免費下載PCAD-31-02考題，PCAD-31-02證照考試

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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最新的 Python Institute Certification PCAD-31-02 免費考試真題 (Q117-Q122):

問題 #117
What is a key assumption of linear regression that distinguishes it from logistic regression?

答案：B


問題 #118
Why is it important to manage object identity correctly in complex data analysis pipelines?

答案：B


問題 #119
Which of the following are limitations of manual data collection methods?
(Choose two)

答案：A,B


問題 #120
Which operations are valid when working with Pandas Series?
(Choose two)

答案：B,C


問題 #121
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

答案：B


問題 #122
......

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