Move From Understanding the Past to Predicting What Comes Next
Data science combines statistics, computing, mathematics, domain expertise, and analytical reasoning to discover patterns and build models that support prediction, classification, recommendation, forecasting, and optimization.
Select a center to explore guidance, standards and practitioner resources
Data science combines statistics, computing, mathematics, domain expertise, and analytical reasoning to discover patterns and build models that support prediction, classification, recommendation, forecasting, and optimization.
The IBACTP® Data Science Insights Center helps professionals move from conventional business analytics toward advanced analytical and AI-driven methods.
A professional data-science lifecycle may include:
Important concepts include:
Models learn from labeled examples.
Applications include:
Predict a category.
Examples:
Predict a numeric value.
Examples:
Identify structure without predetermined labels.
Applications include:
Professionals may encounter:
The model is too simple to capture meaningful patterns.
The model learns the training data too closely and performs poorly on new observations.
Professional modeling requires balancing complexity and generalization.
Feature engineering can involve:
Better features can sometimes improve a model more than selecting a more complex algorithm.
Organizations use controlled experiments to evaluate whether interventions cause measurable changes.
Examples include:
Used when observations are ordered through time.
Applications include:
Concepts may include:
Artificial intelligence is increasingly changing how professionals interact with data.
Emerging capabilities include:
AI should augment analytical judgment—not replace the need to validate data, assumptions, methodology, and results.
Professional data science must address:
Pair these resources with an IBACTP® credential that validates the competence they describe.
