Introduction

Agile methodologies, initially developed for software development, have become a popular approach for managing data science projects. Agile offers flexibility, continuous improvement, and collaboration, which are crucial in data science projects where requirements often evolve over time. This course focuses on applying Agile practices to data science workflows to enhance efficiency, communication, and deliverable quality.

Participants will learn how to incorporate Agile principles into data science teams and projects, including project planning, iterative development, collaboration, and delivery of high-quality models and analyses. The course will emphasize practical approaches, providing hands-on experience with Agile tools and techniques tailored for the data science domain.

Objectives

By the end of this course, participants will:

• Understand the core principles of Agile and how they apply to data science projects.
• Learn how to manage data science projects using Agile methodologies (Scrum, Kanban).
• Gain experience in Agile ceremonies, including sprint planning, daily standups, and retrospectives.
• Learn how to define and prioritize user stories, product backlogs, and epics in the context of data science projects.
• Develop skills for iterative model development, testing, and deployment.
• Explore Agile tools like Jira and Trello, used to manage tasks and projects.
• Gain insights into how Agile promotes collaboration and ensures continuous feedback in data science workflows.

Who Should Attend?

This course is ideal for:

• Data scientists, data engineers, and machine learning engineers working in Agile environments.
• Project managers and team leads responsible for managing data science teams and projects.
• Professionals interested in learning how to apply Agile methods to data science and machine learning.
• Anyone looking to understand how Agile methodologies can improve the efficiency and outcomes of data-driven projects.

Day 1: Introduction to Agile Methodologies for Data Science

Morning Session: Overview of Agile

• Introduction to Agile: Principles and practices.
• History and evolution of Agile methodologies (Scrum, Kanban, Lean).
• Core values and principles of Agile: Flexibility, collaboration, and iterative progress.
• Why Agile is effective for data science: Managing complexity, uncertainty, and evolving requirements.
• Overview of Agile roles in data science: Product Owner, Scrum Master, Development Team.

Afternoon Session: Agile in Data Science Context

• Key challenges in data science projects (uncertainty in data, evolving models, unclear end goals).
• How Agile addresses these challenges: Iterative development, rapid feedback, and flexibility.
• Transitioning from traditional project management methods to Agile for data science teams.
• The role of the Product Owner in defining and managing data science goals and user stories.
• Hands-on: Setting up an Agile workflow for a hypothetical data science project.

Day 2: Scrum Framework for Data Science Projects

Morning Session: Scrum Basics for Data Science

• The Scrum framework: Roles, ceremonies, and artifacts.
• Product Backlog: How to create and prioritize user stories for data science tasks (data wrangling, model training, feature engineering).
• Sprint Planning: Setting up objectives, estimating work, and breaking down tasks into manageable chunks.
• The Sprint: Timeboxing, goal-setting, and tracking progress.
• Scrum ceremonies: Sprint review, daily standups, and retrospectives.
• Hands-on: Writing user stories for a data science project and creating a product backlog.

Afternoon Session: Running a Scrum Sprint in Data Science Projects

• Organizing data science tasks into sprints (e.g., data collection, model development, performance evaluation).
• Estimating tasks in story points or hours: How to break down complex data science tasks.
• Managing dependencies in data science: Handling data availability, integration with other teams, and tool dependencies.
• Ensuring regular feedback and iteration: How to manage changes in models, data, and requirements.
• Hands-on: Running a simulated Scrum sprint with tasks and iterative development for a data science project.

Day 3: Kanban and Lean Practices in Data Science Projects

Morning Session: Introduction to Kanban for Data Science

• Understanding Kanban: Visualizing work, limiting work-in-progress (WIP), and ensuring smooth flow.
• The Kanban board: Setting up boards for data science tasks (e.g., “To Do,” “In Progress,” “In Review,” “Completed”).
• Managing workflows: Identifying bottlenecks, reducing task delays, and improving efficiency.
• Lean principles in data science: Continuous improvement, reducing waste, and maximizing value.
• Comparing Scrum and Kanban for data science teams: When to use each and their complementary roles.

Afternoon Session: Implementing Kanban in Data Science Projects

• Practical applications of Kanban in data science: Workflow management for data preprocessing, modeling, and evaluation.
• How to handle iterative model improvement, training data reprocessing, and feature engineering with Kanban.
• Managing capacity and WIP limits for the data science team: Optimizing flow without overloading resources.
• Hands-on: Setting up a Kanban board for a data science project and managing its tasks.

Day 4: Agile Tools for Managing Data Science Projects

Morning Session: Agile Tools for Data Science

• Overview of Agile tools for data science project management: Jira, Trello, Asana, Monday.com.
• Creating and managing user stories in Agile tools: Defining tasks and assigning priorities.
• Using Jira for tracking progress, managing sprints, and integrating with other tools (e.g., GitHub, GitLab for code versioning).
• Managing backlogs, sprints, and releases with Trello: Creating visual boards for data science teams.
• Hands-on: Setting up and managing a project in Jira for a data science sprint.

Afternoon Session: Collaboration and Feedback Loops in Agile Data Science

• The importance of regular feedback in Agile data science: Incorporating feedback from stakeholders and team members.
• Collaboration between data science and business teams: Ensuring the alignment of business goals and technical work.
• Using Agile ceremonies (daily standups, retrospectives) for continuous improvement.
• Monitoring and reporting progress: Creating and tracking key performance indicators (KPIs) for data science projects.
• Hands-on: Using Jira or Trello for task tracking and collaborating with team members on a data science project.

Day 5: Advanced Agile Practices and Best Practices in Data Science Projects

Morning Session: Scaling Agile for Larger Data Science Teams

• Scaling Agile in data science projects: SAFe (Scaled Agile Framework) and LeSS (Large Scale Scrum) for large teams.
• Managing cross-functional teams: Ensuring communication between data scientists, engineers, product managers, and other stakeholders.
• Integrating Agile with DevOps and continuous integration (CI/CD) pipelines for faster deployments.
• Building Agile data pipelines for automated, iterative delivery of machine learning models and analytics.
• Case study: Applying Agile to a large-scale machine learning project.

Afternoon Session: Best Practices and Overcoming Common Pitfalls

• Best practices for managing Agile data science teams: Communication, flexibility, and continuous improvement.
• Common pitfalls in Agile data science projects and how to avoid them (e.g., scope creep, unmanageable backlogs).
• Case studies of successful Agile data science projects and lessons learned.
• Q&A and open discussion on real-world challenges and how to apply Agile methods in various data science environments.

Materials and Tools:

• Software: Jira, Trello, Asana, Google Sheets, GitHub/GitLab for collaboration and version control.
• Datasets: Sample data science tasks for hands-on activities (e.g., model development, feature engineering, data wrangling).
• Recommended Reading: “Agile Data Science” by Russell Jurney and “Lean Analytics” by Alistair Croll.

Post-Course Support:

• Access to course materials, recorded sessions, and community forums for ongoing learning.
• Practical exercises and a final project focused on building an Agile data science workflow.
• Continuing support through expert Q&A sessions and further resources for expanding Agile practices in data science.