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

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PCAD-31-02 Latest Exam Dumps & New PCAD-31-02 Dumps Questions

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

NEW QUESTION # 54
Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

A. df.loc[2:7, ['col1', 'col2']]
B. df['col1', 'col2']
C. df.iloc[0:5, 'col2']
D. df.loc[:, 'col1']

Answer: A,D

NEW QUESTION # 55
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

A. 0
B. Error
C. (4, 5, 6)
D. 5.0

Answer: D

NEW QUESTION # 56
Why is it important to adjust data presentations based on the audience's background?

A. To simplify all metrics to percentages only
B. To avoid using charts altogether
C. To ensure the data is understood and supports actionable insights
D. To include as many technical terms as possible

Answer: C

NEW QUESTION # 57
What is the main reason for using try-except blocks in a data analysis script that reads multiple files?

A. To automatically restart the script after execution
B. To enforce strict type annotations
C. To ensure functions always return None
D. To gracefully handle runtime errors without crashing the program

Answer: D

NEW QUESTION # 58
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 # 59
......

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