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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q66-Q71):

NEW QUESTION # 66
Which function is used to reshape a NumPy array without modifying the original data values?

A. flatten()
B. resize()
C. transpose()
D. reshape()

Answer: D

NEW QUESTION # 67
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

A. Scatter plot
B. Line chart
C. Box plot
D. Pie chart

Answer: C

NEW QUESTION # 68
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

A. Commit → Connect → Execute → Close
B. Connect → Execute → Commit → Close
C. Execute → Connect → Commit → Close
D. Connect → Commit → Execute → Close

Answer: B

NEW QUESTION # 69
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 reduces processing time by skipping null checks
D. It guarantees compatibility with visualization tools

Answer: D

NEW QUESTION # 70
When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

A. mean()
B. skew()
C. sum()
D. std()

Answer: B

NEW QUESTION # 71
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