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

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Quiz Python Institute - Updated PCAD-31-02 - Study Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Reference

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q19-Q24):

NEW QUESTION # 19
In supervised learning, what is the primary goal when training a model on labeled data?

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

Answer: A

NEW QUESTION # 20
What is the primary purpose of using annotations in a data visualization?

A. To highlight important data points and provide contextual information
B. To enhance interactivity in static plots
C. To replace the need for axes
D. To ensure faster rendering in large datasets

Answer: A

NEW QUESTION # 21
When fetching DATETIME values from a SQL database into Python, which Python data type is most appropriate for storing and manipulating these values?

A. float
B. datetime.datetime
C. time.struct_time
D. str

Answer: B

NEW QUESTION # 22
Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

A. ROLLBACK
B. SELECT
C. INSERT
D. UPDATE

Answer: B,C

NEW QUESTION # 23
Why might using a pie chart to represent category proportions be less effective than a bar chart?

A. Pie charts display individual data points more clearly
B. Pie charts cannot be created using Seaborn
C. Bar charts are limited to one variable only
D. Human perception is less accurate at judging angles than lengths

Answer: D

NEW QUESTION # 24
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