Fundamentals of Data Science - Einzelansicht

Zumel, N., Mount, J., & Porzak, J. (2014). Practical data science with R (pp. 101-104). Manning.

Grolemund, G. (2014). Hands-On Programming with R: Write Your Own Functions and Simulations. " O'Reilly Media, Inc.".

Interest in Data Science. This class is limited to 18 students. The class will be held in English. Project Presentations can be held in German or in English.

Seminar presentation, short report, and written exam

PLEASE NOTE THE COURSE STARTS ON 16.04.2019 at 10:15!

Data is increasingly seen as a driving force behind many industries, ranging from data-driven start-ups to traditional manufacturing companies. Recent years have been marked by the hype around big data technologies and the implications that go along with it. In response to these developments, data science has become one of the most demanded specializations. Against this background, this class will introduce students to the fundamentals of data science, using R for data analysis.

Purpose of the class: This course is an introduction to data science using the statistical programming language R. Preliminary R knowledge is not required. We start by introducing the interface and work our way from the very basic concepts of the R language through more sophisticated data manipulation and analysis. We illustrate every step with easy-to-follow examples. R doesn’t function as your average scripting language, and it has plenty of unique features that may seem surprising at first. After participating in the class you should be able to do the following:
-Perform data analysis by using a variety of powerful tools
-Use the power of R to do statistical analysis and other data-processing tasks
-Know how to find, download, and use code that has been contributed to R by its very active community of developers
-Know where to find extra help and resources to take your R coding skills to the next level
-Create beautiful graphs and visualizations of your data

Audience: Bachelor students who are interested in data science and data analysis. At a broader level, the course serves as good preparation for writing a bachelor thesis or doing an internship in the "data science" field.

Format: Each week contains a mix of taught material, self-study material, practical exercises and homework (assignments). Then, participants in a group of 2 people will be given an opportunity to apply the skills they got to their own project and share the results with other participants during the presentation session.

The language of project presentations: German or English. Lectures and Exercises will be held in English.

Syllabus (Tentative)

16.04 10:15 - 13:45 – Introduction
23.04 10:15 - 13:45 – Lecture | Exercise
30.04 10:15 - 13:45 – Lecture | Exercise
07.05 10:15 - 13:45 – Lecture | Exercise
14.05 10:15 - 13:45 – Lecture | Exercise
21.05 10:15 - 13:45 – Lecture | Exercise
28.05 10:15 - 13:45 – Lecture | Exercise
04.06 10:15 - 13:45 – Lecture | Exercise
11.06 10:15 - 13:45 – Seminar | Project Work
18.06 10:15 - 13:45 – Seminar | Project Work
25.06 10:15 - 13:45 – Seminar | Project Work
02.07 10:15 - 13:45 – Seminar | Project Work
09.07 10:15 - 13:45 – Seminar | Project Work
16.07 10:15 - 13:45 – Exam