Introduction to Data Science
(MATH08077)

This is the website for the Introduction to Data Science (MATH08077) course offered at the University of Edinburgh for the academic year 2024/5.

Learn to explore, visualize, and analyse data to understand natural phenomena, investigate patterns, model outcomes, and make predictions, and do so in a reproducible and shareable manner. Gain experience in data collection, wrangling, and visualization, exploratory data analysis, predictive modelling, and effective communication of results while working on problems and case studies inspired by and based on real-world questions. The course will focus on the R statistical computing language. No statistical or computing background is necessary. Additional official course information can be found here.

1. Welcome to IDS

Week 1: Get acquainted with the course, the technology, the workflow, and the skills you will acquire throughout the semester.

2. Wrangling and Tidying Data

Week 2: Data wrangling, joining, and tidying.

3. Importing and Joining Data

Week 3: Importing data, data types and classes, recoding.

4. Uncertainty quantification

Week 4: Uncertainty quantification and hypothesis testing with bootstrap.

5. Doing Data Science & Visualizing Data

Week 5: Tips for effective communication of results, and collaboration. Intro to Data Visualization.

6. Visualizing Data

Week 6: Data visualization (part 2), interpretation of graphical information and tips for effective data visualization.

7. Ethics

Week 7: Misrepresentation of findings, data privacy, and algorithmic bias.

8. Modelling Data

Week 8: Linear models for predicting numerical data from single and multiple variables.

9. Classification and Model Building

Week 9: Logistic regression for predicting categorical data and model building.

10. Model Validation

Week 10: Evaluating models with cross validation and further topics in modeling.

11. Looking Beyond IDS

Week 11: Additional topics beyond IDS

Course Components

Information on the various components of the course.

Assessments

Information on the assessments for the course.

Announcements

Updates with important information will be posted here as well as emailed to your university email address.

Errata

A list of significant corrections and clarifications to material on the course.

Help

A list of all the ways you can get support for this course.

People

Details and contact information for staff associated with the course.

Policies

Details on the course & university expectations for students and staff.

Resources

A list of books, tools, and cheetsheets that will come in handy while taking the course.

Setup & Troubleshooting

Instructions to setup the toolkit for the course and fix common issues.

This online work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International licence (visit here for more information). These materials have been adapted from Data Science in a Box by Mine Çetinkaya-Rundel, which is under the same licence.