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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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大量の時間と金銭をかかるのに比べて、正しい仕方は肝心なことです。もしあなたはPython Institute　PCAD-31-02試験に準備しているなら、あんたのための整理される備考資料はあなたにとって最善のオプションです。我々の目標はあなたに試験にうまく合格させることです。弊社の誠意を信じてもらいたいし、Python Institute　PCAD-31-02試験2成功するのを祈って願います。

Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) 認定 PCAD-31-02 試験問題 (Q15-Q20):

質問 # 15
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

A. plt.legend()
B. plt.annotate()
C. plt.title()
D. plt.xlabel()

正解：B

質問 # 16
Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?

A. python get pandas
B. pip install pandas
C. install pip pandas
D. python install pandas

正解：B

質問 # 17
What is the primary goal of pre-processing data before performing analysis?

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

正解：A

質問 # 18
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

A. tuple
B. set
C. list
D. dictionary

正解：B

質問 # 19
Which operations are valid when working with Pandas Series?
(Choose two)

A. Using .columns to rename
B. Merging by index using merge()
C. Applying NumPy universal functions
D. Arithmetic vector operations

正解：C、D

質問 # 20
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