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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which of the following statistical measures are considered measures of central tendency?
(Choose two)

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

Answer: A,D

NEW QUESTION # 47
Which operations are valid when working with Pandas Series?
(Choose two)

A. Using .columns to rename
B. Merging by index using merge()
C. Arithmetic vector operations
D. Applying NumPy universal functions

Answer: C,D

NEW QUESTION # 48
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A. Open Excel files using matplotlib
B. Use the requests module to retrieve JSON responses
C. Load CSV directly with re.compile()
D. Access data using requests.get() and parse it with .json()

Answer: B,D

NEW QUESTION # 49
Which characteristics are typically associated with supervised learning algorithms?
(Choose two)

A. They require labeled training data
B. They automatically detect outliers
C. They work only with numerical features
D. They are useful for regression and classification tasks

Answer: A,D

NEW QUESTION # 50
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

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

Answer: D

NEW QUESTION # 51
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