Outline

Part 1: Review of Agile Practices

1. Project management as a component of enterprise work
   1. History of project management in other disciplines
   2. Gantt charts, critical paths, and mapping out dependency
2. Pre-agile methods in software work
   1. Waterfall
   2. Spiral model
3. Why Agile?
   1. Review agile manifesto
   2. Review flavors of agile: Kanban, Scrum, XP

Exercise 1: Write 10 user stories for an example project.

Exercise 2: Write a comparison of Kanban and Scrum and which might be the best fit for your needs.

Part 2: The Data Science Practice: Process & Assumptions

1. Data science workflow intro
2. OSEMN
   1. Obtain data
   2. Scrub data
   3. Explore data
   4. Model data
   5. Interpret data
3. The tasks and their roles: breaking down the process
   1. Data engineers
      1. ETLs, data warehouses, and integrations
   2. Data scientist
      1. Analysis, research, and model development
   3. Product manager
      1. Feature and metric development

Exercise: Using the OSEMN process, break down developing a model that predicts which video to post next. Think about how the data system that you would be pulling from would look, what kind of data system you would be loading into, how you would implement the model, etc.

Part 3: Data Science and Agile

1. Introduction to data science and agile
   1. How agile works with data science
   2. Why agile works well with data science
2. Applying agile concepts to the data science process
   1. OSEMN, scrum, and Kanban
   2. How to prioritize work in data science
   3. Benefits of agile and data science
   4. Challenges of agile and data science
3. Data science and agile: roles
4. Example project breakdown
   1. Walking through fraud detection model development
      1. Task breakdown example
      2. Task dependency management

Exercise: Using what you have learned about data science and agile methodologies, break down a project to develop a video rating system and dashboard.

Part 4: Adopting Scrum in Data Science Teams

1. Managing data science teams with agile
   1. Implementing agile methods into a new team
   2. The value of constantly reviewing and feedback
   3. Setting clear expectations for the team
2. Involving stakeholders
   1. Communication with stakeholders
   2. Feature/scope creep
   3. Managing expectations
3. Common mistakes with agile

Exercise: Agile and data science writing prompt.

Part 5: Agile engineering for technical integration

1. Versioning
   1. Database versioning
   2. Model versioning
2. CI/CD
   1. Continuous integration: conceptual overview
   2. Integrating with enterprise applications
   3. Testing priorities
   4. Opportunities for automation
3. Application integration
   1. Software team considerations
   2. Data science as a service
   3. Product-oriented pipelines
   4. Rapid feedback
4. Tools for integration
   1. DVC
   2. Git
   3. EMF Store
   4. ODBC
   5. DAX and PowerBI

Part 6: Review

1. What is agile, and why do we use it?
   1. Review Kanban, scrum, and XP
2. Implementing agile in your data science process
   1. How can agile benefit data science?
   2. Where does agile not align with data science?
3. The importance of aligning data science teams and stakeholders with agile methods

Part 7: Bonus discussion: Agile In-Depth

1. Scrum
   1. Terms, concepts, and breakdown
   2. Pros and cons
2. Kanban
   1. Terms, concepts, and breakdown
   2. Pros and cons
3. XP
   1. Terms, concepts, and breakdown
   2. Pros and cons
4. Agile Tools
   1. Kanbanflow
   2. JIRA
   3. TDSP
5. Walkthrough
   1. Task breakdown for developing a Twitter tweet

Exercise: Using what you have learned about agile methods, use the scrum method to plan out developing the Udemy search feature, as well as the web pages the search feature returns.