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CIMP Tracks

CIMP certification is currently offered in seven tracks:

  • Information Management Foundations (IMF)
  • Data Quality (DQ)
  • Data Governance (DG)
  • Master Data Management (MDM)
  • Data Modeling & Metadata Management (META)
  • Data Integration (DI)
  • Business Intelligence & Analytics (BA)

The table below maps our course catalog to the curricula for CIMP tracks. Each curriculum includes the fundamentals course (marked with letter 'F') and several core courses (marked with letter 'C').

To meet CIMP academic requirements in a track, you must complete five courses from its curriculum, including the fundamentals course and at least two core courses. 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).

To meet CIMP Ex academic requirements in a track, you must complete the fundamentals course and all core courses, and one elective course that can be chosen from the entire course list. For the IM Foundations track specifically, you must take the fundamentals course, plus at least two courses from each core area: data management, data integration, and information analytics.

See CIMP Rulebook for complete CIMP and CIMP Ex requirements beyond the coursework. To enroll in the program, choose one of our many CIMP Packages.




Courses (in alphabetical order) CIMP Track
IMF DQ DG MDM META DI BA
Analytics-based Enterprise Performance Management






Analytics Fundamentals C3




F
Best Practices in Data Resource Management






Big Data Fundamentals C3



C
C
Conceptual Data Modeling






Crafting the Business Case for Data Quality






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 Integration Fundamentals and Best Practices C2



C
Data Mining Concepts & Techniques





C
Data Mining in R






Data Parsing, Matching & De-duplication


C


Data Profiling
C

C2

Data Quality Assessment
C

C2

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



Data Quality Scorecard
C




Data Stewardship Fundamentals or Data Stewardship Core

C



Data Understanding and Preparation for Data Science






Data Virtualization


C
C
Data Warehousing Fundamentals C2



C
Diagnostic Analytics Using Statistical Process Control






DW and BI Data Modeling



C1

Ensuring Data Quality in Data Integration






Framing & Planning Data Science Projects






Fundamentals of Business Intelligence C3




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


F

Fundamentals of Predictive Analytics C3




C
Hadoop Fundamentals






How to Deploy and Sustain Data Governance

C



Information Management Fundamentals F
C



Introduction to NoSQL






Location Intelligence & GIS






Logical Data Modeling



C1

MDM Architecture and Implementation


C

MDM Fundamentals and Best Practice or MDM for Data Stewards C2

F


Organizing for Data Quality






Prescriptive Analytics Using Simulation Models






Putting the Science in Data Science: Fundamentals of Research Methods






Root Cause Analysis
C




The Data Model Scorecard






Web Analytics





C