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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q126-Q131):

NEW QUESTION # 126
Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

Answer: A,D


NEW QUESTION # 127
Which function is used to reshape a NumPy array without modifying the original data values?

Answer: A


NEW QUESTION # 128
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

Answer: A,B


NEW QUESTION # 129
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

Answer: B


NEW QUESTION # 130
Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)

Answer: B,D


NEW QUESTION # 131
......

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