PCAD-31-02 Latest Test Fee, PCAD-31-02 Exam Objectives

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q97-Q102):

NEW QUESTION # 97
What is a major challenge in aggregating data from multiple sources?
Response:

Answer: D


NEW QUESTION # 98
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

Answer: D


NEW QUESTION # 99
Which statements correctly describe differences between linear and logistic regression?
(Choose two)

Answer: A,C


NEW QUESTION # 100
Which operations are valid when working with Pandas Series?
(Choose two)

Answer: A,C


NEW QUESTION # 101
What is a best practice when organizing data in a spreadsheet for analysis?

Answer: C


NEW QUESTION # 102
......

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