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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q141-Q146):

NEW QUESTION # 141
What is the primary role of the pandas library in the Python data science ecosystem?

A. Performing statistical tests
B. Manipulating labeled data structures
C. Visualizing large datasets
D. Reducing execution time of compiled code

Answer: B

NEW QUESTION # 142
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

A. Dropping column headers
B. Ensuring consistent data types
C. Resetting the index after filtering
D. Converting the DataFrame to a Series

Answer: B,C

NEW QUESTION # 143
Why is it important to adjust data presentations based on the audience's background?

A. To include as many technical terms as possible
B. To ensure the data is understood and supports actionable insights
C. To simplify all metrics to percentages only
D. To avoid using charts altogether

Answer: B

NEW QUESTION # 144
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A. Distance-based modeling algorithms
B. Features with different units and scales
C. Boolean feature encoding
D. Presence of duplicate data

Answer: A,B

NEW QUESTION # 145
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

A. It uses random sampling with replacement
B. It enables confidence interval estimation
C. It assumes a normal distribution
D. It requires a large population sample

Answer: A,B

NEW QUESTION # 146
......

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