The Sawyer Business School’s Data Analytics Academic Micro-Credential
In this academic course titled ISOM 631, students learn the fundamentals of data analytics using the CRISP-DM (Cross Industry Standard Process for Data Mining) processing model across six steps: (1) Understanding the Business Problem, 2) Determining the data required to address the problem 3) Preparing the data 4) Modeling the data for analysis, 5) Evaluating the outcomes of the model, 6) implementing the solution considering stakeholder access. The step is iterative to enable adaption.
- Type Validation
- Level Intermediate
- Time Months
- Cost Paid
Earning Criteria
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To achieve this high level of business understanding of data analytics, the students must be able to start an independent analytics project with minimal guidance. They accomplish this by studying many examples of data analytics concepts during class time, which are reinforced with homework and assessments. The final project is another opportunity to start an independent analytics project with a new business problem and dataset.