Frequently asked questions
In which language is the program?
The planned course language is English unless all participants understand German well enough. However, all provided materials are in English.
At what time do the courses start and end?
The course takes place on fridays, 9 a.m. to 5 p.m. For details please see the curriculum.
How can I get discount?
The 20% discount is intended for applicants from social, scientific and cultural facilities. If a company is a sponsoring member of the German Society for Data Science (GDS) e.V. and registers more than four applicants who are admitted to the course by the Steering Committee, a discount of 20% will be granted per participant. In order to claim the discount you must attach a proof together with your application.
Are there any requirements?
The Certificate Program in generally intended for anyone working in any area of the working or educational life. In order to participate, either a university degree with focus in mathematics, statistics, computer science or related disciplines and at least one year of work experience is required or any kind of university degree qualifying and at least three years of work experience in an area relevant to the certified data science training program.
Is R the main programming language used in the course?
Yes. Basic knowledge in R is highly recommended for the Professional Certificate Program "Data Science". A crash course is available before the beginning of every program. For more information follow the link to the website of the Munich R Courses. For more detailed information please look at the curriculum.
What is the admission process?
The admission process can only be done online. Please submit your application online via the following link. You will shortly receive a confirmation of receipt via e-mail. Shortly after the deadline, the steering committee decides whether your application is being considered. Not later than six weeks before the beginning of the course the registration is binding.
What does the fee include?
The fee includes snacks, drinks, coffee, course material and a graded certificate.
What if I miss one or several course days?
Participation is not compulsory but highly recommended. All material is available online shortly after each course day. However, participating and passing the exam is mandatory to obtain the certificate.
What happens at the exam?
There will be a written and an oral exam on the 10th day of our certificate program (see curriculum for latest dates). Both exams (written and oral) will be open book exams. The written exam (60 min.) starts at 9:00 am and consists of short questions, multiple choice and true-false questions.
For the oral exam, you will receive a written description of a data analytic problem with associated questions. Each participant has 60 min. preparation time to come up with a sketch for a reasonable plan in order to approach the problem description and solve the questions. After the preparation time, the oral exam will be graded based on a group discussion with other participants (you can see this as a "team meeting" in which you discuss how to approach a data analytic problem with the topics learned at our certificate program). During the discussion, there will be two examiners who will sometimes ask general questions in-between.
What if I fail the exam?
A failed exam can be repeated on the next possible regular date and a separate examination fee will be charged for each retest. See the Course Regulations (104 KByte) for further information.
Do I need to bring anything?
Please note we do not provide computers or laptops. For the entire course we kindly ask you to bring your own laptop with a current version of the free software R (https://cran.r-project.org) and RStudio (https://www.rstudio.com/products/rstudio/download).
If you do not have administration rights on your laptop (often the case in company laptops), you should also make sure that you can install R packages on your laptop (for example, with the R command install.packages ("mlr")). We will provide Internet access for all participants via BayernWLAN, please make sure that you can access free WIFI hotspots as you may need internet access for some lectures (e.g. for solving quizzes).
Is there any relevant literature?
This bibliography is only a recommendation, not a requirement to prepare for the course.
- T. Hastie, R. Tibshirani, J. Friedman (2009): The Elements of Statistical Learning. Springer
- K. Murphy (2012): Machine Learning: a Probabilistic Perspective. MIT Press
- E. Alpaydin (2010): Introduction to Machine Learning. MIT Press
- C. M. Bishop (2006): Pattern Recognition and Machine Learning. Springer
- C. Eckert: IT-Sicherheit: Konzepte - Verfahren - Protokolle. Oldenburg
- M. Brenner, N. Felde, et al. (2015): Praxisbuch ISO/IEC 27001: Management der Informationssicherheit und Vorbereitung auf die Zertifizierung. Zur Norm ISO/IEC 27001. Carl Hanser Verlag GmbH
- C. Heumann, M.Schomaker, Shalabh (2017): Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R. Springer
- Wickham (2010): ggplot2: Elegant Graphics for Data Analysis. Springer