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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q92-Q97):

NEW QUESTION # 92
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

Answer: A,B


NEW QUESTION # 93
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

Answer: A,B


NEW QUESTION # 94
What does the standard deviation of a dataset represent in the context of statistical analysis?

Answer: D


NEW QUESTION # 95
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

Answer: C,D


NEW QUESTION # 96
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

Answer: A,C


NEW QUESTION # 97
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