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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q92-Q97):

NEW QUESTION # 92
In supervised learning, what is the primary goal when training a model on labeled data?

A. To analyze unstructured data formats like text
B. To learn a function that maps inputs to known outputs
C. To cluster data points into similar groups
D. To discover hidden patterns without outputs

Answer: B

NEW QUESTION # 93
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

A. It uses random sampling with replacement
B. It assumes a normal distribution
C. It requires a large population sample
D. It enables confidence interval estimation

Answer: A,D

NEW QUESTION # 94
Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

A. df.to_csv()
B. df.memory_usage()
C. df.info()
D. df.dtypes

Answer: C,D

NEW QUESTION # 95
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

A. DataFrame.remove_duplicates()
B. DataFrame.nunique()
C. DataFrame.drop_duplicates()
D. DataFrame.unique()

Answer: A

NEW QUESTION # 96
When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

A. Creating samples by removing all duplicates
B. Scaling all values between 0 and 1 before resampling
C. Generating multiple datasets of the same size by randomly sampling with replacement
D. Drawing one sample and calculating the mean only once

Answer: C

NEW QUESTION # 97
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