Foundations of Data Science Course Syllabus

Overview (80-120 words) describing structure and time commitment.

Module 1: Introduction to Data Science Concepts

Estimated time: 2 hours

• Course orientation and learning objectives
• History and evolution of data science
• Definitions and core terminology
• Real-world use cases and industry applications

Module 2: The Impact of Data Today

Estimated time: 3 hours

• Data roles in organizations: data scientists, analysts, and engineers
• How businesses leverage data for decision-making
• Ethical considerations in data collection and usage
• Key skills for success in data-driven environments

Module 3: Your Career as a Data Professional

Estimated time: 2 hours

• Core competencies and skill sets for data roles
• Collaboration strategies in data teams
• Using AI assistants like Gemini for data tasks
• Tools and platforms for teamwork and productivity

Module 4: Data Applications & Workflow

Estimated time: 4 hours

• Introduction to the PACE framework: Plan, Analyze, Construct, Execute
• Planning and scoping analytics projects
• Stakeholder identification and communication strategies
• Creating project roadmaps and deliverables

Module 5: Capstone Project

Estimated time: 3 hours

• Applying the PACE framework to a mini business intelligence project
• Developing a project plan and stakeholder documentation
• Designing communication materials for project outcomes

Prerequisites

• Familiarity with basic data analysis concepts
• Completion of Google Data Analytics Certificate or equivalent experience
• Basic understanding of data workflows and reporting

What You'll Be Able to Do After

• Explain the role and impact of data science in modern organizations
• Identify key tools and methodologies used by data professionals
• Map career pathways in data science and analytics
• Apply the PACE framework to structure real-world data projects
• Create clear project plans and stakeholder communications for data initiatives