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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q126-Q131):

NEW QUESTION # 126
What is the main reason for using try-except blocks in a data analysis script that reads multiple files?

A. To ensure functions always return None
B. To gracefully handle runtime errors without crashing the program
C. To automatically restart the script after execution
D. To enforce strict type annotations

Answer: B

NEW QUESTION # 127
What is the main purpose of validating a dataset before applying statistical analysis?

A. To ensure the dataset meets expected quality and structure
B. To generate more columns from the dataset
C. To sort the data alphabetically
D. To visualize patterns more clearly

Answer: A

NEW QUESTION # 128
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

A. Scatter plot
B. Histogram
C. Line chart
D. Heatmap

Answer: A,D

NEW QUESTION # 129
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

A. Linear regression
B. Decision tree regression
C. Polynomial regression
D. Logistic regression

Answer: D

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

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

Answer: D

NEW QUESTION # 131
......

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