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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q21-Q26):

NEW QUESTION # 21
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Connection object to interface with the database
B. Database driver or module (e.g., sqlite3)
C. Cursor object for executing queries
D. Python's random module for generating keys

Answer: A,B

NEW QUESTION # 22
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

A. Catching all exceptions using a generic except: clause without specifying the error
B. Using specific exception types like FileNotFoundError or ZeroDivisionError
C. Suppressing all error output using pass
D. Logging error messages for later inspection during failures

Answer: C,D

NEW QUESTION # 23
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

A. Features with different units and scales
B. Presence of duplicate data
C. Boolean feature encoding
D. Distance-based modeling algorithms

Answer: A,D

NEW QUESTION # 24
What is the primary reason to use .head() and .tail() methods in pandas when exploring datasets?

A. To rename columns
B. To delete missing values
C. To preview the first and last few rows of data
D. To create visualizations

Answer: C

NEW QUESTION # 25
Which techniques are most effective when designing reusable and testable functions in Python?
(Choose two)

A. Returning print statements instead of values
B. Avoiding external state or side effects
C. Creating large monolithic functions
D. Use of default parameters

Answer: B,D

NEW QUESTION # 26
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