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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q11-Q16):

NEW QUESTION # 11
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

A. [25, 100]
B. [10, 15]
C. [100, 225]
D. [25, 100, 225]

Answer: C

NEW QUESTION # 12
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

A. groupby().mean()
B. groupby().aggregate()
C. groupby().count()
D. groupby().max()

Answer: A

NEW QUESTION # 13
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 links Matplotlib with Seaborn's grid styling
C. It creates a 3D surface plot using pandas
D. It overlays multiple plots in the same figure without axes

Answer: A

NEW QUESTION # 14
What is the primary reason to use .head() and .tail() methods in pandas when exploring datasets?

A. To rename columns
B. To create visualizations
C. To preview the first and last few rows of data
D. To delete missing values

Answer: C

NEW QUESTION # 15
Which Python construct allows you to iterate over a list of dictionary values and apply conditional logic to accumulate numerical values that meet a certain threshold?

A. while loop with try block
B. lambda expression with reduce
C. filter() function with map
D. for loop with if condition

Answer: D

NEW QUESTION # 16
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