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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A. Sensor logs
B. Transactional data
C. Surveys
D. Web scraping

Answer: C

NEW QUESTION # 14
Why is it important to manage object identity correctly in complex data analysis pipelines?

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

Answer: D

NEW QUESTION # 15
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

A. matplotlib
B. NumPy
C. pandas
D. seaborn

Answer: B

NEW QUESTION # 16
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

A. numpy.outer()
B. array1.dot(array2)
C. numpy.matrix()
D. array1 * array2

Answer: D

NEW QUESTION # 17
Which techniques are most effective when designing reusable and testable functions in Python?
(Choose two)

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

Answer: A,B

NEW QUESTION # 18
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