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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Working with Data Using Python Libraries	30%	- Pandas Library 1. Handling missing data 2. DataFrame operations (merge, join, concat) 3. Data selection and filtering 4. Series and DataFrame structures 5. GroupBy operations - NumPy Fundamentals 1. Basic statistical functions 2. Vectorized operations 3. NumPy arrays and operations 4. Array indexing and slicing - Data Visualization 1. Creating basic charts (line, bar, scatter, histogram) 2. Seaborn introduction 3. Matplotlib basics 4. Customizing plots
Topic 2: Data Analysis Fundamentals	20%	- Data Collection and Preparation 1. Data sources and acquisition methods 2. Data import/export operations 3. Data cleaning and preprocessing basics - Introduction to Data Analysis 1. Data analysis process lifecycle 2. Data analysis concepts and terminology 3. Types of data (structured, unstructured, semi-structured)
Topic 3: Python Programming for Data Analysis	30%	- File Operations 1. Reading from files (text, CSV) 2. Writing to files 3. Context managers (with statement) - Python Data Types and Structures 1. Lists, tuples, dictionaries, sets 2. Data type conversions 3. Numbers, strings, booleans - Control Flow and Functions 1. Loops (for, while) 2. Return values and scope 3. Function definitions and parameters 4. Conditional statements (if, elif, else)
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. 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 (Q25-Q30):

NEW QUESTION # 25
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

A. Adding plot titles and axis labels
B. Inserting audio annotations
C. Automatic plot animations
D. Changing plot color palettes

Answer: A,D

NEW QUESTION # 26
What may occur if a model is evaluated using the same data it was trained on?

A. Overfitting
B. Data leakage
C. Overgeneralization
D. Underfitting

Answer: A

NEW QUESTION # 27
Which of the following practices contributes most to making Python scripts modular and maintainable?

A. Writing all logic in the global scope
B. Using global variables freely
C. Hard-coding file paths in every function
D. Structuring code with reusable functions and modules

Answer: D

NEW QUESTION # 28
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?

A. A histogram of the data distribution
B. A normalized version of the data column
C. Basic descriptive statistics such as count, mean, and quartiles
D. Only the standard deviation of the data

Answer: C

NEW QUESTION # 29
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

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

Answer: B

NEW QUESTION # 30
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