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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Working with Data Using Python Libraries30%- Pandas Library
  • 1. Handling missing data
  • 2. Series and DataFrame structures
  • 3. Data selection and filtering
  • 4. DataFrame operations (merge, join, concat)
  • 5. GroupBy operations
- NumPy Fundamentals
  • 1. Vectorized operations
  • 2. Array indexing and slicing
  • 3. NumPy arrays and operations
  • 4. Basic statistical functions
- Data Visualization
  • 1. Customizing plots
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Seaborn introduction
  • 4. Matplotlib basics
Python Programming for Data Analysis30%- 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. Return values and scope
  • 4. Conditional statements (if, elif, else)
- File Operations
  • 1. Context managers (with statement)
  • 2. Writing to files
  • 3. Reading from files (text, CSV)
Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Pattern identification
  • 2. Data distribution analysis
  • 3. Descriptive statistics computation
  • 4. Correlation analysis
- Data Analysis Workflow
  • 1. Problem definition
  • 2. Data exploration and cleaning
  • 3. Analysis and modeling
  • 4. Results interpretation and presentation
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. Types of data (structured, unstructured, semi-structured)
  • 2. Data analysis process lifecycle
  • 3. Data analysis concepts and terminology

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q45-Q50):

NEW QUESTION # 45
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

Answer: A,D


NEW QUESTION # 46
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

Answer: A


NEW QUESTION # 47
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

Answer: D


NEW QUESTION # 48
Why is it important to define and apply validation rules consistently across all stages of a data pipeline?

Answer: D


NEW QUESTION # 49
What is the main purpose of using the bootstrapping technique in inferential statistics?

Answer: D


NEW QUESTION # 50
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