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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Classify data as quantitative or qualitative 2. Differentiate structured, semi-structured, and unstructured data 3. Explain how data becomes meaningful - 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
Topic 2: Communicating Insights and Reporting	12.5%	- Data Storytelling and Reporting 1. Structure insights as a narrative 2. Present insights with visual and verbal techniques 3. Create clear and concise analytical reports - Data Visualization 1. Recognize common visualization types (bar, line, pie charts) 2. Select appropriate visuals for different data types 3. Interpret simple data visualizations
Topic 3: 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
Topic 4: 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. Use the math and statistics modules for basic calculations 2. Perform basic operations with NumPy arrays 3. Utilize the collections module for specialized containers 4. Work with the datetime module for date/time data - Simple Analytical Techniques 1. Identify basic patterns and trends in data 2. Calculate descriptive statistics (mean, median, mode)

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Python Institute PCED - Certified Entry-Level Data Analyst with Python Sample Questions (Q14-Q19):

NEW QUESTION # 14
Given the following string:
text = "Report2024Final"
Which of the following statements are entirely correct? (Choose two.)

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

Answer: B,C,D

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 # 15
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, 180, 10)
target_temps = np.linspace(35.0, 37.0, 4)
import numpy as np
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, 181, 10)
target_temps = np.linspace(35.0, 37.0, 5)
D. timestamps = np.linspace(0, 180, 10)
target_temps = np.arange(35.0, 37.0, 5)
import numpy as np

Answer: C

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 # 16
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(math.ceil(statistics.mean(steps)))
print(statistics.median(steps))
B. import math
print(math.stdev(steps))
print(statistics.mean(steps))
print(statistics.median(steps))
import statistics
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(statistics.stdev(steps))
print(round(math.mean(steps)))
print(math.median(steps))
import statistics

Answer: A

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 # 17
A teacher wants to compare the number of students in different school clubs:

Which type of chart would be most appropriate to show this comparison? Select the best answer.

A. Bubble chart
B. Pie chart
C. Line chart
D. Bar chart

Answer: D

Explanation:
A bar chart is best for comparing quantities across distinct categories, such as the number of students in each club, because it clearly shows differences in counts side by side.

NEW QUESTION # 18
An online retailer collects customer reviews, order transaction logs, and product ratings.
Which of the following correctly classifies these types of data? Select the best answer.

A. Product ratings are quantitative and unstructured; customer reviews are structured and qualitative; transaction logs are quantitative and unstructured.
B. Product ratings are qualitative and unstructured; customer reviews are quantitative and structured; transaction logs are semi-structured and qualitative.
C. Product ratings are quantitative and structured; customer reviews are qualitative and unstructured; transaction logs are structured and quantitative
D. Product ratings are qualitative and structured; customer reviews are qualitative and unstructured; transaction logs are quantitative and semi-structured.

Answer: C

Explanation:
Product ratings are numeric values stored in fixed fields, making them quantitative and structured.
Customer reviews are free-text responses, which are qualitative and unstructured. Transaction logs consist of organized records with defined fields such as order ID, date, and amount, making them structured and quantitative.

NEW QUESTION # 19
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