PCED-30-02勉強の資料、PCED-30-02試験勉強過去問

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Python Institute PCED-30-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Working with Data and Performing Simple Analyses	32.5%	- 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 - Data Cleaning and Transformation 1. Handle missing and inconsistent data 2. Perform basic data transformation (filtering, sorting, grouping)
Topic 2: Communicating Insights and Reporting	12.5%	- 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 - Data Visualization 1. Select appropriate visuals for different data types 2. Interpret simple data visualizations 3. Recognize common visualization types (bar, line, pie charts)
Topic 3: 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. Read from and write to files (text, CSV) 2. Use the csv module for basic CSV operations - Python Fundamentals 1. Use variables, data types, and basic operators 2. Implement control flow structures (loops, conditionals) 3. Define and use functions
Topic 4: Introduction to Data and Data Analysis Concepts	22.5%	- 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 - 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

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Python Institute PCED - Certified Entry-Level Data Analyst with Python 認定 PCED-30-02 試験問題 (Q25-Q30):

質問 # 25
You have collected sales data and want to compute the average using the NumPy library.
Which option correctly shows how to install NumPy and use it in your code to perform this task?

A. To install NumPy:
pip install numpy as np
Then run this code:
import np
data = np.array([10, 20, 30])
print(np.mean(data))
B. To install NumPy:
from pip install numpy
Then run this code:
import numpy as np
data = np.array([10, 20, 30])
print(np.mean(data))
C. To install NumPy:
pip install numpy
Then run this code:
import numpy as np
data = numpy.arr([10, 20, 30])
print(numpy.mean(data))

正解：B

解説：
NumPy is installed with pip install numpy, then imported with import numpy as np. After that, you can create an array and compute the mean using np.array(...) and np.mean(...).

質問 # 26
A program uses slicing on a string "Python". The expression s[1:4] is evaluated. Which substring will be returned?

A. yth
B. ythn
C. tho
D. ytho

正解：A

解説：
Slicing includes the start index but excludes the end index. Characters at indices 1, 2, and 3 are selected, producing the substring "yth".

質問 # 27
A variable is assigned using chained assignment: a = b = c = 10. The developer then changes b =
5. What will be the value of a after this operation?

A. 0
B. None
C. 1
D. Error

正解：A

解説：
Integers are immutable in Python. Reassigning b to a new value does not affect a or c, which still reference the original integer value 10.

質問 # 28
A program uses while False: followed by print statements inside the loop. The developer expects output. What will actually happen during execution?

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

正解：A

解説：
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.

質問 # 29
An e-commerce site records every product view and purchase.
Which of the following best describes how these logs become marketing insights?

A. Raw event logs are stored, archived, and indexed - like by user ID and session - which then facilitate audit trails.
B. Raw event logs are filtered, aggregated, and formatted into summaries - like total visits per category - which then provide traffic reports.
C. Raw event logs are structured, analyzed, and interpreted into information - like conversion rates by product - which then builds knowledge of customer preferences.
D. Raw event logs are validated, organized, and compiled into datasets - like timestamped user actions - which then support system debugging.

正解：C

解説：
Marketing insights are produced when raw event logs are structured, analyzed, and interpreted to generate meaningful metrics such as conversion rates. These interpreted results transform data into actionable knowledge about customer behavior and preferences.

質問 # 30
......

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