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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

A. Resetting the index after filtering
B. Dropping column headers
C. Converting the DataFrame to a Series
D. Ensuring consistent data types

Answer: A,D

NEW QUESTION # 46
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A. cursor.execute("SELECT * FROM users WHERE id = ?", user_id)
B. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
C. cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})
D. cursor.execute("SELECT * FROM users WHERE id = " + user_id)

Answer: B

NEW QUESTION # 47
What is the main purpose of validating a dataset before applying statistical analysis?

A. To sort the data alphabetically
B. To generate more columns from the dataset
C. To ensure the dataset meets expected quality and structure
D. To visualize patterns more clearly

Answer: C

NEW QUESTION # 48
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

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

Answer: C,D

NEW QUESTION # 49
Which practices help ensure secure and effective execution of SQL queries in Python scripts?
(Choose all that apply)

A. Using string concatenation to insert variables into SQL
B. Employing parameterized statements for user-supplied data
C. Using string formatting to build SQL queries dynamically
D. Wrapping SQL commands in try-except blocks

Answer: B,D

NEW QUESTION # 50
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