Python Institute PCAD-31-02認定デベロッパー & PCAD-31-02専門試験

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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Working with Data Using Python Libraries	30%	- NumPy Fundamentals 1. NumPy arrays and operations 2. Array indexing and slicing 3. Vectorized operations 4. Basic statistical functions - Data Visualization 1. Customizing plots 2. Seaborn introduction 3. Matplotlib basics 4. Creating basic charts (line, bar, scatter, histogram) - Pandas Library 1. Series and DataFrame structures 2. Handling missing data 3. DataFrame operations (merge, join, concat) 4. Data selection and filtering 5. GroupBy operations
Applied Data Analysis Projects	20%	- Data Analysis Workflow 1. Data exploration and cleaning 2. Problem definition 3. Analysis and modeling 4. Results interpretation and presentation - Exploratory Data Analysis (EDA) 1. Data distribution analysis 2. Descriptive statistics computation 3. Pattern identification 4. Correlation analysis
Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data sources and acquisition methods 3. Data import/export operations - Introduction to Data Analysis 1. Data analysis concepts and terminology 2. Data analysis process lifecycle 3. Types of data (structured, unstructured, semi-structured)
Python Programming for Data Analysis	30%	- File Operations 1. Writing to files 2. Reading from files (text, CSV) 3. Context managers (with statement) - Python Data Types and Structures 1. Numbers, strings, booleans 2. Data type conversions 3. Lists, tuples, dictionaries, sets - Control Flow and Functions 1. Return values and scope 2. Function definitions and parameters 3. Conditional statements (if, elif, else) 4. Loops (for, while)

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PCAD-31-02専門試験、PCAD-31-02学習資料

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) 認定 PCAD-31-02 試験問題 (Q127-Q132):

質問 # 127
What is the primary purpose of using a requirements.txt file in Python-based data projects?

A. To store the current working directory of Python modules
B. To list the packages and versions required for consistent environment setup
C. To automatically compress source code for deployment
D. To track script runtime dependencies during execution

正解：B

質問 # 128
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Python's random module for generating keys
B. Connection object to interface with the database
C. Database driver or module (e.g., sqlite3)
D. Cursor object for executing queries

正解：B、C

質問 # 129
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A. Transactional data
B. Sensor logs
C. Web scraping
D. Surveys

正解：D

質問 # 130
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

A. groupby().mean()
B. groupby().count()
C. groupby().max()
D. groupby().aggregate()

正解：A

質問 # 131
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A. To optimize the model's training accuracy
B. To reduce the number of iterations during training
C. To evaluate the model's performance on unseen data
D. To ensure the loss function is minimized

正解：C

質問 # 132
......

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