Data Science Retreat (DSR) helps coders or people with significant quantitative training (e.g. science, engineering, or math graduates) ramp-up quickly for a career in data science - arguably the fastest-growing, highest-demand profession. DSR is a 3-month, rigorous, and full-time/intensive course in the startup-capital of Europe: Berlin. Learn software engineering, data science, and business communication faster and more deeply with mentors doing code reviews plus pair-programming - all on real-world data and valuable problems. You will develop a portfolio project, demonstrating you can own a business problem, solve it, and communicate why your results are definitive.
Marketplace demands and needs have outpaced any educational programs. we are not seeing too many programs which really round out the entire package of skills needed. We think most of the even new data mining and analytics programs are back around the 2005 time period. Things are shifting so fast that unless you are learning all this stuff on your own, you will not be prepared for the job market in many cases.
You bring your training, tuition, and drive to master our curriculum alongside our world-class mentors and partners; Towards the end we provide a networking event with top-tier technology companies, where you will show off your new skills and portfolio project, likely leaving with a career-changing job.
Why go into a Data science career?
If you are looking for a career where your services will be in high demand, you should find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. So what’s getting ubiquitous and cheap? Data. And what is complementary to data? Analysis.
Prof. Hal Varian UC Berkeley, Chief Economist at Google, interviewed by Freakonomics
"A position in the industry, where I can use my teaching, data processing and analysis skills to further some business goal, seems like a much more preferable option than sitting around writing research papers and applying for grants all day. Not to mention that academia pays less and has worse overtime conditions than any industry job I could concievably get."
Geir Smestad, CompSci Masters Student
Co-founder & CTO of Datasalt. He’s core committer in two Hadoop-based open-source projects, Splout SQL and Pangool. Splout provides a SQL view over Hadoop's Big Data with sub-second latencies and high throughput. Pangool is an improved low-level Java API for Hadoop based on the Tuple MapReduce paradigm (ICDM 2012). Pere is an early adopter of Hadoop, working in Big Data projects since 2008. He’s also the organizer of Big Data Beers Berlin.
Mikio is co-founder and Chief Data Scientist at TWIMPACT, a startup working on real-time event analysis for all kinds of applications. He’s also the author of Streamdrill, a library that solves the top 10 problem. The top items for all trends are continuously updated from the data you send in. No need for iterative computation or big heaps of data to start the analysis. Mikio also wrote jblas, and is currently a PostDoc in machine learning at Technische Universität Berlin.
Adam is currently the Chief Data Scientist and Director of Engineering for Zanox, Europe’s largest affiliate network, where he supervises more than sixty people. He has been in technology roles for over 15 years in a variety of industries, including online marketing, financial services, healthcare, and oil and gas. His background is in Applied Mathematics, and his interests include online learning systems, high-frequency/low-latency data processing systems, recommender systems, distributed systems, and functional programming (especially in Haskell).
Marek is an assistant professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland. He has a PhD in computer science and his research interests focus on applied maths (aggregation theory, data analysis and mining, computational statistics, automated decision making, fuzzy sets and systems). An R enthusiast since R_1.4.0 and an author of best-selling Polish book on R programming. Loves teaching & sharing his knowledge and experience with others!
Konstantinos enjoys learning, teaching, researching and solving. Not necessarily in that order. He strongly advocates the need for a deep understanding of theory along with an extensive practical experience in order to be able to solve complex problems. He has a PhD in statistical signal processing and has held various research engineering as well as teaching positions. At DSR he is giving lectures on the applications of algebra, probabilities and statistics in data science and on the implementation of machine learning algorithms using software tools.
Jackie's interests were nurtured in the machine learning group at the MPI in Tuebingen where she worked on kernel methods and has since ventured to the probabilistic side using Bayesian modelling, and now sometimes even combines them. Her primary applications are neuroscience and image processing. She is currently at The Institute of Technology, Berlin and is putting the finishing touches on her PhD thesis about large-scale approximate inference in probabilistic models.
Tim is our R teaching assistant and data visualization person. With his background in economics, geography and social science, he deals with large and messy datasets on a daily basis. His ambition is to find intuitive ways to assess and visualize real-life data. In his PhD thesis he models the relocation behavior of people and examines the impact on the housing market and the demographic composition of regions.
Wait... but don't you need advanced math? Isn't 3 months too short to teach that?
Yes, you will need a solid background in linear algebra and probability theory to create new algorithms. But this is very different from what you need to simply apply algorithms known to work for a class of problems. Vision + good judgement + intuition + hacking skills + natural analytic skills + craftsmanship + curiosity + Google skills are, in fact, more useful and less expensive than advanced math knowledge.
I'm good at thinking analytically and using the scientific method. But I know nothing about software engineering/databases. Would this work for me?
Absolutely yes. Being comfortable with at least one programming language is a prerequisite, but if you have never put a system in production, written tests, or used version control, etc. such skills comprise software engineering and craftsmanship, and you will pick those up. You will simply spend more time pairing with advanced software engineers than others as needed.
How would you improve my communication skills? I hear they are key for a data scientist.
You will present your results in front of a non-technical audience four times, getting feedback by a professional trainer, with video review and tight timing. You will train in explaining complex ideas to other students. Because no matter how accurate your algorithm predictions are, if you cannot convince the decision makers in a tight time window, it will not have mattered.
- 7000 eur (payable 2333 eur/mo)
- Access to real problems and datasets from companies
- Access to our mentors
- Code reviews
- Portfolio project supervision
- Interview training
- Presentation and communication training with a professional trainer
- Portfolio project supervision
- Interviews with our hiring partners on hiring day
- Career support for three months after graduation; business network events. Hiring intros if needed
- Office with big monitors in Berlin
Think you can’t afford it? Don’t worry, apply anyway! We have scholarships and payment plans for high-potential candidates.
Where: Berlin, Germany, one of the world’s great cities, the upcoming “startup capital of Europe” that’s also affordable and brimming with software and startup activities.
When: Batch 02, Aug 1st to Oct 31st.
Class size: Ten to fifteen students will be accepted.
Our office: Microsoft Ventures offices, in Unter der Linden 17 (Mitte). One of the best locations in Berlin (Batch 01; Batch 02 location TBD).
We expect you to have basic programming experience and familiarity with databases. Exercises will be in R or python. You'd need to have a basic understanding of at least one of these languages. In the application form you can say whether you want to use a different one: Clojure/incanter and Julia could be the next languages we support.