PYTHON INSTITUTEPython Institute PCAD - Certified Associate in Data Analytics with Python
The Python Institute PCAD certification validates the essential skills for performing practical data analysis at the junior to intermediate level. It covers the full data analysis cycle—from data acquisition and cleaning through exploration and modeling to communicating insights—with Python and SQL at its core. Earning PCAD demonstrates your ability to collect, integrate, clean, validate, analyze, and visualize data using essential libraries like Pandas, NumPy, Matplotlib, and Seaborn, and to apply sound programming practices including object-oriented design and exception handling. This credential is ideal for junior data analysts and professionals transitioning into data analytics, providing a competitive edge in data-driven industries.
378 practice questions · Updated 2025-01-01
PCAD-31 Curriculum
Every domain, objective, and concept the PCAD-31 exam measures.
- Data collection methods
- Data collection method selection
- Data aggregation techniques
- Data integration challenges
- Data storage solutions
- Data storage considerations
- Structured vs. Unstructured Data
- Identifying Erroneous Data
- Rectifying or Removing Erroneous Data
- Data Normalization
- Data Scaling
- Data Cleaning Techniques
- Data Standardization Techniques
- Basic Data Validation Methods
- Validation Rules for Data Integrity
- File Format Recognition
- Dataset Access and Management
- Data Extraction from Sources
- Spreadsheet Readability and Formatting
- Data Cleaning and Preprocessing
- Data Transformation for Analysis
- Python syntax and control structures for data problems
- Functions for data analysis
- Python Data Science ecosystem
- Core data structures for data organization
- Python scripting best practices
- Importing modules
- Module aliasing and selective imports
- Managing packages with PIP
- Understanding exceptions and tracebacks
- Try-except blocks
- Handling multiple exceptions
- The else and finally clauses
- Raising exceptions
- Creating custom exceptions
- Maintaining script robustness
- Classes and Objects
- Attributes and Methods
- Encapsulation and Data Hiding
- Inheritance and Polymorphism
- Composition and Aggregation
- Magic Methods for Comparison
- Object Identity and Hashing
- Copying and Mutability
- Design Patterns for Data Workflows
- Dunder Methods for Representation
- Data Classes and Named Tuples
- Object Lifecycle and Cleanup
- SQL SELECT Queries
- SQL Data Manipulation
- SQL Table Creation and Schema Definition
- Python Database Connections
- Parameterized Queries in Python
- SQL Data Types and Python Conversion
- SQL Injection Prevention
- Measures of Central Tendency
- Measures of Dispersion
- Percentiles and Quartiles
- Skewness and Kurtosis
- Correlation Analysis
- Covariance
- Scatter Plot Interpretation
- Bootstrapping fundamentals
- Constructing bootstrap sampling distributions
- Bootstrap confidence intervals
- Bootstrap standard errors and bias
- Applications and limitations of bootstrapping
- Linear regression concepts and assumptions
- Fitting and evaluating linear regression
- When to use linear regression
- Logistic regression concepts and assumptions
- Fitting and evaluating logistic regression
- When to use logistic regression
- Limitations of regression methods
- Data Cleaning with Pandas
- Data Organization with Pandas
- Merging Datasets with Pandas
- Reshaping Datasets with Pandas
- Series and DataFrame Relationship
- Data Access with Locators
- Slicing DataFrames and Series
- Array Operations in NumPy
- Core Data Structures Distinction
- Grouping Data with groupby
- Summarizing Data with Aggregations
- Extracting Insights from Data
- Descriptive Statistics Fundamentals
- Python Libraries for Descriptive Statistics
- Data Visualization for Descriptive Analysis
- Test Datasets and Overfitting
- Train-Test Split Techniques
- Supervised Learning Algorithms Overview
- Model Training and Prediction
- Model Accuracy Metrics
- Model Evaluation Workflow
- Matplotlib fundamentals
- Seaborn fundamentals
- Plot customization
- Subplots and figure layout
- Pros and cons of chart types
- Choosing appropriate representations
- Labels and titles
- Annotations and text
- Ticks and scales
- Styling and themes
- Handling overplotting
- Saving and exporting figures
- Audience Analysis
- Message Tailoring
- Visualization Selection
- Integrating Visuals and Text
- Key Finding Extraction
- Evidence-Based Reasoning
- Structured Presentation
Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for PCAD-31, so none is invented.