This directory collects materials from the Johns Hopkins Data Science Specialization on Coursera, together with my own progress, quizzes, projects, and completion certificates.
The structure here combines:
- Original course materials from the official JHU Data Science Specialization GitHub repository
- My personal work including weekly assignments, quizzes, and programming projects
- PDF certificates for each completed course
- Supplementary reading material and notes
Introduction to data science, R, RStudio, Git, and GitHub.
- 📁 Course Contents | Lectures
- 📝 Week 1 | Week 2 | Week 3
- 🎓 Certificate
Fundamentals of R programming, data types, control structures, and functions.
- 📁 Course Contents | Lectures
- 📝 Week 1 | Week 2 | Week 3 | Week 4
- 💻 Project 1 | Project 2 | Project 3
- 🎓 Certificate
Obtaining data from various sources and preparing it for analysis.
- 📁 Course Contents
- 📝 Week 1 | Week 2 | Week 3 | Week 4
- 💻 Programming Assignment
- 🎓 Certificate
Techniques for exploring and visualizing data.
Creating reproducible data analysis using R Markdown and knitr.
- 📁 Course Contents
- 📝 Week 1 | Week 2
- 💻 Programming 1 | Programming 2
- 🎓 Certificate
Probability, statistical inference, hypothesis testing, and confidence intervals.
- 📁 Course Contents | Lectures
- 📝 Week 1 | Week 2 | Week 3 | Week 4
- 💻 Final Project
- 🎓 Certificate
Linear and generalized linear models for prediction and inference.
- 📁 Course Contents
- 📝 Week 1 | Week 2 | Week 3 | Week 4
- 💻 Final Project
Machine learning algorithms and practical applications using the caret package.
- 📁 Course Contents
- 📝 Week 1 | Week 2 | Week 3 | Week 4
- 💻 Final Project
- 🎓 Certificate
Creating interactive data visualizations and web applications using Shiny, R Markdown, and other tools.
- 📁 Course Contents
- 📝 Week 1 | Week 2 | Week 3
- 💻 Final Project
All course completion certificates are available in the certificates folder:
| Course | Certificate |
|---|---|
| Data Scientist's Toolbox | 📄 View |
| R Programming | 📄 View |
| Getting and Cleaning Data | 📄 View |
| Reproducible Research | 📄 View |
| Statistical Inference | 📄 View |
| Practical Machine Learning | 📄 View |
The miscellaneous folder contains supplementary books and materials used during the specialization:
- "The Art of Data Science" by Roger Peng & Elizabeth Matsui
- "Machine Learning with R" by Brett Lantz
- "Exploratory Data Analysis with R" by Roger Peng
- "Statistical Inference for Data Science" by Brian Caffo
- "Regression Models for Data Science in R" by Brian Caffo
- "Developing Data Products in R" by Brian Caffo
- "The Elements of Data Analytic Style" by Jeff Leek
- And more...
Each course folder typically contains:
contents/– Original course materials from the JHU repositorylectures/– PDF lecture slidesweek1/,week2/, etc. – My weekly work, quizzes, and assignmentsproject/,programming/,final project/– Course projects and deliverablesswirl/– Interactive Swirl programming exercises (where applicable)
The original course materials are from the Johns Hopkins Data Science Specialization on Coursera.
Original contributors:
- Brian Caffo
- Jeff Leek
- Roger Peng
- Nick Carchedi
- Sean Kross
The course materials are available under the Creative Commons Attribution NonCommercial ShareAlike (CC BY-NC-SA) license.
My own additions (quizzes, solutions, code, notes, and collected certificates) are layered on top of the original content and are intended for personal study and reference, respecting the same non-commercial, share-alike spirit.
Note: This directory is not an official JHU or Coursera repository. It is a personal learning archive combining official course content and my work from completing the specialization.
To explore the materials:
- Navigate to any course folder to see the content
- Check the
week*folders for my solutions and notes - Review project folders for final assignments
- View certificates in the
certificates/directory
Feel free to use these materials for your own learning, keeping in mind the CC BY-NC-SA license terms.