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CIMP Tracks

To meet CIMP academic requirements in a track (see table next page), you must complete five courses from its curriculum, including the fundamentals course (F) and at least two core courses (C). For META track, one of the completed core courses must be a data modeling course (C1) and one must be a metadata management course (C2). For IMF track, you must complete the fundamentals course plus at least one course from each core area: data management (C1), data integration (C2), and information analytics (C3).




Courses (in alphabetical order) CIMP Track
IMF DQ DG MDM META BIA SC
Analytical Modeling, Evaluation, and Deployment Best Practices






Analytics-based Enterprise Performance Management






Analytics Fundamentals C3



F

Artificial Intelligence Fundamentals





C
Big Data Fundamentals C2





Conceptual Data Modeling






Crafting the Business Case for Data Quality






Curating and Cataloging Data






Data Governance for Business Leaders






Data Governance Fundamentals or Data Governance for Data Stewards C1
F C


Data Integration Techniques for Designing an ODS






Data Mining Concepts & Techniques




C
C
Data Mining in R






Data Parsing, Matching & De-duplication


C


Data Privacy and Protection Fundamentals

C



Data Profiling
C

C2

Data Quality Assessment
C

C2

Data Quality Fundamentals or Data Quality for Data Stewards C1 F




Data Quality Scorecard
C




Data Science Fundamentals C3




F
Data Stewardship Fundamentals or Data Stewardship Core

C



Data Strategy for the Age of Big Data






Data Understanding and Preparation for Data Science





C
Data Virtualization






Data Visualization and Storytelling




C
Data Warehousing Fundamentals C2





Diagnostic Analytics Using Statistical Process Control






DW and BI Data Modeling



C1

Ensuring Data Quality in Data Integration






Framing & Planning Data Science Projects





C
Fundamentals of Business Intelligence C3



C

Fundamentals of Data Modeling and Metadata Management or
Metadata Management for Data Stewards
C1

C F

Fundamentals of Predictive Analytics C3



C

Hadoop Fundamentals






How to Deploy Data Governance: Part 1

C



How to Deploy Data Governance: Part 2

C



Information Management Fundamentals F





Introduction to Graph Databases






Introduction to NoSQL






Knowledge Graph Architecture for the Enterprise






Location Intelligence & GIS






Logical Data Modeling



C1

MDM Fundamentals: Architecture and Implementation or MDM for Data Stewards C2

F


Modernizing Data Governance






Organizing for Data Quality






Prescriptive Analytics Using Simulation Models






Putting the Science in Data Science: Fundamentals of Research Methods






Root Cause Analysis
C




Streaming Data: Concepts, Applications, & Technologies






The Data Model Scorecard






Web Analytics




C