Being Berlin based, and a cornerstone of the Data Science community, Data Science Retreat has been a pretty regular feature of our coverage. We covered their launch back in April last year, the success of their early batches, and some of the student projects. Jose Quesada, DSR’s founder and director, has an interesting story himself.

Need help asking the right questions?

DSR’s existing offering, a 3 month ‘bootcamp’ style Data Science program, is aimed at providing practical experience and guidance for aspiring data scientists. Students are guided through the course by a host of industry experts acting as mentors, producing portfolio projects in the run up to the climax – demo day. The key points of the program involve learning to ask the right questions, and building experience with various aspects of machine learning, with a focus on Python and R languages.

Looking to build scalable data products?

Adding to the Data Science program is a brand new Big Data Engineering program. For candidates looking for guidance on designing data products that scale gracefully (using streams, for example), and getting to grips with Apache Spark, this is for you. Rather than Python and R, this program focuses on Python and Scala. There is a much greater focus on building products and putting algorithms into production.

With a 5% acceptance rate to the course, it is clear there is demand from young professionals looking to add a real qualification to their resume – and with an 86% success rate of finding a career afterward it seems to work!

Their next batch starts on September 16th! Find out more on their website.

(image credit: Jörg Schubert)

This week, a wealth of industry experts shared their insights into the changing landscape of big data with us. On Monday, Chairman of MBN Solutions Paul Forrest shared his thoughts on how big data can become “the bridge” to success. On Tuesday, Jamal Khawaja informed us why we’ve never been more vulnerable to data hacks and breaches, and what we can do about this. We also spoke to one of the co-founders of Mutinerie about the fast-paced life of coworking spaces. On Wednesday, Philip Berliner shared with us his incendiary and insightful polemic “Social Media is Dead. Big Data is on Life Support.” Here’s our picks of the best big data stories of the week:

TOP DATACONOMY ARTICLES

How Facebook Deal With Their Masses of User-Generated Data

For decades, companies have lived by the mantra “customer is king”. But in the age of the Internet- when users generate hoardes of data, not all of which is useful or accurate- the rules of the game have changed. We recently spoke to Tye Rattenbury, Trifacta’s lead Data Scientist, about how he dealt with the masses of user-generated data in his previous role at Facebook, as well his current role with Trifacta.

The Most Interesting Man in Data Science

From apple grower to fine arts student, from software developer to machine learning PhD- Jose Quesada has done it all. Now, he’s established Data Science Retreat, a course to help people with his passion for growth and development to delve into the world of data science. We recently spoke to Jose about his remarkable story, the Data Science Retreat experience, and why so-called “soft skills” are often the making of future data scientists.

How We Can Use Data Mining to Fight Corruption

“Last year, Transparency International Georgia launched an open-source procurement monitoring and analytics portal, which extracts data from the government’s central e-procurement website and repackages it into user-friendly formats. Users can now generate profiles of procurement transactions made by government agencies, profiles of companies bidding for contracts, & search aggregate statistical data on government spending.”

TOP DATACONOMY NEWS

Mario Gets Self-Aware with Application of Artificial Intelligence                                                                                                

Researchers of the Cognitive Modelling Group at Germany’s University of Tubingen have developed the Mario AI Project wherein a self aware Mario who makes decisions based on what it learns through spoken instructions or concepts and by exploring his environment.

Stack Exchange Gain $40m to Become to Sole Platform That Matters for Dev Hiring Companies

Stack Exchange the startup behind the popular Q&A platform for professional and enthusiast programmers, Stack Overflow, has secured $40 million in investment in a Series D round of funding, it revealed earlier this week.

Facebook Open Sources Deep Learning and AI Tools on Torch

“Facebook in an unprecedented move has open-sourced some of its machine learning tools with the scientific computing framework,Torch. The announcement came earlier last week on Friday, through the Facebook AI Research (FAIR) blog.”

TOP UPCOMING EVENTS

2-3 February, 2015- 14th Wearable Technologies Conference, Munich
     

“The world’s most profound event for wearables will once again gather all important players of the wearable tech ecosystem at the 14th WT | Wearable Technologies Conference in Munich on February 2 and 3.”  

11-12 February, 2015- Big Data & Analytics Summit, Melbourne

“Big Data & Analytics Innovation is back in Australia for two days of inspiring, insightful & educational presentations, panel sessions, interactive discussions and world-class networking. Big Data & Analytics Innovation will bring you right up to speed to assist you with your every need covering an array of topics, themes and problem points.”

TOP DATACONOMY JOBS

Big Data Solutions Architect, adsquare   

This is truly a chance of a lifetime. At adsquare you will be part of a rapidly growing ad tech startup that will add a totally new dimension to the world of mobile advertising. You will work hand in hand with the adsquare team on understanding the real-time, real-world user context. If you are enthusiastic about BIG DATA processing, think analytical and love distributed backend systems with state of the art frameworks you shouldn’t miss out on this opportunity.

Physicist / Mathematician / Computer Scientist as Data Scientist, Blue Yonder

If you would like to be part of a highly innovative, challenging and extremely future-oriented software market, and a young and highly motivated team, then please send us your detailed application.

From apple grower to fine arts student, from software developer to machine learning PhD- Jose Quesada has done it all. Now, he’s established Data Science Retreat, a course to help people with his passion for growth and development to delve into the world of data science. We recently spoke to Jose about his remarkable story, the Data Science Retreat experience, and why so-called “soft skills” are often the making of future data scientists.

Give us a brief introduction to you & your work at Data Science Retreat.

I love developing people. When I see the ‘delta’ on a person after hard work, it makes me feel good. Data Science is ‘the’ place for this ‘huge delta’ development: because the state of the art is changing rapidly, you are forced to teach yourself new things every week just to stay current. Fields like this tend to attract people who like pushing themselves.

I came from a rural background. My father grew apples, and would expect me to do the same. Instead, I studied psychology and fine arts. Then I did a PhD with lots of machine learning. In it I developed a software system to teach pilots how to land commercial aircrafts without the need of a senior instructor sitting next to them (which I didn’t patent; silly me).

What you can see is that I changed direction many times; I taught myself mostly all I know that is really useful. I think we live in self-taught paradise. But after a certain level of excellence, it’s hard to make progress. This is something most aspiring data scientists find. No matter how many MOOCs you do, there’s a barrier that very few people ever break.

This is why Data Science Retreat started. I think I know how to create an environment where you can go “faster than average self-taught speed” and break the barrier of excellence that most people encounter. I asked myself: “What does it need to exist for this to happen?”. My answers was: you need to have access to ‘chief data scientist’-level people, contributors to leading open source packages, etc, and they need to be invested in your progress. You need to be surrounded by other people seeking excellence, too. DSR is the kind of setup that I wish I had when I started. Two batches later, all I can say is that I’m very proud of the result, as is everyone involved.

Talk us through the course structure.

You can check the instructional part online in our curriculum. What you don’t see there is how we approach the ‘portfolio project’, where you do original work under mentors.

We start with finding a good question. This is a creative process, and a skill you will use often once you graduate. Not all questions are answerable with data and machine learning. Of those which are answerable, not all of them produce business value. Once you know you have a good question, finding the data that can be challenging. Or cleaning it. Or making sure it’s correct.

Next step, you find a good evaluation metric (‘How do you know when you’ve won?”), and start iterating with your predictive models. When to stop fine tuning parameters is also a key skill; you will hit diminishing returns eventually.

Once you have demonstrated you answered the question you started with, it’s time to present your results and make a convincing case in front of stakeholders. Here, your communication skills determine everything: your beautiful product may never get put in production if you don’t do this well. You’ve exercised your communication skills quite a bit already by settling on the question; ideally the company is receptive, and was sold on the value. Do they believe you have generated that value, now that you’re finished?

At all times, you could have asked different mentors. You got around 270hrs of instruction on state-of-the-art methods. But let’s be honest: anything can happen here. It’s stressful. You are at the helm managing your project. Often you find your data has nothing going on for it, your predictive models are not doing anything interesting, or you cannot answer the question. You are back at square one. And there’s a hard deadline where companies will sit and look at you with their undivided attention.

What differentiates Data Science Retreat from other courses for aspiring data scientists?

1. Our mentors are at the ‘chief data scientist’-level or contributors to leading open source packages. All our mentors teach, and they are invested in your success. There’s nowhere else in the world you can get this today.

2. We focus on the question as much as on the technical details of the solution. We provide training on technical communication; you will present often, and get one-on-one feedback from a communication expert.

3. We prepare our participants for leadership positions. That is, either being the lead data scientist, or the only one in the company. This is far harder than preparing someone to join an existing group of data scientists and solve problems picked by someone else.

Why did you choose Berlin as the HQ?

There are two hotspots in the EU: Berlin and London.

Berlin has been doing really well in the last five to ten years with regards to Internet tech startups. When you look at figures in terms of size, how many VC-backed companies there are, how much venture funding flows into those companies etc. As a result the tech scene is huge in Berlin, there’s an interesting meetup almost every day.

London is also very interesting. There’s definitely money floating around because of so many banks. But tech-wise, choices are more conservative. If you are a bank, losing information, even if it’s a single transaction, is a big no-no. You have to stick to tried-and-true technologies. Berlin companies can afford to pick riskier, newer technologies, because they often deal with consumer-level information, which is usually not as crucial. If Twitter loses a tweet, it is unlikely they will get sued, unlike a bank. I suspect Berlin is already ahead of London tech-wise, and with time this difference will only grow. This is a good thing for data science, because companies who can take risks will use data scientists sooner than conservative companies.

What do you consider to be the main differences between the data science scenes in the US & Europe?

There’s of course a lot more VC money in the US, and this makes it easier for companies who use data science to exist. There are more web-scale, B2C companies in the US. 40% of the data science jobs are in the valley according to LinkedIn. And the pay is higher over there. So what’s to like about the EU?

• Since there’s less VC money floating around, companies doing well in EU (and hiring) are more likely to have solid business models (and be resilient to big changes in the economy).
• EU companies are less prone to follow fads.
• EU companies tend to offer better working conditions, even if salaries may be lower. Retirement, health insurance etc are all well covered by law. You get a full month of vacation. If you are on a high tax bracket, at least you know your money is not used for say fueling the military industry.
• There’s something to be said about being early days for data science in EU. There are better opportunities for truly outstanding people. From what I hear, there are in the order of 100 people able to do a good job at lead/chief data scientist in the entire EU. If you are one of them, or can imagine to be one shortly, you are clearly in a privileged position.

Still, I worry about EU competitiveness mid-term. Some companies are too traditional, and have trouble integrating data scientists in their structures. But this is a topic for another day 🙂

What are the essential skills and traits a data scientist must possess?

There are three must have skills to just enter the data science space. You need to know some programming of some kind preferably R or Python, but really any programming language will do. The second thing is that you absolutely must know some statistics and machine learning. This shouldn’t be a superficial understanding of these data analysis techniques – any programmer can blindly implement a technique as a black box. You need to actually understand why a particular technique is suitable and what its limitations are. Finally, you need to know how to query databases.

Different data scientists will have different strong suits. Some will be very strong with data visualizations, some with databases and others with statistics but all data scientists need to have these basic skills to work in this space.

We do not run coding tests, because nowadays with sites like stack overflow, it’s easy to write almost anything without really understanding the details. We consider coding tests non-discriminative. We do like to see code samples on github for existing projects.

We invite the most promising applicants to an interview. There, we make sure we are a good match for each other. There are questions about creativity with data, communication, and raw machine learning knowledge. We want to see people who have put the effort to learn this stuff on their own. Many interviews end early.

You’re currently accepting applicants for your third class; what level of prior knowledge do your candidates typically have?

You only need to know at least one programming language well. Other than that, there are no real prerequisites. We have applications from people who are already data scientists, but feel they are stagnating at work. Initial skillsets are all over the place, which makes is challenging (and fun!) to prepare the teaching. As you can see, the curriculum is very varied, and no participant has had experience in more than one or two topic groups.

A big chunk of people applying have been in the industry for years, and/or have PhDs. But I’ve seen many people with no experience, who weren’t so strong on paper, but ended up doing incredibly well during DSR. The sheer willpower and raw intelligence of some participants has been inspiring. I’m happy my interview process detected these people and let them in! I wish more and more people applied even though they felt intimidated by DSR’s reputation; if you know you have it in you, and have a burning passion for data topics, by all means apply! We tell you whether you are accepted the same day of the interview. If you are on the fence, I’ll encourage you to go for it; this batch we are hosted by Zalando, which is a great place because they have around 40 data scientists working already (two DSR alumni!).

Follow @DataconomyMedia

(image credit: See1,Dot 1, Teach1)

In Europe, startups and established corporations alike are tapping into the power of data science. However, putting the right team together to your business’ data sciences problems remains challenging, as the talent scarcity continues. This is, of course, good news for data scientists; those who combine expertise in machine learning & big data technologies with business acumen can expect to play a central role in an organisation’s decision-making process, and start an exciting career in a rapidly evolving industry.

Graduates of quantitative disciplines like engineering or the natural sciences will find alot of their current expertise overlap with the skills needed to become a data scientist. Blog posts, online-courses and tutorials can provide a strong foundation for transitioning to data science, though most people who choose this route reach a sticking point. To progress further, practical experience becomes crucial; it’s one thing to learn these new skills, but it’s quite another to apply them to unique problems and challenges as you would in the workplace. MOOCs have undoubtedly been instrumental in bringing education to the masses, but to fully master a field as multi-faceted and nuanced as data science, it helps to shed the anonymity. Hands-on experience, overseen by a mentor who can identify your particular strengths and weaknesses, offers huge benefits. Particularly in terms of time; with hard work, expert support and a solid background in mathematics, statistics and programming languages the transition from rocket science to recommendation engines can be a matter of just a few months.

2014 saw high-quality training programmes flourish, particularly in the US, but Europe was largely left playing catch-up. This is where Data Science Retreat comes in. Started by former Chief Data Scientist Jose Quesada, PhD, DSR is on a mission to train passionate data scientists from all over the world. Since the spring of 2014, he and his team have been working on a rigorous and intensive 3-month training program in Berlin, that recruits its participants from all around the world.

Each class involves a small number of select participants, who attend classes taught by chief data scientists, and work to solve real business challenges under expert mentorship. The curriculum spans everything from big data technologies to business communication, with the hope of setting up the pupils with a skill set covering every key aspect of work as data scientist. During the course, students also work on their own portfolio projects, which they present to a number of German and international companies at a hiring-day at the end of the program. Previous participants’ projects include a recommendation engine for flatshares & the Berlin real-estate market, a predictor for the outcome of art-auctions and the location of crime-scenes in urban areas.

The program has also been well-received by the hiring companies: more than 86% of the participants found the job they wanted within 3 months of their completion of the program, with 60% of participants getting multiple job offers, and having the freedom to pick.

The next 3-month DSR session will begin in early February 2015 and will be hosted at the German online retail giant Zalando, home to a team of more than 40 data scientists.

Are you a budding data scientist who is looking to specialize and excel in the data science field? Applications are still being accepted if you are interested in being a part of Data Science Retreat batch 03!

Follow @DataconomyMedia

(Image credit: Data Science Retreat)

Data science training programs have been rapidly increasing across the United States recently, and they seem to be spreading to Europe as well. An inaugural class of 10 students will begin a three month training program called the Data Science Retreat in Berlin, expanding on the knowledge available from online courses such as Coursera and Udacity to equip participants with practical applications of how to handle data. The retreat will challenge and engage participants with interactive projects and problems.

According to an interview of Jose Quesada, the program director, with VentureBeat, “Knowledge you can get from books, tutorials, blog posts, attending Coursera courses, and so on, but when you try to apply that knowledge, there is something missing.”  In the United States programs like Quesada’s are becoming more common, with data science programs at universities and programs including Zipfian Academy and the Insight Data Science Fellows Program, while in Europe, Science to Data Science and Persontyle‘s courses are in their nascent phases.

Of the mentors on the retreat, Quesada says: “I want to get only people who have been there in the trenches and have the credentials” so that students can learn to identify potential pitfalls early on and tackle problems head on. The training will include Python, R, D3.js, Hadoop, and Spark. It will also give ideas on how to communicate ideas about big data to those who are less well versed in the topic, such as higher ups.

Unlike other retreats, namely the Hacker Retreat, the Data Science Retreat will pay its mentors. It also offers a program for the students to have sponsors for their tuition, for whom they would then work after graduation for some time. According to VentureBeat, ‘one sponsor of the Data Science Retreat, Microsoft Ventures, will provide the space for in-person meetings in Berlin’s Mitte neighborhood. MapR and the Unbelievable Machine Co. are also on board as partners.’ So far 10 applicants will be participating in the retreat, half of them Ph.Ds. And Quesada and Kai Wu, another Retreat organiser, have high expectations for their graduates – they see companies like Amazon in great need of data scientists across the globe, and would be happy for their graduates to fill those roles.

The orignial article and interview were first published on VentureBeat

(Image Credit:  Yann Gar)