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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which of the following would most likely be classified as unstructured data?

Answer: A


NEW QUESTION # 109
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

Answer: A,D


NEW QUESTION # 110
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

Answer: A


NEW QUESTION # 111
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

Answer: A,D


NEW QUESTION # 112
Which features are commonly supported by both Matplotlib and Seaborn for customizing visualizations?
(Choose two)

Answer: C,D


NEW QUESTION # 113
......

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