The ever-growing influx of data allows us to develop, interpret and apply an increasing set of learning techniques. However, with this increase in data comes a challenge: how to make sense of the data and identify the components that really matter in our modeling efforts. This course gives a detailed and modern overview of statistical learning with a specific focus on high-dimensional data.
In this course we emphasize the tools that are useful in solving and interpreting modern-day analysis problems. Many of these tools are essential building blocks that are often encountered in statistical learning. We also consider the state-of-the-art in handling machine learning problems. We will not only discuss the theoretical underpinnings of supervised learning, but focus also on the skills and experience to rapidly apply these techniques to new problems.
During this course, participants will actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualization techniques, especially on high-dimensional data problems. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, you will gain hands-on experience in using them on real data during the course and interpreting the results.
At the end of this course, students are able to apply and interpret the theories, principles, methods and techniques related to contemporary data science and understand and explain different approaches to data analysis:
Sections from the following freely available books:
Several freely available articles.
In this course, we will exclusively use R & RStudio for data analysis.
First, install the latest version of R for your system (see
install the latest (desktop open source) version of the RStudio
integrated development environment
We will make extensive use of the
tidyverse suite of packages, which
can be installed from within
R using the command
To develop the necessary skills for completing the assignments and
the exam, 9
R practicals must be made and submitted. These
exercises are not graded, but students must fulfill them to pass the
There are two pass/fail assignments. Successful and timely completion of these assignments will grant you a bonus point on the exam. You make and submit these as a group.
100% of your grade will be determined by an exam featuring both
knowledge questions as well as practical data analysis skills in
R. Some example questions will be made available to you so you can
|Wednesday||11-11-2020||13:15 - 15:00||Lecture 1|
|Friday||13-11-2020||11:00 - 12:45||Q&A 1|
|Wednesday||18-11-2020||13:15 - 15:00||Lecture 2|
|Friday||20-11-2020||11:00 - 12:45||Q&A 2|
|Wednesday||25-11-2020||13:15 - 15:00||Lecture 3|
|Friday||27-11-2020||11:00||Deadline assignment 1 EDA|
|Friday||27-11-2020||11:00 - 12:45||Q&A 3|
|Wednesday||02-12-2020||13:15 - 15:00||Lecture 4|
|Friday||04-12-2020||11:00 - 12:45||Q&A 4|
|Wednesday||09-12-2020||13:15 - 15:00||Lecture 5|
|Friday||11-12-2020||11:00 - 12:45||Q&A 5|
|Wednesday||16-12-2020||13:15 - 15:00||Lecture 6|
|Friday||18-12-2020||11:00 - 12:45||Q&A 6|
|Wednesday||13-01-2021||13:15 - 15:00||Lecture 7|
|Friday||15-01-2021||11:00 - 12:45||Q&A 7|
|Wednesday||20-01-2021||13:15 - 15:00||Lecture 8|
|Friday||22-01-2021||11:00||Deadline assignment 2 Prediction|
|Friday||22-01-2021||11:00 - 12:45||Q&A 8|
|Wednesday||27-01-2021||13:15 - 15:00||Lecture 9|
|Friday||29-01-2021||11:00 - 12:45||Q&A 9|
|Friday||05-02-2021||13:30 - 16:30||Exam|
11-11-2021 | 13:15 - 15:00
Read the prerequisites on the practicals website uudav.nl. Install R and RStudio as per the instructions there.
18-11-2020 | 13:15 - 15:00
25-11-2020 | 13:15 - 15:00
Hand in on blackboard before practical 3 (
27-11-2020 | 11:00).
02-12-2020 | 13:15 - 15:00
09-12-2020 | 13:15 - 15:00
16-12-2020 | 13:15 - 15:00
ISLR Chapter 9 & Paragraph 4.4.4
13-01-2021 | 13:15 - 15:00
20-01-2021 | 13:15 - 15:00
Hand in on blackboard before practical 8 (
22-01-2021 | 11:00).
27-01-2021 | 13:15 - 15:00
Optional reading: - Kumar Chapter 6: Association analysis all paragraphs through 6.2.2
05-02-2021 | 13:30 - 16:30
05-03-2021, to be confirmed.