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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Python Programming for Data Analysis30%- Control Flow and Functions
  • 1. Conditional statements (if, elif, else)
  • 2. Function definitions and parameters
  • 3. Return values and scope
  • 4. Loops (for, while)
- File Operations
  • 1. Reading from files (text, CSV)
  • 2. Writing to files
  • 3. Context managers (with statement)
- Python Data Types and Structures
  • 1. Lists, tuples, dictionaries, sets
  • 2. Data type conversions
  • 3. Numbers, strings, booleans
Working with Data Using Python Libraries30%- Data Visualization
  • 1. Seaborn introduction
  • 2. Customizing plots
  • 3. Matplotlib basics
  • 4. Creating basic charts (line, bar, scatter, histogram)
- NumPy Fundamentals
  • 1. Basic statistical functions
  • 2. Vectorized operations
  • 3. Array indexing and slicing
  • 4. NumPy arrays and operations
- Pandas Library
  • 1. GroupBy operations
  • 2. Handling missing data
  • 3. Series and DataFrame structures
  • 4. Data selection and filtering
  • 5. DataFrame operations (merge, join, concat)
Data Analysis Fundamentals20%- Data Collection and Preparation
  • 1. Data cleaning and preprocessing basics
  • 2. Data import/export operations
  • 3. Data sources and acquisition methods
- Introduction to Data Analysis
  • 1. Data analysis concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
Applied Data Analysis Projects20%- Data Analysis Workflow
  • 1. Analysis and modeling
  • 2. Results interpretation and presentation
  • 3. Data exploration and cleaning
  • 4. Problem definition
- Exploratory Data Analysis (EDA)
  • 1. Pattern identification
  • 2. Correlation analysis
  • 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 (Q21-Q26):

NEW QUESTION # 21
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

Answer: B,D


NEW QUESTION # 22
What is the benefit of using the __name__ == "__main__" construct in Python scripts?

Answer: C


NEW QUESTION # 23
Which element is essential to justify a conclusion drawn from a dataset?

Answer: B


NEW QUESTION # 24
What is the primary purpose of using a requirements.txt file in Python-based data projects?

Answer: D


NEW QUESTION # 25
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: B,C


NEW QUESTION # 26
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