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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Descriptive statistics computation
  • 3. Correlation analysis
  • 4. Pattern identification
- Data Analysis Workflow
  • 1. Analysis and modeling
  • 2. Data exploration and cleaning
  • 3. Results interpretation and presentation
  • 4. Problem definition
Python Programming for Data Analysis30%- Control Flow and Functions
  • 1. Return values and scope
  • 2. Loops (for, while)
  • 3. Conditional statements (if, elif, else)
  • 4. Function definitions and parameters
- File Operations
  • 1. Writing to files
  • 2. Reading from files (text, CSV)
  • 3. Context managers (with statement)
- Python Data Types and Structures
  • 1. Data type conversions
  • 2. Lists, tuples, dictionaries, sets
  • 3. Numbers, strings, booleans
Data Analysis Fundamentals20%- Data Collection and Preparation
  • 1. Data sources and acquisition methods
  • 2. Data import/export operations
  • 3. Data cleaning and preprocessing basics
- Introduction to Data Analysis
  • 1. Data analysis concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
Working with Data Using Python Libraries30%- NumPy Fundamentals
  • 1. Basic statistical functions
  • 2. Array indexing and slicing
  • 3. Vectorized operations
  • 4. NumPy arrays and operations
- Data Visualization
  • 1. Matplotlib basics
  • 2. Customizing plots
  • 3. Creating basic charts (line, bar, scatter, histogram)
  • 4. Seaborn introduction
- Pandas Library
  • 1. DataFrame operations (merge, join, concat)
  • 2. Data selection and filtering
  • 3. Handling missing data
  • 4. GroupBy operations
  • 5. Series and DataFrame structures

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

1. Which data collection method is most appropriate when gathering opinions from a target audience at scale?

A) Web scraping
B) Transactional data
C) Sensor logs
D) Surveys


2. What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

A) Seaborn requires no data preprocessing for missing values
B) Seaborn provides high-level abstractions for attractive and informative graphics
C) Seaborn automatically saves plots as image files
D) Seaborn works exclusively with NumPy arrays


3. What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

A) Abstraction
B) Encapsulation
C) Composition
D) Inheritance


4. Which methods can be used to validate data types and structure in a Pandas DataFrame?
(Choose two)

A) df.dtypes
B) df.to_csv()
C) df.info()
D) df.memory_usage()


5. Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

A) pandas.read_html()
B) math
C) sqlalchemy
D) re


Solutions:

Question # 1
Answer: D
Question # 2
Answer: B
Question # 3
Answer: D
Question # 4
Answer: A,C
Question # 5
Answer: A

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