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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Applied Data Analysis Projects	20%	- Data Analysis Workflow 1. Analysis and modeling 2. Results interpretation and presentation 3. Data exploration and cleaning 4. Problem definition - Exploratory Data Analysis (EDA) 1. Correlation analysis 2. Pattern identification 3. Descriptive statistics computation 4. Data distribution analysis
Topic 2: 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. Matplotlib basics 2. Seaborn introduction 3. Customizing plots 4. Creating basic charts (line, bar, scatter, histogram) - Pandas Library 1. GroupBy operations 2. Data selection and filtering 3. DataFrame operations (merge, join, concat) 4. Handling missing data 5. Series and DataFrame structures
Topic 3: Data Analysis Fundamentals	20%	- Introduction to Data Analysis 1. Data analysis process lifecycle 2. Data analysis concepts and terminology 3. Types of data (structured, unstructured, semi-structured) - Data Collection and Preparation 1. Data cleaning and preprocessing basics 2. Data sources and acquisition methods 3. Data import/export operations
Topic 4: 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. Return values and scope 2. Function definitions and parameters 3. Conditional statements (if, elif, else) 4. Loops (for, while) - Python Data Types and Structures 1. Data type conversions 2. Lists, tuples, dictionaries, sets 3. Numbers, strings, booleans

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q54-Q59):

NEW QUESTION # 54
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

A. Scatter plots
B. Histogram analysis
C. Box plots
D. Correlation matrix

Answer: A,D

NEW QUESTION # 55
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

A. df.rename()
B. df.sort_values()
C. df.replace()
D. df.dropna()

Answer: C

NEW QUESTION # 56
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].isnull().sum()
B. df['id'].nunique()
C. df['id'].fillna()
D. df['id'].duplicated().any()

Answer: D

NEW QUESTION # 57
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

A. matplotlib
B. csv
C. sqlite3
D. openpyxl

Answer: D

NEW QUESTION # 58
What is the benefit of using the __name__ == "__main__" construct in Python scripts?

A. It defines the main function in Python
B. It allows selective execution of code when run as a script
C. It prevents modules from being imported
D. It allows access to all global variables

Answer: B

NEW QUESTION # 59
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