The complete course material
| Session | Date | Topic | Slides |
|---|---|---|---|
| 1 | March 17, 2022 | General introduction | Slides Orga, Slides Install |
| 2 | March 23, 2022 | Philosophy of Data Science | Slides Philo |
| 3 | March 24, 2022 | First steps in R | Slides |
| 4 | March 31, 2022 | Basic object types | Slides |
| 5 | April 6, 2022 | Advanced object types | Slides |
| 6 | April 7, 2022 | Visualization I | Slides |
| 7 | April 27, 2022 | Projects and data import | Slides |
| 8 | April 28 and May 4, 2022 | Data wrangling I | Slides |
| 10 | May 5, 2022 | R Markdown | Slides |
| 11 | May 12, 2022 | Intro models | Slides |
| 12 | May 18 and May 19, 2022 | Simple Linear Regression | Slides |
| 14 | June 1 and June 2, 2022 | Multiple Linear Regression | Slides |
| 16 | June 9, 16 and 23, 2022 | Sampling Theory | Slides |
| A1 | Omitted | Bootstrap and confidence intervals | Slides |
| A2 | Omitted | Hypothesis testing | Slides |
Session 1: Introduction and installation
Mandatory readings
Session 2: Philosophy of Data Science
Mandatory readings
Session 3: First steps in R I - Basics and functions
Mandatory readings
- R for Data Science, Chapter 4: Workflow - basics
- Tutorial: Commands, objects, and assignments
- Tutorial: Functions
Further readings
Exercises
After installing (and updating) the DataScienceExercises-package
(as described here), run the following command
from within your R session:
learnr::run_tutorial(
name = "Basics",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Session 4: Basic object types
Mandatory readings
Exercises
learnr::run_tutorial(
name = "ObjectTypes1",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Session 5: Advanced object types
Mandatory readings
Exercises
learnr::run_tutorial(
name = "ObjectTypes2",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Session 6: Visualization
Mandatory readings
Further readings
Exercises
learnr::run_tutorial(
name = "Visualization1",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Session 7: Project organization and data import
Mandatory readings
Exercises
learnr::run_tutorial(
name = "ProjectOrga",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Sessions 8 & 9: Data wrangling
- Slides
- Lecture notes
- Exercise data
- Lecture scripts from the session and exercise solutions
- Data for lecture notes
Mandatory readings
Further readings
Exercises
learnr::run_tutorial(
name = "Wrangling1",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Session 10: R Markdown
- Slides
- DesasterMarkdown.pdf
- NicerMarkdown.pdf
- Code for the markdown desaster and a possible solution
Mandatory readings
Further reading
Exercises
learnr::run_tutorial(
name = "RMarkdown",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
The practical exercise for this topic can be found here.
Session 11: Modelling data
After installing (and updating) the DataScienceExercises-package
(as described here), run the following command
from within your R session:
Exercises
After installing (and updating) the DataScienceExercises-package
(as described here), run the following command
from within your R session:
learnr::run_tutorial(
name = "Models",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Sessions 12 & 13: Simple linear regression
Mandatory readings
Exercises
learnr::run_tutorial(
name = "LinearRegression1",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Sessions 14 & 15: Multiple linear regression
Mandatory readings
Exercises
learnr::run_tutorial(
name = "LinearRegression2",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))
Sessions 16 & 17: Sampling theory
Mandatory readings
Exercises
learnr::run_tutorial(
name = "Sampling",
package = "DataScienceExercises",
shiny_args=list("launch.browser"=TRUE))