Introduction

Welcome to the 5-day professional course "Data Science Project Lifecycle and Management." This course is designed to provide participants with comprehensive knowledge and skills necessary to manage data science projects effectively from inception to completion. Participants will learn about project planning, execution, monitoring, and optimization in data science applications across various industries.

Objectives

• Understand the end-to-end lifecycle of data science projects.
• Learn best practices for managing data science teams and resources.
• Gain proficiency in tools and techniques for project planning and execution.
• Develop strategies for addressing challenges and risks in data science projects.
• Improve communication and reporting skills for data science project management.

Course Outlines

Day 1: Introduction to Data Science Project Lifecycle

• Overview of Data Science and its impact on industries
• The standard phases of a data science project lifecycle
• Roles and responsibilities in a data science team
• Understanding project objectives and requirements
• Introduction to project management methodologies

Day 2: Planning and Initiating Data Science Projects

• Defining the project scope and deliverables
• Resource allocation and budgeting techniques
• Tools for project planning and scheduling
• Establishing a communication plan
• Risk management and mitigation strategies

Day 3: Executing Data Science Projects

• Data collection and preprocessing techniques
• Integration of data engineering and analysis
• Leveraging machine learning models and algorithms
• Management of project timelines and tasks
• Ensuring data quality and validation

Day 4: Monitoring and Controlling Project Progress

• Tracking project performance using KPIs and metrics
• Techniques for project adjustments and agile iterations
• Documentation and change management processes
• Tools for collaboration and progress monitoring
• Handling conflicts and team dynamics

Day 5: Project Closure and Optimization

• Conducting project reviews and post-mortems
• Presentation and communication of project outcomes
• Lessons learned and best practice documentation
• Ensuring knowledge transfer and continuous improvement
• Project handover and stakeholder engagement