Python Institute最新PCAD-31-02考題：Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)＆認證成功保證，簡單的培訓方式

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Descriptive statistics
  • 1. Frequency distributions and percentiles
  • 2. Measures of central tendency and dispersion
  • 3. Correlation and covariance analysis
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Probability concepts and distributions
  • 3. Statistical significance and interpretation
Topic 2: SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Error handling and best practices
  • 3. Connecting to SQLite, MySQL or PostgreSQL
Topic 3: Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Audience-focused presentation
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Customization and styling
  • 3. Heatmaps, pair plots and correlation matrices
Topic 4: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Hyperparameter tuning basics
  • 3. Overfitting, underfitting and generalization
- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Model training, testing and evaluation
  • 3. Regression: linear, multiple, polynomial
Topic 5: Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data integration and merging
  • 3. Data collection methods and sources

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最新的 Python Institute Certification PCAD-31-02 免費考試真題 (Q74-Q79):

問題 #74
Which characteristics are commonly associated with cloud-based data storage solutions?

答案：D


問題 #75
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

答案：B,C


問題 #76
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

答案：B,D


問題 #77
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

答案：B,D


問題 #78
What is the primary role of the pandas library in the Python data science ecosystem?

答案：A


問題 #79
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