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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q133-Q138):

NEW QUESTION # 133
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

Answer: C


NEW QUESTION # 134
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

Answer: C


NEW QUESTION # 135
Which type of chart is most appropriate when comparing the distribution of a numerical variable across multiple categories?

Answer: C


NEW QUESTION # 136
What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

Answer: B


NEW QUESTION # 137
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

Answer: B


NEW QUESTION # 138
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