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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q67-Q72):

NEW QUESTION # 67
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

A. list comprehensions
B. match-case statements
C. decorators
D. while loops

Answer: A,D

NEW QUESTION # 68
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.rename()
B. df.sort_values()
C. df.replace()
D. df.dropna()

Answer: C

NEW QUESTION # 69
What result will the following Pandas expression return: df['Age'].notnull().all()?

A. Returns True if at least one value in 'Age' is not NULL
B. Returns True if all values in 'Age' are not NULL
C. Returns the number of NULL values in 'Age'
D. Returns True if all values in 'Age' are NULL

Answer: B

NEW QUESTION # 70
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

A. Automatic plot animations
B. Changing plot color palettes
C. Inserting audio annotations
D. Adding plot titles and axis labels

Answer: B,D

NEW QUESTION # 71
Which actions are considered best practices when managing datasets in Python using pandas?
(Choose two)

A. Use .info() to understand dataset structure
B. Use .describe() for summary statistics
C. Always convert all columns to strings
D. Replace missing values with hardcoded zeros without analysis

Answer: A,B

NEW QUESTION # 72
......

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