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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which term best describes the process of combining customer data from multiple systems into a single unified dataset?

A. Data normalization
B. Data binning
C. Data warehousing
D. Data integration

Answer: D

NEW QUESTION # 109
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

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

Answer: A,C

NEW QUESTION # 110
Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?

A. python install pandas
B. pip install pandas
C. python get pandas
D. install pip pandas

Answer: B

NEW QUESTION # 111
Which method is typically used in Pandas to check if a column contains only values within an expected range?

A. df.isin()
B. df.sort_values()
C. df.clip()
D. df.sample()

Answer: A

NEW QUESTION # 112
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. Enables procedural programming only through decorators
C. Encourages code reuse through inheritance and modularity
D. Supports clearer abstraction by modeling real-world entities

Answer: C,D

NEW QUESTION # 113
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