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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q99-Q104):

NEW QUESTION # 99
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Linear regression requires target variables to be binary
B. Linear regression applies only to classification tasks
C. Logistic regression uses the logit function to model relationships
D. Logistic regression outputs probabilities, while linear regression predicts continuous values

Answer: C,D

NEW QUESTION # 100
Why is min-max scaling applied before using algorithms like K-Nearest Neighbors or SVM?

A. To make all features interpretable as binary values
B. To convert strings into numeric values
C. To prevent features with larger scales from dominating distance calculations
D. To compress all values into integers

Answer: C

NEW QUESTION # 101
What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

A. To improve data visualization aesthetics
B. To reduce memory usage in the dataset
C. To avoid treating the same category as different due to case differences
D. To make string comparison operations slower

Answer: C

NEW QUESTION # 102
Which method is commonly used to handle missing numerical data in a dataset?

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

Answer: C

NEW QUESTION # 103
Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)

A. Ignoring data types and treating everything as strings
B. Automatically casting Python variables to SQL column types without validation
C. Using a mapping function to convert SQL types into Python objects
D. Defining column types in Python using sqlite3.register_converter()

Answer: C,D

NEW QUESTION # 104
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