Tools for Data Science Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This beginner-friendly course introduces learners to essential open-source tools used across the data science workflow. Designed for those new to the field, it provides hands-on experience with Jupyter Notebooks, RStudio, GitHub, and IBM Watson Studio. The course spans approximately 5 weeks with a weekly commitment of 2–3 hours, combining conceptual understanding with practical exercises to build foundational tool proficiency. Each module includes interactive labs to reinforce learning and prepare learners for real-world data tasks.
Module 1: Introduction to Open Source Tools
Estimated time: 3 hours
- Overview of data science tools and environments
- Understanding the open source philosophy
- Exploring the data science ecosystem
- Identifying common open-source tools in data science
Module 2: Jupyter Notebooks and JupyterLab
Estimated time: 3 hours
- Introduction to Jupyter Notebook interface
- Running code cells and markdown cells
- Managing notebook outputs and kernels
- Using JupyterLab for enhanced workflows
Module 3: RStudio and GitHub
Estimated time: 3 hours
- Getting started with RStudio IDE
- Writing and executing R scripts
- Introduction to Git and version control
- Using GitHub: cloning, committing, and pushing repositories
Module 4: IBM Watson Studio
Estimated time: 3 hours
- Introduction to IBM Cloud and Watson Studio
- Setting up a Watson Studio project
- Uploading and managing data assets
- Running basic data tasks in the cloud environment
Module 5: Final Assignment
Estimated time: 4 hours
- Integrating Jupyter Notebooks into a project
- Using RStudio for data analysis components
- Incorporating GitHub for version control
Module 6: Final Project
Estimated time: 6 hours
- Deliverable 1: Create a data science project using Watson Studio
- Deliverable 2: Include Jupyter Notebook and R scripts in the workflow
- Deliverable 3: Document and share work using GitHub
Prerequisites
- Basic computer literacy
- Familiarity with web browsers and file navigation
- No prior programming experience required
What You'll Be Able to Do After
- Identify and describe key open-source data science tools
- Use Jupyter Notebooks for basic code execution and documentation
- Write and run R scripts in RStudio
- Apply Git and GitHub for version control of data projects
- Build and manage a data science project in IBM Watson Studio