Data Science Retreat

I attended (and graduated) from the very first batch of Data Science Retreat (DSR) which took place in Berlin from the beginning of May to end of July 2014 .
Since I lot of time has passed since I graduated from this bootcamp / course, I think I can now properly asses the value that DSR added to my own career. Furthermore, since I stayed in contact with the bootcamp and the students which attended several of the later batches, I think I can also judge the development of the bootcamp over time.
Overall, DSR helped my own career tremendously in several ways. My salary increased significantly after graduating and finding a new job and continued to increase in the years afterwards as the skills (many acquired at DSR) were in high demand. From my experience, this was true for most graduates of my batch as well as later batches.
The curriculum of the first DSR batch was very relevant for my work after graduating and DSR also helped me to prepare for finding a job and negotiating a better salary etc. Many of the skills I learned at DSR are still relevant for me nowadays. A lot of the technical skills / knowledge is out of date now, but this normal for any technical profession, especially for a relatively new discipline as data science. At the same time, I think DSR made good adjustments to its curriculum to mirror the changes taking place in the industry, e.g. switching the main programming language from R to Python.
All instructors at my own batch were quite good and had relevant industry experience. Our main instructor is now the Chief Data Officer at a prominent German FinTech company. In future batches, the quality of the instructors increased even more as the bootcamp became more well known and the instructors gained experience in teaching the same material over time.
As my batch was the very first batch of DSR, we witnessed some growing up pains especially regarding the organization of the bootcamp, but this improved significantly over time and several years later DSR seemed to run like a well-oiled machine.
DSR also helped my career by providing an excellent opportunity to network with professionals from my and later batches, instructors and all kinds of other relevant industry people. I used my connection to people I met through DSR several times to find solution for some technical problems or further my career. People associated with DSR are - for the absolute most part - also very nice folks to know and I am glad to say I made some good friends through DSR :)
Again, overall I can wholeheartedly recommend DSR for people who have a relevant background & are looking for (and are willing to learn) a lot of new skills which will help them in their future data science career.

I was in the first batch of data science retreat. I thought the course was solid and it helped me get a career in data science, and later software engineering. The atmosphere in the group was nice, and I made some friends whom I'm still in touch with. I also thought that the level and motivation of everyone was very high. The curricilum was quite intense with a balance of linear algebra and hands on data science. The teachers were passionate and mostly knowledgable. There were some session with industry people, which were interesting. So all and all I thought it was a good experience.

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.

Overall for me it was worth it.    Curriculum - the bootcamp begins with lectures with best practices for Python, NumPy, Pandas, Statistics etc. goes through Machine Learning Fundamentals (basics, Decision Trees, Time Series), Deep Learning (Computer Vision, NLP, Transfer Learning) and ends up with practical lectures around Debugging and Deployment. It also has useful sessions for Communications and also Job Search (in our batch both lectures were very good).  It is a good idea to take a look at the curriculum page and what to do before the bootcamp (https://datascienceretreat.com/curriculum/). Also, if you have the chance, do an introductory course for deep learning. A good advice I got before the bootcamp "The more you do before the course, the more you will get out of it"🙂    Teachers - some were amazing, others less so.    The price in my opinion is high. However, one can apply for a financing program with Chancen eG.    Feedback is asked in regular intervals and is indeed used to improve the bootcamp. It is important also to give feedback while you are attending, as it is often acted upon.    To sum up - I met some awesome people & professionals -  teachers, organizers and fellow bootcampers - and with some of them we are still in touch. I am grateful to them for the knowledge, experience and cool things we created!

I graduated Data Science retreat in Berlin in 2016 and had a very positive experience. Like most other students, I was trying to change my career from a related quantitative field into data science and machine learning and Data Science retreat was the perfect help to accomplish that.

About half the program was spent on lectures from experts in different fields from boosting algorithms to image recognition. Most teachers were practitioners and the quality of their courses was very good. Thanks to the small class size, you could always get help with your specific problems and questions.

The other half of the time was spend on developing an individual "portfolio project". You could freely choose what project to work on and were responsible for the outcome. Of course, you could ask the mentors for help every time.

At the end of the bootcamp the students presented their projects to an audience of potential employers. I got several interviews out of this and an offer at a startup which I accepted.

The Data Science Retreat offers a comprehensive curriculum from industry experts who apply machine learning in their every-day jobs. It provides academic basics which are brought into practical perspectives with examples and exercises, commonly from the teacher's professional fields (unlike what MOOCs can offer).

This is definitely the course for those who want to maximise their development in the field of Data Science. You will be best prepared if you bring: i) at least basic coding skills in Python, ii) at least a basic understanding of the fundamental ML concepts (e.g. An Introduction to Statistical Learning by Gareth James + basics in ANNs), iii) enthusiasm to learn about new statistical concepts.

The course is what you make it. It provides you with a multitude of highly relevant and exciting topics as well as with the opportunity to deep-dive into each one of them. However, you choose how much you would like to deepen your knowledge in specific topics.

I myself took the most out of my portfolio project which forced me to revise and deepen specific topics as well as my coding. I worked with Adam on the prediction of marginal CO2 emissions from the Australian energy grid in order to reduce CO2 emissions. This kind of practical hands-on experience on a highly relevant industrial issue is difficult to get from online courses.

Although I know that DSR currently puts a stronger focus on it, one little thing that I found missing in my batch 19 was an enhanced focus on model deplyoment.

An excellent, comprehensive course in the fundamentals of deep learning and applied statistics that provides students with the skill and mindset that ML experts and data scientists require to address real-world problems.  DSR draws from the expertise of its diverse teachering faculty, comprised of professionals and academics who are seasoned practitioners of data science in the modern workplace.  In parallel with intensive coursework in prediction modeling, neural networks, and cloud computing through python-based coding instruction, students learn best practices and identify one problem applicable to machine learning which they work together to solve, under the guidance of instructors, by the end of the course.

A wonderful, very educational experience, where I vastly improved not just my data science skills, but also my math and programming (and this I say as someone with over 10 years professional experience in the software industry.)

PS I wrote up my experiences at DSR in detail over on Towards Data Science, so if you'd like a deep look at what day-to-day life is like at this bootcamp, I'd recommend you take a look at that article.