Schedule
There are a lot of materials in Data Science Course in a Box, which allows instructors to pick and choose what they want depending on the length of the course they’re teaching, their audience, and the curriculum within which the course is placed. The following are two options for course schedules, one for a 11-week course and the other for a 15-week course.
11-week schedule
| Unit | Week | Title | Type |
|---|---|---|---|
| 1 | 1 | Welcome to data science! | Lecture |
| 1 | 1 | Meet the toolkit: Programming | Lecture |
| 1 | 1 | Meet the toolkit: Version control & collaboration | Lecture |
| 1 | 1 | Hello R | Lab |
| 1 | 1 | Pet names | Homework |
| 2 | 2 | Data and visualisation | Lecture |
| 2 | 2 | Visualising data with ggplot2 | Lecture |
| 2 | 2 | Visualising numerical data | Lecture |
| 2 | 2 | Visualising categorical data | Lecture |
| 2 | 2 | StarWars + Dataviz | Application exercise |
| 2 | 2 | Plastic waste | Lab |
| 2 | 2 | Airbnb listings in Edinburgh | Homework |
| 2 | 3 | Tidy data | Lecture |
| 2 | 3 | Grammar of data wrangling | Lecture |
| 2 | 3 | Working with a single data frame | Lecture |
| 2 | 3 | Working with multiple data frames | Lecture |
| 2 | 3 | Tidying data | Lecture |
| 2 | 3 | Hotels + Data wrangling | Application exercise |
| 2 | 3 | Nobel laureates | Lab |
| 2 | 3 | Road traffic accidents | Homework |
| 2 | 4 | Data types | Lecture |
| 2 | 4 | Data classes | Lecture |
| 2 | 4 | Importing data | Lecture |
| 2 | 4 | Recoding data | Lecture |
| 2 | 4 | Hotels + Data types | Application exercise |
| 2 | 4 | Nobels + Sales + Data import | Application exercise |
| 2 | 4 | Option 1: La Quinta is Spanish for next to Denny’s, Pt. 1 Option 2: La Quinta is Spanish for next to Denny’s, Pt. 2 |
Lab |
| 2 | 4 | College majors | Homework |
| 2 | 5 | Tips for effective data visualization | Lecture |
| 2 | 5 | Brexit + Telling stories with dataviz | Application exercise |
| 2 | 5 | Scientific studies and confounding | Lecture |
| 2 | 5 | Simpson’s paradox | Lecture |
| 2 | 5 | Doing data science | Lecture |
| 2 | 5 | Option 1: Take a sad plot and make it better Option 2: Simpson’s paradox |
Lab |
| 2 | 5 | Legos | Homework |
| 2 | 6 | Web scraping | Lecture |
| 2 | 6 | Scraping top 250 movies on IMDB | Lecture |
| 2 | 6 | Web scraping considerations | Lecture |
| 2 | 6 | IMDB + Web scraping | Application exercise |
| 2 | 6 | Functions | Lecture |
| 2 | 6 | Iteration | Lecture |
| 2 | 6 | University of Edinburgh Art Collection | Lab |
| 2 | 6 | Money in politics | Homework |
| 3 | 7 | Misrepresentation | Lecture |
| 3 | 7 | Data privacy | Lecture |
| 3 | 7 | Algorithmic bias | Lecture |
| 3 | 7 | Conveying the right message through visualisation | Lab |
| 3 | 7 | Project proposals | Project |
| 4 | 8 | Fitting and interpreting models | Lecture |
| 4 | 8 | Modelling nonlinear relationships | Lecture |
| 4 | 8 | Models with multiple predictors | Lecture |
| 4 | 8 | More models with multiple predictors | Lecture |
| 4 | 8 | Grading the professor, Pt 1 | Lab |
| 4 | 8 | Option 1: Bike rentals in DC Option 2: Peer review of project proposals |
Homework |
| 4 | 9 | Logistic regression | Lecture |
| 4 | 9 | Prediction and overfitting | Lecture |
| 4 | 9 | Feature engineering | Lecture |
| 4 | 9 | Grading the professor, Pt 1 | Lab |
| 4 | 9 | Exploring the GSS | Homework |
| 4 | 10 | Cross validation | Lecture |
| 4 | 10 | The Office, Part 1 | Application exercise |
| 4 | 10 | The Office, Part 2 | Application exercise |
| 4 | 10 | Quantifying uncertainty | Lecture |
| 4 | 10 | Bootstrapping | Lecture |
| 4 | 10 | Option 1: Smoking during pregnancy Option 2: Work on projects Option 3: Collaboration on GitHub |
Lab |
| 4 | 10 | Modelling the GSS | Homework |
| 5 | 11 | Text analysis | Lecture |
| 5 | 11 | Comparing texts | Lecture |
| 5 | 11 | Interactive web apps | Lecture |
| 5 | 11 | Machine learning | Lecture |
| 5 | 11 | Project presentations | Lab |
| 5 | 11 | Wrap up | Homework |
15-week schedule
| Unit | Week | Title | Type |
|---|---|---|---|
| 1 | 1 | Welcome to data science! | Lecture |
| 1 | 1 | Meet the toolkit: Programming | Lecture |
| 1 | 1 | Meet the toolkit: Version control & collaboration | Lecture |
| 1 | 1 | Hello R | Lab |
| 1 | 1 | Pet names | Homework |
| 2 | 2 | Data and visualisation | Lecture |
| 2 | 2 | Visualising data with ggplot2 | Lecture |
| 2 | 2 | Visualising numerical data | Lecture |
| 2 | 2 | Visualising categorical data | Lecture |
| 2 | 2 | StarWars + Dataviz | Application exercise |
| 2 | 2 | Plastic waste | Lab |
| 2 | 2 | Airbnb listings in Edinburgh | Homework |
| 2 | 3 | Tidy data | Lecture |
| 2 | 3 | Grammar of data wrangling | Lecture |
| 2 | 3 | Working with a single data frame | Lecture |
| 2 | 3 | Working with multiple data frames | Lecture |
| 2 | 3 | Tidying data | Lecture |
| 2 | 3 | Hotels + Data wrangling | Application exercise |
| 2 | 3 | Nobel laureates | Lab |
| 2 | 3 | Road traffic accidents | Homework |
| 2 | 4 | Data types | Lecture |
| 2 | 4 | Data classes | Lecture |
| 2 | 4 | Recoding data | Lecture |
| 2 | 4 | Hotels + Data types | Application exercise |
| 2 | 4 | La Quinta is Spanish for next to Denny’s, Pt. 1 | Lab |
| 2 | 4 | College majors | Homework |
| 2 | 5 | Importing data | Lecture |
| 2 | 5 | Nobels + Sales + Data import | Application exercise |
| 2 | 5 | Tips for effective data visualization | Lecture |
| 2 | 5 | Brexit + Telling stories with dataviz | Application exercise |
| 2 | 5 | Take a sad plot and make it better | Lab |
| 2 | 5 | La Quinta is Spanish for next to Denny’s, Pt. 2 | Homework |
| 2 | 6 | Scientific studies and confounding | Lecture |
| 2 | 6 | Simpson’s paradox | Lecture |
| 2 | 6 | Doing data science | Lecture |
| 2 | 6 | Simpson’s paradox | Lab |
| 2 | 6 | Legos | Homework |
| 2 | 7 | Web scraping | Lecture |
| 2 | 7 | Scraping top 250 movies on IMDB | Lecture |
| 2 | 7 | Web scraping considerations | Lecture |
| 2 | 7 | IMDB + Web scraping | Application exercise |
| 2 | 7 | Work on projects | Lab |
| 2 | 7 | Work on projects | Homework |
| 2 | 8 | Functions | Lecture |
| 2 | 8 | Iteration | Lecture |
| 2 | 8 | University of Edinburgh Art Collection | Lab |
| 2 | 8 | Money in politics | Homework |
| 3 | 9 | Misrepresentation | Lecture |
| 3 | 9 | Data privacy | Lecture |
| 3 | 9 | Algorithmic bias | Lecture |
| 3 | 9 | Conveying the right message through visualisation | Lab |
| 3 | 9 | Project proposals | Project |
| 3 | 9 | Peer review of project proposals | Homework |
| 4 | 10 | Fitting and interpreting models | Lecture |
| 4 | 10 | Modelling nonlinear relationships | Lecture |
| 4 | 10 | Models with multiple predictors | Lecture |
| 4 | 10 | More models with multiple predictors | Lecture |
| 4 | 10 | Grading the professor, Pt 1 | Lab |
| 4 | 10 | Bike rentals in DC | Homework |
| 4 | 11 | Logistic regression | Lecture |
| 4 | 11 | Prediction and overfitting | Lecture |
| 4 | 11 | Feature engineering | Lecture |
| 4 | 11 | Grading the professor, Pt. 1 | Lab |
| 4 | 11 | Exploring the GSS | Homework |
| 4 | 12 | Cross validation | Lecture |
| 4 | 12 | The Office, Part 1 | Application exercise |
| 4 | 12 | The Office, Part 2 | Application exercise |
| 4 | 12 | Bootstrapping | Lecture |
| 4 | 12 | Work on projects | Lab |
| 4 | 12 | Grading the professor, Pt. 2 | Homework |
| 4 | 13 | Quantifying uncertainty | Lecture |
| 4 | 13 | Bootstrapping | Lecture |
| 4 | 13 | Hypothesis testing | Lecture |
| 4 | 13 | Inference overview | Lecture |
| 4 | 13 | Smoking during pregnancy | Lab |
| 4 | 13 | Modelling the GSS | Homework |
| 5 | 14 | Text analysis | Lecture |
| 5 | 14 | Comparing texts | Lecture |
| 5 | 14 | Interactive web apps | Lecture |
| 5 | 14 | Machine learning | Lecture |
| 5 | 14 | Collaborating on GitHub | Lab |
| 5 | 14 | Wrap up | Homework |
| 5 | 15 | Bayesian inference | Lecture |
| 5 | 15 | Building interactive web apps, Pt. 1 | Lecture |
| 5 | 15 | Building interactive web apps, Pt. 1 | Lecture |
| 5 | 15 | Project presentations | Lab |
| 5 | 15 | N/A | Homework |