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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

Answer: D


NEW QUESTION # 21
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

Answer: A,C


NEW QUESTION # 22
What may occur if a model is evaluated using the same data it was trained on?

Answer: B


NEW QUESTION # 23
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: C,D


NEW QUESTION # 24
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: B


NEW QUESTION # 25
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