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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q140-Q145):

NEW QUESTION # 140
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)

A. Allows the use of lambda for procedural logic
B. Supports clearer abstraction by modeling real-world entities
C. Encourages code reuse through inheritance and modularity
D. Enables procedural programming only through decorators

Answer: B,C

NEW QUESTION # 141
Which of the following statistical measures are considered measures of central tendency?
(Choose two)

A. Interquartile range
B. Mean
C. Variance
D. Mode

Answer: B,D

NEW QUESTION # 142
Which practices help maintain data integrity across large datasets in Pandas workflows?
(Choose two)

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

Answer: C,D

NEW QUESTION # 143
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

A. groupby().mean()
B. groupby().aggregate()
C. groupby().max()
D. groupby().count()

Answer: A

NEW QUESTION # 144
Why is it important to manage object identity correctly in complex data analysis pipelines?

A. It guarantees consistent hashing of string values
B. It avoids redundant copies and preserves shared state when required
C. It ensures type casting is consistently applied
D. It prevents reloading modules unnecessarily

Answer: B

NEW QUESTION # 145
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