Fundamentals of Data Science - Einzelansicht

Die Veranstaltung findet online statt und beginnt in der 2. Vorlesungswoche.

Dear students,

this summer semester, the course will be offered online. Throughout the course, we will rely heavily on Moodle and Zoom. While most of you are familiar with Moodle, using Zoom for learning might be new and unfamiliar. Please explore our "Zoom for students" guidelines for more details. 

On April 24 (Friday), we will send out the Moodle key via email to all registered in PULS participants.

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.".

Supplementary Readings: Additional articles and resources will be provided on a need-to basis via Moodle.

Seminar presentation, short report, and written exam

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 very basic concepts of R programming and work our way through more sophisticated tasks of data representation, manipulation, and analysis. We illustrate every step with easy-to-follow examples.  After taking the course, you should be able to do the following:

• Program in R for data science, which includes (a) getting help and (b) applying the code contributed by the active community of R developers
• Get the data in and out of R
• Understand the data via conducting descriptive analysis and visualizing the data 
• Create beautiful graphs and visualizations with the ggplot package
• Use the power of R to build and assess statistical and machine learning models
• Write reports and blog-posts in R Markdown

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, we will cover a new topic and offer materials for practicing new skills and self-studying (HW assignments). Towards the end of the semester, group project work will allow course participants to apply their R-programming and data science skills and share results with fellow students. Each project group is assigned a specific dataset and works on the corresponding task, e.g., predicting customer churn, earthquakes, defaults on a loan or mortgage.

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

Syllabus (Tentative)

Tue 28.04 - Organisational trivia & introduction into R 
Tue 05.05 - Objects in R
Tue 12.05 - Functions & flow of the code & Data Import/Export

Tue 19.05 - EDA & Visualization I
Tue 26.05 - EDA & Visualization II
Tue 02.06 - Visualisation III ggplot

Tue 09.06 - Modeling Part I
Tue 16.06 - Modeling Part II
Tue 23.06 - Modeling Part III

Tue 30.06 - Modeling Part IV & Consultation Hours
Tue 07.07 - Project work (no session)
Tue 14.07 - Project work: Deadline & Presentations-Session