Data Science Retreat
Data Science Retreat is a 3 month full time programming retreat based in Berlin, Germany. DSR offers two tracks for data scientists and data engineers. The retreat is not specific to a single language, but students must arrive proficient in at least one language. Major technologies covered include Python and R for data scientists and Python and Scala for data engineers. Pair programming is a main feature of the program. DSR concludes with a portfolio project and includes a hiring day with several hiring partners and 3 months of access to their hiring network. Tuition is 8000 euros and class sizes are between 5-10 students.
Recent Data Science Retreat News
Recent Data Science Retreat Reviews: Rating 4.67
The Data Scientist track is for data scientists with some experience in machine learning. The instruction focuses on advanced topics in machine learning, and requires programming ability, but not as much as that of a professional engineer. Some aspects of the Big-data Engineer track are included, such as Hadoop and Spark.
- Minimum Skill Level
- You should know at least one programming language well (even if it's not R or Python).
The Big-data Engineer track is for engineers with experience building software products. The instruction focuses on building robust, scalable, data-intensive systems. A solid introduction to machine learning is included, focusing more on implementation and putting algorithms into production.
- Minimum Skill Level
- You should know at least one programming language well (even if it's not R or Python).
Data Science Retreat Reviews
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After spending 5 years in academia, I took a few MOOCs and found myself loving data science.
However, MOOCs were not giving me enough progress to becoming a data scientist. For a few weeks, I researched and applied to a few data science bootcamps worldwide. Some bootcamps focused heavily on statistics, which I found later on, are not true ML bootcamps. These are based on using statistical techniques to gain insights from data. Other bootcamps were too simple, and focused on SQL. However, I was after ML and artificial intelligence (A.I.), and that’s what I found at DSR.
Data Science Retreat covers a comprehensive, very intensive and practical training on using the most recent advances in the tools required to becoming a data scientist, machine learning engineer, and a very good understanding of big data handling (data engineering, distributed systems).
The instruction style is perfect; the instructors describe the main idea and let you struggle in finding the solution, which at the end, improves your ability to becoming independent and work on your own, being creative.
An important exercise at DSR is the portfolio project. Each participant has to come up with a product idea and implement it, which is challenging and fun. My mentor was very helpful in providing critical feedback on my deep learning project.
I have been in batch 8, which ended in December 2016. Since then, I am still in contact with DSR and they offer great support and recommendations to my job search.
Going through DSR, I am happy with my decision to join it. The staff, instructors, and other participants were all very helpful and in harmony.
What could have been done better?
- Focus more on making models for production in all the classes, such as machine learning pipelines, and enforce this through exercise from the early stages in the program.
- Provide more details on the job market for data scientists in Germany / Europe.
So I didn't have the patience or time to go back to uni and it was getting increasingly lonely doing online courses, and topics such as Apache Spark are simply not available in online courses. I now see why, it is quite fiddly to get it to run on your machine and so the instructors from the two sections we had on it (RDDs, dataframes and real-time/streaming) went through the set-up process. There were many great instructors in this course, again intensive training in NumPy and Pandas and some detail on the tricks within scikit-learn. The neural networks component was one thing I saw in this course for the first time, and am happy to have developed some proficiency with it. There was also Python environment management I've been dying to learn for quite a while since I damaged my system python once and had to reinstall everything. It is the proper way to do projects. There were a number of topics introduced with less detail such as big O notation and microservices which are important to know about and apply.
There were a lot of fantastic networking opportunities around DSR, with several alumni and succesful industry contacts sometimes joining for lunch or class.
I was a bit disappointed that in Germany no aspect of the course was in German and there were no German participants, but several of the instructors as well as the managing director are German. Still, it was a great group of people socially and professionally and I look forward to staying in touch.
Also, the process of converging on a project idea was more painful and less smooth than it could have been. It needs to be described in writing, it's like they think you can read their minds regarding what they are expecting. Hopefully they will fix that by the time you sign up for a course, and it is in any case a great opportunity to add something impressive to your online portfolio.
It is a very intense 3 months which was what I was looking for.
A refreshing experience that pushes you to explore further data science and create an ambitious portfolio project. You will undoubtly get a job at the end of the course.
During the bootcamp I personally felt the switch from learner to skilled practitioner, and it gave me great confidence in my abilities as a data scientist. I enjoyed the informal atmosphere and collaboration with my peer learners.
- Small batches (8 to 10 participants), good selection of participants (expected strong background in ML)
- DSR covers what online courses don't cover. Assuming you come well prepared (already know python and machine learning), it will boost your skills and make you employable
- Most teachers are extremely skilled and really know their area. Some might not be able to follow though, so expect to spend hours after class to make sure you absorbed all knowledge. Teachers are available outside of class hours to answer you questions.
- DSR claims to use the meerkat method but sometimes it sounds more like an excuse for lack of ability to teach of some of the teachers
- Still managed in a very artisanal manner, lacks consistency (e.g. teachers work independently and tend to cover the same topics, the sequence of topics is not always logical)
- Few teachers not up to the expectations for a course of that price
- I find the screening and selection process quite intensive. So, everyone attending data science retreat are more or less in an intermediate or advanced level. So, you learn a lot from your batch mates.
- Lecturers are experts in their respective field
- The regularly updated syllabus me
- Smaller batch size, in my batch it was 7
- Attracting practising data scientists to sharpen their skills ( had a batchmate, who was working as a data scientist in a company)
- Supportive staff, spacious place to work and discuss
Not sure pro or con-
- Pressure to come up with data product with value for a project portfolio.
- Meerkat method? am still thinking whether it was used or not
- I attended a recent batch which started in September 2016
- High standards for acceptance was great - meant we could move fast and learn a lot not only from teachers and mentors but also from other participants
- Great benefit is lots of teachers, each with their own speciality - you get cutting edge advice and material about things the teachers are using themselves in their full-time jobs
- High quality of mentors and teachers and support for the portfolio project
- Great curriculum and decent job support
- Not for everyone - Requires a lot of hard work to get the most of it, but a massive plus if you are very motivated - the course moves at a pretty decent pace and you are not spoon-fed how to do everything, it requires working hard on exercises and a hands-on approach. However, this means you cover a lot of ground and learn very quickly how to figure things out as fast as possible, as you would in a real job
I was a participant of a batch in 2016.
My overall expectation was to become an expert in predictive modeling.
But there my expectation was not clearly met, because I found out.
However, the hands on experience offered to me was really useful.
What we covered was Random Forest, SVM, Deep Learning.
In the end, there is no free lunch. There is no shortcut to master all the topics.
But all in all there is a lot of pointers to various directions.
So the total experience is to extend my horizons, and it amazingly did.
I now know where to look for further information, what is possible.
You get to know which cutting edge technology is being used by professionals and how they apply it.
It was a multiplication of knowledge in general and I am satisfied with the outcome.
I see the portfolio project as a very useful tool to associate what you learn with a application context. However, it will improvement the experience a lot more if the students don't need to concern with data engineering.
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