Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) PCAD-31-02 Certified Exam Dumps

PCAD-31-02 Exam Dumps

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Certification Provider: Python Institute
Exam Code / Number: PCAD-31-02
Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
Exam Questions: 146
Last Updated: Jul 02, 2026
Corresponding Certification: Python Institute Certification

Go To PCAD-31-02 Questions

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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


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