Introduction

Data science projects often involve complex datasets, advanced algorithms, and cross-functional teams. Managing such projects effectively requires not just technical expertise but also a solid understanding of project management methodologies and best practices. This course is designed to equip data scientists and project managers with the tools and techniques needed to successfully lead and manage data science projects.

Participants will gain insights into scoping, planning, executing, and delivering data science projects while navigating challenges like data quality, team collaboration, and stakeholder management. The course will also cover frameworks like Agile, Scrum, and Kanban, tailored to the unique needs of data science initiatives.

Course Objectives

By the end of this course, participants will be able to:

• Understand the project management lifecycle for data science projects.
• Scope, plan, and prioritize data science projects using Agile methodologies.
• Manage project timelines, resources, and risks in data science projects.
• Collaborate effectively with cross-functional teams (data scientists, engineers, business stakeholders).
• Communicate project progress and results to stakeholders and leadership.
• Apply project management tools like Jira, Trello, and Asana to manage tasks and track progress.
• Overcome common challenges in data science projects, such as data quality issues, model interpretability, and changing business requirements.

Who Should Attend?

This course is ideal for:

• Data scientists who wish to develop project management skills.
• Data science team leads and project managers in charge of data-driven projects.
• Business analysts and product managers working in data-driven organizations.
• Data engineers collaborating with data science teams and managing project timelines.
• Professionals seeking to build stronger management skills in data science contexts.

Day-by-Day Course Breakdown

Day 1: Introduction to Project Management for Data Science

Overview of Data Science Projects

• Key differences between traditional IT projects and data science projects.
• The importance of scoping and defining data science projects clearly.
• Overview of the data science lifecycle: problem definition, data collection, data preparation, modeling, and deployment.

Project Management Methodologies for Data Science

• Introduction to Agile and Scrum in data science projects.
• Waterfall vs. Agile: Why Agile is more suited for data science.
• Overview of Kanban and its use in managing workflows.
• Hands-on exercise: Mapping out a data science project lifecycle using Agile methodology.

Day 2: Planning and Scoping Data Science Projects

Scoping and Defining the Project

• Setting clear objectives and defining key results (OKRs) for data science projects.
• How to translate business problems into data science problems.
• Defining data requirements, project goals, and KPIs.
• Hands-on exercise: Defining the scope for a sample data science project.

Planning the Project

• How to break down a data science project into manageable tasks.
• Creating project timelines and setting realistic milestones.
• Resource planning: team, budget, and tools/resources required.
• Hands-on exercise: Creating a project plan with tasks, deadlines, and resources.

Day 3: Managing Teams and Resources in Data Science Projects

Collaborating with Cross-Functional Teams

• Understanding the roles of data scientists, engineers, and business stakeholders.
• Facilitating cross-functional collaboration to ensure alignment.
• Managing expectations and balancing technical feasibility with business needs.
• Hands-on exercise: Creating communication plans for cross-functional teams.

Managing Resources and Risks

• Identifying and managing resources (data, tools, personnel).
• Risk management: Identifying common risks in data science projects (data quality issues, model complexity, etc.).
• Strategies for mitigating risks and ensuring timely delivery.
• Hands-on exercise: Risk assessment for a hypothetical data science project.

Day 4: Monitoring, Reporting, and Communicating Progress

Tracking Progress with Project Management Tools

• Overview of project management tools like Jira, Trello, and Asana.
• How to track progress, assign tasks, and manage project backlogs.
• Hands-on lab: Setting up a project management board on Jira or Trello.

Communicating Progress to Stakeholders

• How to provide regular status updates to stakeholders and leadership.
• Managing expectations and ensuring transparency throughout the project lifecycle.
• Reporting on project metrics and KPIs.
• Hands-on exercise: Creating a status report for project stakeholders.

Day 5: Delivering Data Science Projects and Post-Project Review

Finalizing the Data Science Project

• Ensuring successful project completion and meeting project goals.
• Tips for post-project evaluation: gathering feedback from stakeholders and team members.
• How to document results for future reference and learning.
• Hands-on exercise: Preparing a final project deliverable and presentation.

Capstone Project: Managing a Data Science Project

• Participants will be given a mock data science project and will manage it from planning to execution.
• The project will include tasks like scoping, risk management, collaboration, progress reporting, and final delivery.
• Final presentations and feedback from instructors.

Conclusion & Certification

At the end of the training, participants will receive a Certificate of Completion, confirming their ability to manage data science projects from start to finish using industry-standard project management techniques.

This course blends theory, practical examples, and real-world case studies to ensure that participants are well-prepared to lead data science initiatives in their organizations.