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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q129-Q134):

NEW QUESTION # 129
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

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

Answer: A

NEW QUESTION # 130
What distinguishes structured data from unstructured data in the context of data modeling and storage?

A. Structured data follows a consistent schema or format
B. Unstructured data contains only numeric values
C. Structured data cannot be stored in databases
D. Unstructured data cannot be stored or analyzed

Answer: A

NEW QUESTION # 131
Which technique is considered a primary defense against SQL injection when executing database queries from Python?

A. Escaping quotes manually in SQL strings
B. Closing the database cursor after each query
C. Using parameterized queries
D. Encrypting the database connection

Answer: C

NEW QUESTION # 132
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

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

Answer: C,D

NEW QUESTION # 133
What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

A. To split the data into subgroups for aggregation
B. To sort the DataFrame by a specific column
C. To append new rows to the DataFrame
D. To filter rows based on index values

Answer: A

NEW QUESTION # 134
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