Prerequisite: None
Data Science projects are not typical business or IT projects – these projects start with a business problem or opportunity to be explored and result in gaining new insights as well as producing analytical models – meaning data science projects have different deliverables, pitfalls, and challenges. Attempting to deliver a data science project with traditional IT project methodologies contributes to the high rate of data science project failures. The Cross-industry standard process for data mining, otherwise known as CRISP-DM is the accepted methodology for data science projects and addresses the success factors required for data science projects. This course provides an introduction to data science methodology and highlights pitfalls to avoid and best practices to leverage.
You Will Learn
- How to clearly identify the problem or opportunity statement which frames the rest of the project
- What modeling techniques align with the problem or opportunity statement
- How to plan for the data understanding phase of the project
- What techniques are used in data understanding
- How the model and its features drive data preparation
- What the model training process is and how to manage expectations
- How models are evaluated
- How to plan for deployment and maintenance of the model
Geared To
- New data scientists
- Project sponsors
- Project managers
- Business analysts