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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q22-Q27):

NEW QUESTION # 22
What may occur if a model is evaluated using the same data it was trained on?

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

Answer: B

NEW QUESTION # 23
What is the main purpose of validating a dataset before applying statistical analysis?

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

Answer: A

NEW QUESTION # 24
Which of the following statements best describes the structural relationship between Pandas Series and DataFrames?

A. A Series can only be created from a DataFrame
B. A Series contains multiple DataFrames
C. A DataFrame is a one-dimensional array of Series
D. A DataFrame is a two-dimensional container of Series objects

Answer: D

NEW QUESTION # 25
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

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

Answer: B,D

NEW QUESTION # 26
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

A. id() function to retrieve unique object identifiers
B. str() function to convert data formats
C. is operator for memory-level comparison
D. == operator for checking value equivalence

Answer: A,C

NEW QUESTION # 27
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