PCAD-31-02높은통과율인기덤프 - PCAD-31-02인증자료

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute PCAD-31-02인증자료, PCAD-31-02퍼펙트 공부자료

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최신 Python Institute Certification PCAD-31-02 무료샘플문제 (Q82-Q87):

질문 # 82
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

정답：C


질문 # 83
When performing bootstrapping on a dataset with 500 observations, what is a typical procedure?

정답：B


질문 # 84
What is the benefit of using the __name__ == "__main__" construct in Python scripts?

정답：D


질문 # 85
Which method is commonly used to handle missing numerical data in a dataset?

정답：D


질문 # 86
Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

정답：A


질문 # 87
......

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