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To be admitted to the Mannheim Master of Applied Data Science & Measurement, candidates are required to have a set of particularly important skills. This will help to assure a swift and rewarding program progress and individual learning success.
Due to our unique track structure the individual admissions process depends on the participants choices as well as the results from the placements test.
Please present yourself in a five-minute video or a PowerPoint presentation with a maximum of 10 slides, answering the
The letter of motivation forms an important part of the admissions committee's evaluation of your application. Please explain your priorities in the three tracks. The committee uses the essay to measure your experience and interest in various components of survey and data science.
Identify the components you have experienced and describe the nature of that experience. Describe how you hope the master's program will advance your training. The committee prefers essays that are no more than two typewritten pages (12 point font). Please highlight elements that can't be found in the CV.
Proof of appropriate English language skills can be provided by the following tests:
a) TOEFL (Test of English as a Foreign Language) with a minimum score of 95.
b) IELTS (International English Language Testing System – Academic Test) with a minimum of 7.0.
c) Certificate of Advanced English with at least level C1.
d) A successfully completed course of study, which was essentially based on English as the language of instruction and examination.
The results must not be more than two years old at the time of the closing date for applications.
The learning experience in this course will mainly rely on the online interaction between students and the instructor during the weekly online meetings. Therefore, we encourage all students in this course to use a web camera and a headset. We recommend that students use a high speed internet connection (LAN), if available, when connecting to the online meetings. Wireless connections (WLAN) are usually less stable and might be dropped. We will use Canvas, a learning management system, for our online courses.
The current course fee is € 17,000 due to an introductory discount, while the regular fee is € 25,000.
Please note the current fee installment plan. 10 equal installments of EUR 1,700 (one thousand, seven hundred), due on the following dates:
The rolling admissions period for the Mannheim Master of Applied Data Science & Measurement (MDM) started on 17 February.
Last places are available. Contact Olga Gotsulyak for an application for the 2020 intake.
Depending on which track you apply for, you need to take part in the Statistical Placement Test (SPT) or the Data Preparation Test (DPT), or both. The required results vary between the tracks.
You will be accepted to the Survey Methodology Track if you take the Statistical Placement Test. If you pass it, your master program will start in September. If you do not pass it, your master program will start with the Pre-Course “Review of statistical concepts” in June.
You will be accepted to the Survey Statistics Track if you pass the Statistical Placement Test with a minimum score of 80%. If you do not achieve the minimum score, you cannot choose this track. If you achieve the necessary score, the next step is taking the Data Preparation Test. If you pass it, your master program will start in September. If you do not pass it, your master program will start with the Pre-Course “Introduction to Real-World Data Management” in June.
You will be accepted to the Data Insights Track if you take the Statistical Placement Test and the Data Preparation Test. In case of passing both tests your master program will start in September. If you fail one of the tests, your master program will start in June with the Pre-Course “Review of statistical concepts” or “Introduction to Real-World Data Management”. If you fail both tests, you need to take both Pre-Courses.
Please find below a short overview about the test topics.
Data management, manipulation and wrangling skills as well as skills with R (base R and tidy R).
The normal distribution, the sampling distribution, standard errors, type I / type II errors, t-test, correlation coefficients, OLS regression and very little about categorical data analysis (odds ratios, logistic regression).