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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q48-Q53):

NEW QUESTION # 48
Which method is commonly used to handle missing numerical data in a dataset?

A. Dropping all columns
B. Multiplying all missing values by zero
C. Replacing with text labels
D. Filling with the mean or median

Answer: D

NEW QUESTION # 49
Why is it important to define and apply validation rules consistently across all stages of a data pipeline?

A. It prevents the propagation of corrupted or invalid data
B. It automatically optimizes SQL queries
C. It guarantees compatibility with visualization tools
D. It reduces processing time by skipping null checks

Answer: C

NEW QUESTION # 50
What is the primary purpose of using a requirements.txt file in Python-based data projects?

A. To list the packages and versions required for consistent environment setup
B. To track script runtime dependencies during execution
C. To store the current working directory of Python modules
D. To automatically compress source code for deployment

Answer: A

NEW QUESTION # 51
Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

A. To reduce the number of iterations during training
B. To evaluate the model's performance on unseen data
C. To optimize the model's training accuracy
D. To ensure the loss function is minimized

Answer: B

NEW QUESTION # 52
Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

A. Using uniform date formats across columns
B. Aligning column headers to the left
C. Centering all data regardless of type
D. Applying consistent font and cell styling

Answer: A,D

NEW QUESTION # 53
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