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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q138-Q143):

NEW QUESTION # 138
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

A. sqlalchemy
B. pandas.read_html()
C. math
D. re

Answer: B

NEW QUESTION # 139
Which method is commonly used to handle missing numerical data in a dataset?

A. Filling with the mean or median
B. Dropping all columns
C. Replacing with text labels
D. Multiplying all missing values by zero

Answer: A

NEW QUESTION # 140
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
B. cursor.execute("SELECT * FROM users WHERE id = " + user_id)
C. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})
D. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)

Answer: A

NEW QUESTION # 141
Which practices help maintain data integrity across large datasets in Pandas workflows?
(Choose two)

A. Automatically converting all text to uppercase
B. Using assertions to enforce domain-specific rules
C. Sorting columns alphabetically
D. Defining and enforcing schema constraints

Answer: B,D

NEW QUESTION # 142
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. Logistic regression
D. Polynomial regression

Answer: C

NEW QUESTION # 143
......

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