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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q86-Q91):

NEW QUESTION # 86
What does the standard deviation of a dataset represent in the context of statistical analysis?

A. The sum of all values in the dataset
B. The most frequently occurring value
C. The average squared distance from the mean
D. The number of outliers in the dataset

Answer: C

NEW QUESTION # 87
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

A. df['id'].nunique()
B. df['id'].isnull().sum()
C. df['id'].duplicated().any()
D. df['id'].fillna()

Answer: C

NEW QUESTION # 88
What is the primary purpose of using annotations in a data visualization?

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

Answer: B

NEW QUESTION # 89
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A. Use the requests module to retrieve JSON responses
B. Open Excel files using matplotlib
C. Load CSV directly with re.compile()
D. Access data using requests.get() and parse it with .json()

Answer: A,D

NEW QUESTION # 90
Which method is used after grouping a DataFrame to compute the average value for each group in a specific column?

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

Answer: C

NEW QUESTION # 91
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