Chapter 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 Lecture
1 1 Hello R Lab
1 1 Edinburgh Airbnb rentals Homework
1 2 Data and visualization Lecture
1 2 Building plots for various data types Lecture
1 2 Plastic waste Lab
1 2 North Carolina bike crashes Homework
1 3 Tidy data and data wrangling Lecture
1 3 Joining data from multiple sources Lecture
1 3 Data tidying and reshaping Lecture
1 3 Nobel laureates Lab
1 3 What should I major in? Homework
1 4 Data types and recoding Lecture
1 4 Importing data Lecture
1 4 La Quinta is Spanish for ‘next to Denny’s’, Pt. 1 Lab
1 4 La Quinta is Spanish for ‘next to Denny’s’, Pt. 2 Homework
1 5 Tips for effective data visualization Lecture
1 5 Scientific studies and confounding Lecture
1 5 Communicating data science results effectively Lecture
1 5 Ugly charts Lab
1 5 Legos and instructors Homework
1 6 Web scraping Lecture
1 6 Functions and iteration Lecture
1 6 University of Edinburgh Art Collection Lab
1 6 Money in politics Homework
1 6 Project proposal
2 7 The language of models Lecture
2 7 Linear models with a single predictor Lecture
2 7 Grading the professor, Pt. 1 Lab
2 7 Project proposal peer review Homework
2 8 Modeling non-linear relationships Lecture
2 8 Linear models with multiple predictors Lecture
2 8 Grading the professor, Pt. 2 Lab
2 8 Bike rentals in DC Homework
2 9 Model selection Lecture
2 9 Model validation Lecture
2 9 Working on projects Lab
2 9 Work on projects Homework
2 10 Logistic regression and classification Lecture
2 10 Quantifying uncertainty Lecture
2 10 Collaborating on GitHub Lab
2 10 Wrapping up Homework
3 11 Data science ethics Lecture
3 11 Text analysis Lecture
3 11 Project presentations & write up

20.2 15-week schedule

Unit Week Title Type
1 1 Welcome to data science! Lecture
1 1 Meet the toolkit Lecture
1 1 Hello R Lab
1 1 Edinburgh Airbnb rentals Homework
1 2 Data and visualization Lecture
1 2 Building plots for various data types Lecture
1 2 Plastic waste Lab
1 2 North Carolina bike crashes Homework
1 3 Tidy data and data wrangling Lecture
1 3 Joining data from multiple sources Lecture
1 3 Nobel laureates Lab
1 3 What should I major in? Homework
1 4 Data tidying and reshaping Lecture
1 4 Data types and recoding Lecture
1 4 La Quinta is Spanish for ‘next to Denny’s’, Pt. 1 Lab
1 4 La Quinta is Spanish for ‘next to Denny’s’, Pt. 2 Homework
1 5 Tips for effective data visualization Lecture
1 5 Scientific studies and confounding Lecture
1 5 Communicating data science results effectively Lecture
1 5 Ugly charts Lab
1 5 Legos and instructors Homework
1 6 Importing data Lecture
1 6 Web scraping Lecture
1 6 Work on projects Lab
1 6 Project proposal Homework
1 7 Functions and iteration Lecture
1 7 Exploring data review3 Lecture
1 7 University of Edinburgh Art Collection Lab
1 7 Peer review of project proposals Homework
2 8 The language of models Lecture
2 8 Linear models with a single predictor Lecture
2 8 Work on projects Lab
2 8 Money in politics Homework
2 9 Modeling non-linear relationships Lecture
2 9 Linear models with multiple predictors Lecture
2 9 Grading the professor, Pt. 1 Lab
2 9 Bike rentals in DC Homework
2 10 Model selection Lecture
2 10 Model validation Lecture
2 10 Grading the professor, Pt. 2 Lab
2 10 [Not available] Homework
2 11 Logistic regression and classification Lecture
2 11 Quantifying uncertainty Lecture
2 11 [Not available] Lab
2 11 [Not available] Homework
2 12 Hypothesis testing with randomization Lecture
2 12 Inference overview Lecture
2 12 So what if you smoke when pregnant? Lab
2 12 Exploring the General Social Survey Homework
2 13 Simulation based inference review Lecture
3 13 Data science ethics Lecture
3 13 Working on projects Lab
3 13 Wrapping up Homework
3 14 Interactive data visualization Lecture
3 14 Interactive data visualization and reporting Lecture
3 14 Collaborating on GitHub Lab
3 14 Work on projects Homework
3 15 Bayesian inference Lecture
3 15 Text analysis Lecture
3 15 Work on projects Lab
3 15 Project presentations & write up

  1. Slides not included in Data Science Course in a Box.↩︎