers, applies the same mask to both arrays so they stay aligned, and then computes the correlation on the cleaned pairs using np.corrcoef.
NEW QUESTION # 25
A Python script includes the expression not False and True. The developer is unsure how logical operators are evaluated in terms of precedence. What will be the final Boolean result of this expression?
- A. Error
- B. False
- C. True
- D. None
Answer: C
Explanation:
The not operator has higher precedence than and, so not False becomes True first. Then True and True is evaluated, resulting in True as the final Boolean value.
NEW QUESTION # 26
Consider the following Python code:

What will be printed when the code above is executed?
40
- A. [20, 30, 40]
1
[1, 2, 4, 5, 6] - B. [20, 30, 40]
1
[1, 2, 4, 5, 6]
50 - C. [10, 20, 30, 40]
1
[1, 2, 4, 5, 6]
50 - D. [10, 20, 30, 40]
0
[1, 2, 3, 4, 5, 6]
50
Answer: A
Explanation:
The last element of the original list is 50. The slice from index 1 up to (but not including) index 4 is
[20, 30, 40]. After removing 30 and appending 60, the list contains one occurrence of 10, and integer division by 10 produces [1, 2, 4, 5, 6].
NEW QUESTION # 27
......
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