PCAD Practice Exams and Study Guide
Python Institute PCAD Exam Specifications
- Exam Code:
- PCAD
- Exam Name:
- Python Institute Certified Associate Data Analyst with Python
- Provider:
- Python Institute
- Time:
- 60 min
- Passing Percentage:
- 75%
- Version:
- 6.0
Question Types
- 120 Single Select
120 Questions with Answers
- PDF Printable (Q & A)
- 3 Months
Key Features to Key Benefits
Real Exam-like Questions and Answers (120 total)
Expert-Curated Questions
Comprehensive Coverage of Objectives
Instant Access After Purchase
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Regularly Updated Content
Free Demo Questions Available
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Python Institute Certified Associate Data Analyst with Python Topics for Real-World Roles
For the Python Institute Certified Associate Data Analyst with Python exam, you'll clean and preprocess real-world datasets in pandas, handle missing values and type conversions, ingest data from CSV, Excel, databases, and APIs, validate data quality and lineage, and automate routine tasks to establish reproducible workflows. You'll perform exploratory data analysis to summarize data and spot patterns and outliers, engineer features to improve analytic models, apply basic statistical testing and confidence measures, create clear visualizations with matplotlib and seaborn, and prepare concise data stories and reports to inform stakeholders and guide business decisions.
Role‑Aligned Topics
- Clean and preprocess real-world datasets with Python using pandas to handle missing values and type casting
- Perform exploratory data analysis to summarize data, identify patterns, and spot outliers
- Create clear, compelling data visualizations with matplotlib and seaborn to communicate insights
- Engineer features and transform data to improve analytic models and decision-making
- Ingest data from diverse sources such as CSV, Excel, databases, and APIs
- Validate data quality, document data lineage, and implement reproducible data workflows
- Apply basic statistical methods for hypothesis testing, confidence intervals, and uncertainty quantification
- Automate routine data tasks with Python scripts to save time and reduce errors
- Prepare concise data stories and reports that inform stakeholders and support business decisions
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