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

  • Exam Code: PCAD-31-02
  • Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
  • Updated: Oct 05, 2026

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
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 process lifecycle
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis concepts and terminology
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. Context managers (with statement)
  • 3. Writing to files
- Python Data Types and Structures
  • 1. Numbers, strings, booleans
  • 2. Data type conversions
  • 3. Lists, tuples, dictionaries, sets
Working with Data Using Python Libraries30%- NumPy Fundamentals
  • 1. NumPy arrays and operations
  • 2. Array indexing and slicing
  • 3. Vectorized operations
  • 4. Basic statistical functions
- Pandas Library
  • 1. GroupBy operations
  • 2. Handling missing data
  • 3. DataFrame operations (merge, join, concat)
  • 4. Series and DataFrame structures
  • 5. Data selection and filtering
- Data Visualization
  • 1. Seaborn introduction
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Matplotlib basics
  • 4. Customizing plots
Applied Data Analysis Projects20%- Data Analysis Workflow
  • 1. Data exploration and cleaning
  • 2. Results interpretation and presentation
  • 3. Problem definition
  • 4. Analysis and modeling
- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Descriptive statistics computation
  • 3. Correlation analysis
  • 4. Pattern identification

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

Question #1

Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

  • A. Logging error messages for later inspection during failures
  • B. Suppressing all error output using pass
  • C. Using specific exception types like FileNotFoundError or ZeroDivisionError
  • D. Catching all exceptions using a generic except: clause without specifying the error
Reveal Solution  Discussion  0

Correct Answer: A,B  🗳️

Question #2

Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

  • A. Boolean feature encoding
  • B. Presence of duplicate data
  • C. Features with different units and scales
  • D. Distance-based modeling algorithms
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

Question #3

What is the main reason to remove duplicate records before performing analysis?

  • A. It ensures training data is intentionally overfitted
  • B. It helps increase sample size
  • C. It reduces redundancy that can skew statistical metrics
  • D. It avoids converting data to JSON format
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #4

What is the primary role of the pandas library in the Python data science ecosystem?

  • A. Manipulating labeled data structures
  • B. Performing statistical tests
  • C. Reducing execution time of compiled code
  • D. Visualizing large datasets
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #5

What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

  • A. A strong negative linear relationship
  • B. A moderate non-linear association
  • C. A weak positive relationship
  • D. No correlation
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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