A number of recent reports refer to a new role of Citizen Data Scientist. This role was defined by recent Gartner report as a power user who “creates or generates models that leverage predictive or prescriptive analytics but whose primary job function is outside of the field of statistics and analytics”. Citizen Data Scientists need the right technology and methodology to drive the most value from data.
The Cross Industry Standard Process for Data Mining or CRISP-DM is the "de facto” standard for developing data mining and predictive analytics projects. This flexible and robust data mining process model describes frequently used methods utilized by the contemporary data scientist. This course will present a practical overview of the powerful, flexible and useful ways to utilize CRISP-DM to address data driven business problems. In this session, we will learn about the six main stages of the CRISP-DM process as applied to a practical use case. We will examine each phase in detail, and examine the most important and frequent dependencies between phases from a citizen data scientist perspective.
You Will Learn
- Cross Industry Standard Process for Data Mining methodology
- Six steps in the CRISP-DM Process
- Key steps in assessing and understanding Data Science Process
- How to express business and technical data science objectives
- Resources, skills, and plans that you can take with you to apply to your next data science project
- Citizen data scientist, business analysis, data analyst, analytics practitioners; data scientists; IT professionals; analytics project leaders, technical managers, scientist, and engineers