Foto: Matthias Friel
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.
Moodle Fundamentals of Data Science SS 2021 (FoDS_2021)Moodle Key: FODS2021
The first class will take place on Tuesday, 13.04, at 10:15 in Zoom.https://uni-potsdam.zoom.us/j/63618632996Passcode: 13042021Materials for the first session will be available on Monday, 12.04., 18:00.
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.
Interest in Data Science. This class is limited to 30 students. The class will be held in English. Project presentations can be held in German or in English. Exam answers can be written in German or in English.
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 13.04 -- Organisational trivia & introduction into R Tue 20.04 -- Objects in RTue 27.04 -- Functions & flow of the code & Data Import/ExportTue 04.05 -- EDA & Visualization ITue 11.05 -- EDA & Visualization IITue 18.05 -- Visualisation III ggplotTue 25.05 -- Modeling Part ITue 01.06 -- Modeling Part IITue 08.06 -- Modeling Part IIITue 15.06 -- Modeling Part IV & Consultation HoursTue 22.06 -- Project work (there will be no Q&A and no tutorial this week) Tue 29.06 -- Project work: Deadline & Presentations-SessionTue 06.07 -- Academic coordinationTue 13.07-- Exam preparation Tue 20.07 --Exam Termin 1. The exam questions will be formulated in English. You can answer them either in English or German.Tue End of September --Exam Termin 2.
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