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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q78-Q83):

NEW QUESTION # 78
Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

A. Aligning column headers to the left
B. Centering all data regardless of type
C. Using uniform date formats across columns
D. Applying consistent font and cell styling

Answer: C,D

NEW QUESTION # 79
What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

A. To avoid using loops and conditional logic
B. To define global variables that persist across files
C. To ensure all values are stored as plain strings
D. To encapsulate related data and behavior for better organization

Answer: D

NEW QUESTION # 80
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()

Answer: B

NEW QUESTION # 81
Which characteristics are commonly associated with cloud-based data storage solutions?

A. Elastic scalability
B. Physical proximity to user machines
C. High availability and redundancy
D. Requires dedicated local hardware

Answer: A

NEW QUESTION # 82
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

A. Using global variables instead of object properties
B. Encapsulating behavior in class methods
C. Applying inheritance to avoid redundant code
D. Replacing all functions with lambda expressions

Answer: B,C

NEW QUESTION # 83
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