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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q124-Q129):

NEW QUESTION # 124
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

A. Avoiding color entirely
B. Adding descriptive axis labels
C. Customizing tick labels
D. Disabling grid lines in all cases

Answer: B,C

NEW QUESTION # 125
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

A. Key-value mapping
B. Slicing
C. Index-based iteration
D. Append

Answer: B,D

NEW QUESTION # 126
When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

A. std()
B. sum()
C. mean()
D. skew()

Answer: D

NEW QUESTION # 127
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

A. A moderate non-linear association
B. A strong negative linear relationship
C. A weak positive relationship
D. No correlation

Answer: B

NEW QUESTION # 128
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 = ':id'", {'id': user_id})
C. cursor.execute("SELECT * FROM users WHERE id = " + user_id)
D. cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))

Answer: D

NEW QUESTION # 129
......

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