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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Which actions are considered best practices when managing datasets in Python using pandas?
(Choose two)

  • A. Replace missing values with hardcoded zeros without analysis
  • B. Use .info() to understand dataset structure
  • C. Always convert all columns to strings
  • D. Use .describe() for summary statistics
Reveal Solution  Discussion  0

Correct Answer: B,D  🗳️

Which statements correctly describe differences between linear and logistic regression?
(Choose two)

  • A. Logistic regression outputs probabilities, while linear regression predicts continuous values
  • B. Linear regression applies only to classification tasks
  • C. Logistic regression uses the logit function to model relationships
  • D. Linear regression requires target variables to be binary
Reveal Solution  Discussion  0

Correct Answer: A,C  🗳️

What is the most appropriate way to ensure that a column used as a foreign key contains only valid references to a parent DataFrame in Pandas?

  • A. Use df.merge() with how='outer'
  • B. Use df.dropna() on the foreign key column
  • C. Use df.sort_values() to sort both columns before merging
  • D. Use .isin() to compare the foreign key column against the parent key column
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

What is the main purpose of validating a dataset before applying statistical analysis?

  • A. To visualize patterns more clearly
  • B. To ensure the dataset meets expected quality and structure
  • C. To sort the data alphabetically
  • D. To generate more columns from the dataset
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?

  • A. To encapsulate related data and behavior for better organization
  • B. To ensure all values are stored as plain strings
  • C. To define global variables that persist across files
  • D. To avoid using loops and conditional logic
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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