OpenEDG PCAD study guide
OpenEDG PCAD · Work through your exam, one topic at a time.
PCAD – Certified Associate Data Analyst with Python is the associate-level credential in the Python Institute's Data Science track, positioned between the entry-level PCED and the professional-level PCPD (still in development). It validates practical data analysis skills at junior to intermediate level: acquiring and integrating data from databases, APIs and web content; cleaning and validating it; exploring and analyzing it with Pandas and NumPy; querying structured data with SQL and connecting Python scripts to databases; applying descriptive and inferential statistics; building and evaluating foundational regression models; communicating findings with Matplotlib and Seaborn; and following sound programming practices (object-oriented design, exception handling) and ethical data handling. The OpenEDG PCAD study guide has 9 sections and about 2,917 words.
01Data Acquisition and Integration
Data acquisition and integration covers getting data from relational databases, HTTP APIs, and web or CSV sources, then combining it into one trustworthy dataset. The exam expects you to know the mechanics of each source and the failure modes that break integration.
367 words, about 2 min02Data Cleaning and Validation
Cleaning and validation turns raw, inconsistent data into a dataset that is complete enough, correctly typed, and rule-compliant for analysis. The exam tests missing-value handling, duplicates, type conversion, outlier treatment, and validation checks.
331 words, about 2 min03Data Exploration and Analysis with Pandas and NumPy
This domain covers exploratory analysis in pandas and NumPy: selecting and filtering, grouping and aggregating, reshaping, and vectorized computation. The exam checks whether you can move from a question to a correct, reproducible summary.
312 words, about 2 min04SQL for Querying and Manipulating Structured Data; Connecting Python to Databases
SQL for querying and manipulating structured data, plus connecting Python to a database. The exam tests SELECT, WHERE, GROUP BY, HAVING, and JOIN logic, aggregation, subqueries, and safe parameterized access from Python.
313 words, about 2 min05Statistical Methods: Descriptive and Inferential Statistics
Descriptive and inferential statistics: summarizing distributions, quantifying uncertainty, and testing claims. The exam checks whether you can choose the right statistic and interpret it correctly.
338 words, about 2 min06Foundational Machine Learning: Regression Analysis and Model Evaluation
Foundational regression and model evaluation: fitting a linear model, separating features and target, evaluating on held-out data, and interpreting error metrics. The exam emphasizes correct workflow over exotic algorithms.
314 words, about 2 min07Data Visualization and Communication of Insights
Visualization and communication: choosing a chart that matches the question, building it in Matplotlib or Seaborn, and labeling it so a reader can interpret it without your narration. Chart choice: line for trends over time, bar for comparisons across categories, histogram for distributions, scatter for relationships, box plot for spread by group.
302 words, about 2 min08Programming Practices: Object-Oriented Design and Exception Handling
Programming practices: object-oriented design and exception handling in data code. The exam checks whether your classes have clear responsibilities and whether failures are handled deliberately rather than swallowed.
323 words, about 2 min09Ethical Data Handling
Ethical data handling: collecting and using data lawfully, protecting individuals, and communicating limitations. The exam tests privacy principles, de-identification, consent, bias, and responsible sharing.
317 words, about 2 min