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Python Institute PCED-30-02 Exam Syllabus Topics:

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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q23-Q28):

NEW QUESTION # 23
Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

A. text[-6].isdigit() returns True, text.startswith("r") returns True
B. text[0:6].isalpha() returns True, and text.find("2024") returns 6
C. text.isalpha()returns True, and text.find ("2024")returns True
D. text.capitalize()changes Report2024Finalto REPORT2024FINAL, and text.find("2024")returns 6
E. text.capitalize()changes Finalto lowercase, and text.find ("L")returns True
F. text[6:8].isdigit()returns True, and text.endswith("Final")returns True

Answer: B,E,F

Explanation:
The slice text[6:8] is "20", which contains only digits, and the string ends with "Final". The first six characters are "Report", which are all alphabetic, and "2024" begins at index 6.

NEW QUESTION # 24
You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A. fields = line.replace(', ', '|').split('|')
B. fields = line.strip().split(', ')
C. fields = line.split(', ').trim()
D. fields = line.split().strip(', ')

Answer: B

NEW QUESTION # 25
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.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np
B. timestamps = np.arange(0, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
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, 181, 10)
target_temps = np.arange(35.0, 37.0, 0.5)
import numpy a3 np

Answer: B

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 # 26
You are reading a data.csvfile line by line. To prepare each line for formatting with f-strings, you need to remove extra whitespace and split the values by commas.
Which line should you insert to correctly clean and parse the input?

A. fields = line.replace(', ', '|').split('|')
B. fields = line.strip().split(', ')
C. fields = line.split(', ').trim()
D. fields = line.split().strip(', ')

Answer: B

Explanation:
Stripping removes leading and trailing whitespace from the line, and then splitting on the comma- space delimiter correctly separates the values into a list for further formatting.

NEW QUESTION # 27
You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

A. The graph doesn't explain what each stress level means or show exact values for each month, which limits interpretation.
B. The font used in the chart is hard to read; using larger, uppercase text would improve clarity.
C. The chart should be in 3D format to show depth and highlight differences more clearly.
D. The months should be replaced with week numbers to provide more detailed insight.
E. The lines are too light and similar in color, making it difficult to distinguish between trends.

Answer: E

Explanation:
The lines are very light and visually similar, making it difficult to clearly distinguish between the different stress level trends, which directly reduces the effectiveness of the visualization in communicating patterns over time.

NEW QUESTION # 28
......

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