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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 sources and acquisition methods 3. Data import/export operations - Introduction to Data Analysis 1. Types of data (structured, unstructured, semi-structured) 2. Data analysis process lifecycle 3. Data analysis concepts and terminology
Topic 2: Python Programming for Data Analysis	30%	- File Operations 1. Reading from files (text, CSV) 2. Writing to files 3. Context managers (with statement) - Python Data Types and Structures 1. Data type conversions 2. Numbers, strings, booleans 3. Lists, tuples, dictionaries, sets - Control Flow and Functions 1. Return values and scope 2. Conditional statements (if, elif, else) 3. Loops (for, while) 4. Function definitions and parameters
Topic 3: Working with Data Using Python Libraries	30%	- NumPy Fundamentals 1. NumPy arrays and operations 2. Vectorized operations 3. Basic statistical functions 4. Array indexing and slicing - Data Visualization 1. Customizing plots 2. Creating basic charts (line, bar, scatter, histogram) 3. Matplotlib basics 4. Seaborn introduction - Pandas Library 1. Series and DataFrame structures 2. Handling missing data 3. DataFrame operations (merge, join, concat) 4. GroupBy operations 5. Data selection and filtering
Topic 4: Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Pattern identification 2. Data distribution analysis 3. Correlation analysis 4. Descriptive statistics computation - 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 (Q142-Q147):

NEW QUESTION # 142
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

A. Logistic regression outputs probabilities, while linear regression predicts continuous values
B. Linear regression applies only to classification tasks
C. Logistic regression uses the logit function to model relationships
D. Linear regression requires target variables to be binary

Answer: A,C

NEW QUESTION # 143
Which metric is most appropriate for evaluating the accuracy of a classification model?

A. Confusion matrix
B. Mean squared error
C. Precision
D. R-squared

Answer: C

NEW QUESTION # 144
Which method is commonly used to handle missing numerical data in a dataset?

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

Answer: C

NEW QUESTION # 145
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A. Parquet stores data in plain text, making it human-readable
B. Parquet offers row-based compression
C. Parquet files can only be used with Excel
D. Parquet supports schema evolution and columnar storage

Answer: D

NEW QUESTION # 146
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

A. Database driver or module (e.g., sqlite3)
B. Python's random module for generating keys
C. Connection object to interface with the database
D. Cursor object for executing queries

Answer: A,C

NEW QUESTION # 147
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