PULS: https://puls.uni-potsdam.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=101641&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung
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.
- Kursleiter*in: Olga Abramova
- Kursleiter*in: Margarita Gladkaya