care_and_feeding.md

Care and Feeding of a Data Science Practice

1. Running a Project

• Finding them
• Defining them
• Doing them
• Delivering them

2. Teams and Culture

• Deciding what you need
• Finding what you need
• Growing it organic
• Keeping it sustained

3. Tools for Data Science

• The essentials
• The open source tool box
• Cloud and vendor platforms
• Managing code and notebooks

4. The Management and Delivery of Data

• The components of a data pipeline
• Roll your own with containers and IaaS
• Doing it with PaaS (and how it can differ by vendor)
• Operationalizing with MLOps
• Where to store it: Data Warehouse vs Data Lake vs Data Lakehouse
• Building a governance model around Master Data Management

5. Communicating (and Influencing)

• Grooming your stakeholder for Data Science
• The what, why, and how of the product pitch
• Before the research question: What is the value proposition and how will you measure success?
• The key topics to address in the stakeholder review meeting
• Let’s get visual: Ways to deliver results of your data analysis
• When to pivot and how to deliver the message