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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q25-Q30):

NEW QUESTION # 25
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

A. FROM
B. GROUP BY
C. JOIN ON
D. HAVING

Answer: C

NEW QUESTION # 26
Which method is typically used in Pandas to check if a column contains only values within an expected range?

A. df.isin()
B. df.sample()
C. df.clip()
D. df.sort_values()

Answer: A

NEW QUESTION # 27
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

A. It configures multiple plots in a grid layout within a single figure
B. It creates a 3D surface plot using pandas
C. It links Matplotlib with Seaborn's grid styling
D. It overlays multiple plots in the same figure without axes

Answer: A

NEW QUESTION # 28
Which method is commonly used to handle missing numerical data in a dataset?

A. Filling with the mean or median
B. Dropping all columns
C. Multiplying all missing values by zero
D. Replacing with text labels

Answer: A

NEW QUESTION # 29
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

A. Catching all exceptions using a generic except: clause without specifying the error
B. Suppressing all error output using pass
C. Using specific exception types like FileNotFoundError or ZeroDivisionError
D. Logging error messages for later inspection during failures

Answer: B,D

NEW QUESTION # 30
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