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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q81-Q86):

NEW QUESTION # 81
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

Answer: C


NEW QUESTION # 82
What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

Answer: C


NEW QUESTION # 83
Which of the following statistical measures are considered measures of central tendency?
(Choose two)

Answer: A,D


NEW QUESTION # 84
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

Answer: D


NEW QUESTION # 85
Which practices are considered part of professional Python scripting standards?
(Choose two)

Answer: A,B


NEW QUESTION # 86
......

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