We offer open trainings and individual company courses in the area of Data Science, Machine Learning and Statistics. The target group of our courses are users from any discipline who on the one hand want to better understand methods from the... mentioned areas and on the other hand want to gain application-related software knowledge for the analysis, visualization, modeling and prediction of data. Many of our instructors are subject matter experts and scientists (e.g. professors, PhD experts or experienced PhD students) and bring both a high level of expertise and several years of teaching and practical experience.
Our course offerings include both software courses with a focus on the practical application of methods from the field of Data Science (especially with the programming languages R and Python) and theoretical courses with a greater focus on how the learned methods work and their use cases. Overall, our training courses are a balanced mix of practical application and theory.
The course was very well structured and had the right mix of theoretical basics and practical application. Due to the appropriate scope and pace, it is also well suited for NLP novices. We now have the prerequisites to convert our existing models to Python or test new models on our data. The course has also whetted our appetite to further explore the newer developments in NLP.
Max and Daniel were very competent and likeable teachers and conveyed the material very well. You could tell that they knew their stuff and enjoyed imparting their knowledge. I have benefited greatly and would recommend the course at any time. Nevertheless, a note: - It went on the 2nd day a little much time for the mediation of basic statistical knowledge drauf. Now this is not a basic statistics course, but an R course. For me it was about being able to implement statistical methods technically with R. (And that is what we learned. (And that's what we learned). Whether I then use these statistical methods "correctly": That is important, but not the subject of this course. I would therefore have preferred that the available time be spent on R, not on repetition of scale levels, averages, etc.
I was not in the Machine Learning course, but in the R Basic course. It says Machine Learning in the title here! One request: The level of prior experience in the course was very different - while no one has really worked with R yet, some people had prior experience in other programming languages and others were very familiar with statistical methods. For me it was both, accordingly it was sometimes even rather too slow. Perhaps one could query this beforehand.
Very much new material in quite a short time. Very comprehensive. Good: the exam to deal with the material intensively.
On the first day, at the beginning, it might have been helpful to introduce the topic of machine learning / statistics in general and then say: And the methods used in unsupervised learning, we present on day X, supervised learning methods on day Y, the theoretical basics on day Z. I was then also able to understand this while learning, but I always find it good to know how individual courses are related.
The materials were very well prepared and the trainers were very competent. Sufficient questions were answered. In view of the given time frame, the amount of material is quite challenging and I would have liked more time for practical exercises analogous to the last project day.
I would actually allow more time for implementation based on examples, group work is appropriate here. In my view, you would need at least 1-2 days more for this. Best also with a trainer per group, so that one does not "get lost" too long at individual points.
Otherwise VERY, VERY good!
There is little to improve, just a few suggestions:
- Print all documents in the same type (the mix of spiral and no binding is unfortunate, either or.
- Overall, an emphasized placement of the content in the overall context would still be important. The oral exam is appropriately structured and Data Import to visualization and weighing of methods. The statistical basics are too formula-dominated, rather more overall context.
Overall the amount of material is borderline high, especially for the exam without any real preparation time. Thanks to all lecturers, all of them sympathetic, method- and explanation-sure!
For me personally, a stronger integration of exercises would make sense. (Similar to a lecture at the university, the knowledge often only really sets in the exercise, so this should also be the case here).
-> More "hands-on" exercises in R would be an improvement for me personally. But overall very satisfied. Thank you!
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Profile active since 09/20/2019 | Last update: 06/10/2020
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