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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute PCAD-31-02日本語版サンプル & PCAD-31-02日本語解説集

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) 認定 PCAD-31-02 試験問題 (Q33-Q38):

質問 # 33
What is the primary goal of pre-processing data before performing analysis?

A. To obscure sensitive information
B. To minimize the number of data points
C. To ensure the data is clean, consistent, and suitable for modeling
D. To compress datasets for storage efficiency

正解：C

質問 # 34
Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

A. var()
B. sort_values()
C. pivot_table()
D. median()

正解：A、D

質問 # 35
Which metric is most appropriate for evaluating the accuracy of a classification model?

A. Precision
B. Mean squared error
C. R-squared
D. Confusion matrix

正解：A

質問 # 36
Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

A. merge(..., how='inner')
B. append()
C. join(..., how='outer')
D. concat()

正解：A

質問 # 37
Which technique is considered a primary defense against SQL injection when executing database queries from Python?

A. Using parameterized queries
B. Closing the database cursor after each query
C. Encrypting the database connection
D. Escaping quotes manually in SQL strings

正解：A

質問 # 38
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