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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Python Basics for Data Analysis	32.5%	- Data Structures 1. Apply common operations and methods to data structures 2. Work with lists, tuples, dictionaries, and sets - File Handling 1. Use the csv module for basic CSV operations 2. Read from and write to files (text, CSV) - Python Fundamentals 1. Use variables, data types, and basic operators 2. Implement control flow structures (loops, conditionals) 3. Define and use functions
Topic 2: Communicating Insights and Reporting	12.5%	- Data Visualization 1. Interpret simple data visualizations 2. Select appropriate visuals for different data types 3. Recognize common visualization types (bar, line, pie charts) - Data Storytelling and Reporting 1. Create clear and concise analytical reports 2. Structure insights as a narrative 3. Present insights with visual and verbal techniques
Topic 3: 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) - Simple Analytical Techniques 1. Calculate descriptive statistics (mean, median, mode) 2. Identify basic patterns and trends in data - Data Analysis with Python Libraries 1. Perform basic operations with NumPy arrays 2. Work with the datetime module for date/time data 3. Utilize the collections module for specialized containers 4. Use the math and statistics modules for basic calculations
Topic 4: Introduction to Data and Data Analysis Concepts	22.5%	- Define and Classify Data 1. Differentiate structured, semi-structured, and unstructured data 2. Classify data as quantitative or qualitative 3. Explain how data becomes meaningful - Data Ethics and Privacy 1. Understand basic data privacy concepts 2. Recognize ethical considerations in data handling - Data Analysis Process and Workflow 1. Describe the steps of the data analysis process 2. Identify common data sources and collection methods 3. Explain the role of data cleaning and preparation

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are given a short Python script that uses both arithmetic and assignment operators. The variable x is initialized as 4, then updated using x *= 2 + 3. What will be printed when the final value of x is displayed?

A. 0
B. 1
C. 2
D. 3

Answer: B

Explanation:
Python evaluates the addition first because of operator precedence. The expression 2 + 3 equals
5, then x *= 5 multiplies the current value of x (4) by 5, resulting in a final value of 20.

NEW QUESTION # 17
A program uses while False: followed by print statements inside the loop. The developer expects output. What will actually happen during execution?

A. Prints once
B. No execution
C. Infinite loop
D. Error

Answer: B

Explanation:
The condition False is evaluated before entering the loop. Since it is always false, the loop body is never executed, and no output is produced.

NEW QUESTION # 18
A loop includes a break statement when a condition is met. The programmer wants to understand how break affects loop execution. What happens when break is executed inside the loop?

A. Skips one iteration
B. Loop ends immediately
C. Error
D. Loop restarts

Answer: B

Explanation:
The break statement immediately terminates the loop, regardless of remaining iterations. Control is transferred to the first statement following the loop block.

NEW QUESTION # 19
You are analyzing survey results from students about their favorite colors. The list colorsstores individual responses:

You want to:
- find the number of unique colors mentioned using NumPy, and
- determine how often each color was chosen using Counter.
Which code snippet correctly performs both tasks? Select the best answer.
from numpy import unique

A. from collections import Counter
unique_colors = np.unique(colors)
color_counts = Counter(set(colors))
import numpy as np
B. from collections import Counter
unique_colors = len(set(colors))
color_counts = np.unique(colors)
C. from collections import Counter
unique_colors = len(unique(colors))
color_counts = Counter(colors)
import numpy as np
D. from collections import Counter
unique_colors = Counter(colors)
color_counts = sum(np.unique(colors))
import numpy as np

Answer: C

Explanation:
numpy.unique returns the distinct values in the list, and taking its length gives the number of unique colors. Counter(colors) correctly counts how many times each color appears in the responses.

NEW QUESTION # 20
You are developing a temperature control module for a laboratory incubator. Your objectives are to:
- generate timestamps every 10 minutes over a 3-hour span (i.e., 0 to 180 minutes), and
- simulate five evenly spaced target temperatures between 35.0°C and 37.0°C for system calibration.
Which code snippet correctly produces both sequences using NumPy? Select the best answer.
import numpy as np

A. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
B. timestamps = np.linspace(0, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np
C. timestamps = np.arange(0, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
D. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np

Answer: A

Explanation:
np.arange(0, 181, 10) generates timestamps from 0 through 180 in 10-minute steps (end is excluded, so using 181 includes 180). np.linspace(35.0, 37.0, 5) generates five evenly spaced temperatures including both 35.0°C and 37.0°C.

NEW QUESTION # 21
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