Python Institute PCAD-31-02 PDF

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which of the following practices contributes most to making Python scripts modular and maintainable?

Answer: B


NEW QUESTION # 27
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

Answer: A,D


NEW QUESTION # 28
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

Answer: A


NEW QUESTION # 29
What is the correct SQL statement to permanently remove a table named customers from a database?

Answer: D


NEW QUESTION # 30
Which of the following are key differences between Pandas and NumPy arrays?
(Choose two)

Answer: B,C


NEW QUESTION # 31
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