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Exam Code: PCAD-31-02
Exam Name: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02)
Certification Provider: Python Institute
Corresponding Certification: Python Institute Certification
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PCAD-31-02

Updated: Sep 19, 2026

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?

  • A. python get pandas
  • B. pip install pandas
  • C. install pip pandas
  • D. python install pandas
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

  • A. Cursor object for executing queries
  • B. Python's random module for generating keys
  • C. Connection object to interface with the database
  • D. Database driver or module (e.g., sqlite3)
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

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

  • A. Repeat all numerical data in both the chart and text
  • B. Avoid summarizing the data verbally
  • C. Include concise textual summaries that highlight the chart's message
  • D. Use visualizations alone without titles or captions
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

  • A. dictionary
  • B. tuple
  • C. list
  • D. set
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Which of the following would most likely be classified as unstructured data?

  • A. Inventory levels in a spreadsheet
  • B. Normalized employee database in PostgreSQL
  • C. Collection of customer service audio recordings
  • D. Product ID and price list in a CSV
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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