Frequently asked questions
What are the formal requirements?
The Certificate Program is intended for anyone working in any area of the working or educational life. In order to participate, either a university degree with a 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 and at least three years of work experience in an area relevant to the certified data science training program.
Recommendation: After each application deadline, the steering committee chooses the most qualified candidates among all applicants, which can vary from one application cycle to the next. So we can never tell you in advance if you meet the formal requirements before you apply (e.g., if you got rejected in one application round, your application might be accepted in a subsequent application round, particularly if the pool of competitors are less suited to the program). The best you can do is to convince the steering committee with a strong CV by listing all data science-related courses you've completed and detailing all data science-related tasks you have performed in your professional career as bullet points (focus on highlighting all mathematical, statistical, computer science and programming knowledge or skills). Remember, the more compelling and tailored your CV, the higher your likelihood of securing a place in the certificate program.
Is R the main programming language used in the course?
Yes. For many practical sessions in the Data Science Certificate Program, we highly recommend a basic knowledge of the statistical programming language R (in some practical sessions, we may supplement the R code with additional Python code; however, our primary emphasis remains on utilizing R in all practical code examples). Therefore, if you lack basic proficiency in R or seek to refresh your skills, we strongly advise either undertaking self-study to learn the basics in R or enrolling in a dedicated R course (e.g., Essential Data Science Training GmbH, a LMU Spin-off, offers a specialized R course before the beginning of every program). For more detailed information please look at the curriculum.
Do you recommend the in-person or the online course format?
It depends on your personal situation. Both formats have advantages and disadvantages. However, note that the online course is usually more competitive than the in-person course due to a higher number of international applicants. For participants not living in Europe, we recommend the online course.
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 application 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 kind of visa do I need?
To attend the 10-day in-person Data Science Certificate Program at LMU Munich, you may require a specific type of visa. We strongly recommend that you explore the option of participating in our next online course if you anticipate visa challenges. The visa application process and requirements for in-person courses can be complex and lengthy. Please consult the German embassies or consulates in your home country for precise and up-to-date information. It is important to note that our acceptance notifications for the in-person course are typically informal e-mail notifications which are provided approximately four weeks before the course start date. Moreover, as the program is designed as extra-occupational training for professionals, participants are not formally enrolled as LMU students which might affect the possibility of applying for a student visa. Additionally, be aware that our 10-day in-person course in Munich is often spread over multiple weeks, potentially exceeding the typical 90-day limit for short-term visits. Due to these factors, securing a visa within the available timeframe can be challenging, which is why we recommend applying for our online course in such situations.
When do I have to pay the fee?
After the application deadline, the most suitable candidates will receive a notification of acceptance. Shortly after this notification, you will receive the invoice from our office (usually with a 14-day or 30-day deadline for the payment). So, the payment does not have to be done before the course starts as the payment deadline is linked to the date of receipt of the invoice (e.g., the payment deadline could also be shortly after the course has started). Please get in touch with us if there are any delays in your payment. You may ask for a reasonable extension of the payment deadline before it expires.
What does the fee include?
The fee includes digital course material and a graded certificate. In the case of an in-person course, the fee also includes catering during coffee and lunch breaks.
What if I miss one or several course days?
Participation is not compulsory but highly recommended. The course material is available online shortly after each course day (currently, we do not provide recordings or videos) and you are expected to self-study the missed course days. Participation in most of the course days and, in particular, passing the exam is mandatory to obtain the certificate.
What happens at the exam?
There will be a written and oral exam on the last unit of the certificate program (see curriculum for the latest dates). The written exam contains multiple choice, single choice, and (short!) open questions. In the oral exam, you discuss a use case or practical problem description together with a lecturer and a few other participants. Consider the oral exam as a “team meeting” in which you have to discuss how you would approach the provided data analytic problem.
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.
What do I need to participate?
Bring a laptop with you, ideally with administration rights. For the entire course, we kindly ask you to install the 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 ("mlr3verse")). 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). In case the course is held as an online live course, we kindly ask you to use a PC or laptop with a camera and microphone function and to make sure that you are able to participate via the conferencing tool ZOOM.
In which language is the program?
The planned course language is English (unless all participants understand German well enough which is very unlikely). The provided course material will always be in English.
At what time do the courses start and end?
If not stated otherwise, the lectures of the in-person course are usually held as a full-day event from 09:00 to 17:00. For the online course, each lecture might be divided into 2 half-days (e.g., on Thursdays and Fridays either from 09:00-12:30 or from 13:30 - 17:00).
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