Agile Data Science Boot Camp

Agile practices have been used in software for years. They have helped engineering teams complete complex software projects and improve communication between engineers and stakeholders. Agile isn’t limited to software development – it’s being implemented everywhere, and most enterprise data practices are areas of need. In this fast-paced world, adapting quickly is key. Data science teams can greatly benefit from the improved flexibility, collaboration and output available from adopting agile concepts and practices. This data science course will provide you with the tools required to leverage agility to break down complex requests from management, provide more accurate timelines, and communicate the overall status of your projects to management and other stakeholders.

Retail Price: $1,995.00

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Course Days: 3

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In This Agile Data Science Course, You Will:

  • Review classic agile practices like Scrum, XP, and Kanban
  • Learn how agile techniques can be applied to data science projects
  • Learn how managing software projects and data science projects differ
  • Learn how stories, sprints, and agile ceremonies and artifacts can be applied to data models and application integration
  • Discover a toolbox of operational enablers drawn from the world of agile engineerings, such as database versioning, continuous integration, and test automation.
  • Examine agile roles and how they can be applied in a data or advanced analytics practice
  • Work using agile principles to address needs with faster, more nimble responses.



This agile data science boot camp is designed specifically to teach new or established data science teams proven agile practices and techniques that will breakdown complex data science work and increase project success. Some professions that will find this course beneficial include: 

  • Data Scientists
  • Data Engineers
  • Machine Learning Engineers
  • DBAs
  • Statisticians
  • Data Analysts
  • Data Visualization Specialists
  • Business Intelligence Specialists
  • Software Developers
  • Scrum Masters
  • Product Managers and Project Managers working on data science projects


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.

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