20 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.

20.1 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

20.2 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