The Data Scientist’s Toolbox Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a structured introduction to the essential tools and concepts used in data science, designed for beginners. You'll gain hands-on experience with R, RStudio, Git, and GitHub, while learning foundational practices in reproducible research and version control. The course spans approximately 16 hours of content and activities, divided into four core modules and a final project, enabling learners to build a solid foundation for a career in data science.
Module 1: Data Science Fundamentals
Estimated time: 4 hours
- Introduction to data science and its significance
- Understanding different types of data
- Exploring the data science process
- Identifying resources for assistance and learning
Module 2: R and RStudio
Estimated time: 4 hours
- Installing R and RStudio
- Navigating the RStudio interface
- Managing R packages
- Working with projects in R
Module 3: Version Control and GitHub
Estimated time: 3 hours
- Understanding version control systems
- Installing and configuring Git
- Creating and managing GitHub repositories
- Collaborating using Git and GitHub
Module 4: R Markdown, Scientific Thinking, and Big Data
Estimated time: 5 hours
- Introduction to reproducible research principles
- Creating dynamic documents with R Markdown
- Integrating code and narrative
- Publishing and sharing reproducible reports
Module 5: Final Project
Estimated time: 4 hours
- Set up R, RStudio, and Git environments
- Create a GitHub repository for version control
- Develop a reproducible report using R Markdown
Prerequisites
- Basic computer literacy
- No prior programming experience required
- Access to a computer with internet connection for software installation
What You'll Be Able to Do After
- Set up and navigate the R and RStudio environment
- Install and configure Git and GitHub for version control
- Create and manage repositories for collaborative projects
- Produce reproducible data analysis reports using R Markdown
- Apply foundational data science concepts and workflows in practical settings