Data science-retreat-how it works plus advice for upcoming data scientists
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The document describes a data science retreat program aimed at helping junior data scientists transition to senior roles. It discusses the challenges companies face in finding qualified data scientists and proposes that the retreat, which involves portfolio projects, mentoring, and pair programming, can help address this skills gap. Companies can sponsor candidates in the program, receiving discounts on their initial salaries if hired. The retreat director advocates this approach as a way for companies to develop strong relationships with candidates and assess their skills directly.
Data science-retreat-how it works plus advice for upcoming data scientists
- 1. Data Science Retreat Berlin, Mar 2014 http://datascienceretreat.com/ Introduction to the first Data Science school in Europe Plus advice for upcoming data scientists
- 2. Who Am I Twitter: @quesada Before: Consulting on predictive models of ecommerce (CLV), data scientist at GetYourGuide
- 3. What this talk is about: Two problems • Making the jump from junior to senior data science is hard (solution: data science retreat) • Acquiring the skillset, even with killer online courses, is hard (solution: meerkat method)
- 4. contents • Data Science retreat • The meerkat method • Data Science retreat for companies • Hiring • Getting tailored courses at your location • Advice to anyone on their path to be a data scientist • Advice to companies growing a data team
- 5. Problem: Making the jump from junior to senior data science is hard
- 6. It’s too hard for companies to find data scientists “It takes 150 phone interviews to find someone who is good enough to bring in to continue on-site” Alex Kagoshima, Pivotal, Berlin
- 7. People applying to Data scientist jobs have no experience • Vincent Granville: “There is no shortage of data scientists. For every linkedin Job, there are several hundreds applications on average”
- 8. Data scientists need to program (5 year experience) Stefan Schmidt (Amazon Berlin): “It takes us months to fill our positions; we hire world-wide for Berlin openings. Most profiles cannot program at the level we need. We have engineers, but the data scientist needs to be able to understand large projects and commit code”
- 9. Truth is, data scientist is a senior role • Often, advising to the CEO directly • This is why so many people with strong profiles and lots of coursera courses cannot find jobs
- 10. The gap from junior to senior • Junior: • Has a technical degree • Has done some courses online • Has never worked with data that generates value to companies • Can apply ‘recipes’, but not think creatively about data sources and algorithms
- 11. The gap from junior to senior • Junior: • Has a technical degree • Has done some courses online • Has never worked with data that generates value to companies • Can apply ‘recipes’, but not think creatively about data sources and algorithms This profile has no practical value for most companies
- 13. The story
- 15. Formulating the analytical problem • Finding the question • Translating something vague into a dependent measure and an actual set of predictors • What generates business value? • The Business Model Canvas to design a data product • Key performance indicators; examples, measurement, improvement • Most business problems are not very well defined. How do we make them actionable? • Analyzing big success stories in data science • Getting Buy-in
- 16. Getting data (APIs, feature engineering) • Using APIs • Using databases • Parsing html; web scrapping • Transforming data (reshape) • Finding APIs • Feature engineering • Avoiding autocorrelation • Removing features with low variance • Detecting outliers • Exploratory analyses • Measuring predictor importance
- 17. Finding insights, making predictions • Regression • Linear regression, penalized models • non-linear regression • SVM • K-nearest neighbors • regression trees + rule-based models (random forests)
- 18. Finding insights, making predictions • Classification • Logistic regression, linear classification • nonlinear classification models • Classification trees + rule- bases models (random forests) • SVM • K-nearest neighbors • naive bayes
- 19. R • R language fundamentals • data structures (including data.table) • subsetting • input/output • functions/control flow • vectorization • split-apply-combine advanced R functional programming in R Profiling object systems packaging Rcpp
- 20. R • advanced R • functional programming in R • Profiling • object systems • packaging • Rcpp
- 21. data at scale • MapReduce • MapReduce, Google 2004. • Applications, extensions. Beyond MapReduce. • Big Data analysis • Preparation and configuration • Hadoop cluster overview. • Practice: Uploading / downloading / moving files around, executing jobs, checking for completion / failure, etc.
- 22. data at scale • Hive / Pig • Defining a Hive table, querying a Hive table. • Integrating R with Hive. • An introduction to Pig. • Mahout • Executing clustering tasks. Visualizing the results with R. • Executing an item-to-item recommender. • Cascading / Pattern • Data flow modeling using PyCascading. • Executing Machine Learning "Pattern" algorithms.
- 23. Location: Microsoft ventures berlin
- 24. Methodology: portfolio project • Ten students per batch • Pair programming and code reviews with mentors (guild model) • Datasets come from companies (non-NDA only) • Portfolio project, where the fellow demonstrates what he can do end-to-end to deliver value • Weekly presentation training to improve communication to non-technical stakeholders (video feedback)
- 25. Who we are looking for • Passion for generating insights from data • Familiarity with trends in data growth, open-source platforms, and public data sets. • From familiarity to strong knowledge of statistical methods • Some experience with statistical languages and packages, including Mahout, R or python with pandas • Some familiarity with visualization software and techniques (including Tableau) • Preferably, experience working hands-on with large-scale data sets • Excellent written and verbal communications skills
- 26. Acquiring the skillset, even with killer online courses, is hard
- 34. Concepts in meerkat method • Learner • Mentor • Scorpion • Maiming the scorpion
- 37. advantages • No need to find the right tutorial/book/whatever • Spend more time at the border of your capability • You Save time doing exercises that would be too easy
- 38. Advantages (cont) • Higher project completion rates: all projects must have a concrete output, so you will see your own progress in tangible ways • You will have an Easier time to demonstrate progress to yourself and to others (the Mentor vouches for the Learner). • You will get more hands-on training than in other methods
- 39. Interested in being a learner? AppLY
- 40. Data Science Retreat for companies
- 41. Sponsors
- 42. Meet Stefan, he's the Chief data scientist of bigCompany. They wanted to go 'all in' with big data, but needed people capable of taming it.
- 43. He realized it's not easy to find Data Scientists.
- 44. What do you mean ‘sub- second queries by Monday’?
- 45. What do you mean ‘improve predictions two standard deviations’?
- 46. €?
- 49. Then Laura, Stefan’s friend, pointed him to Data Science Retreat … an intensive course helping selected fellows ramp-up fast for a career in data science. “Tell me more…”. Stefan was very interested.
- 50. Stefan could interview ten data scientists that were as good as Ben. He hired three, an they jumped into their roles with little training. Stefan was Ecstatic!
- 51. How It works • As a sponsor you pay 7000€ in advance + 3000€ after the data scientist worked on-site for 3 months and you know you want to keep him. • students who take the sponsorship agree to work for a reduced salary (50%) the first 3 months. The salary savings during the internship should cover the cost of the sponsorship. When the students finish the program, no one has any obligations.
- 52. • You prepay 7000€ and become a sponsor. • At this point, you don't know the students. • But as a committed sponsor you participate actively during the retreat, see the student's presentations, go out for lunch with them, etc • Thanks to these activities, you have now developed strong relationships and know more about the students than what would come out in interviews. How It works: an example
- 53. How it works, an example (contd) • You have set your target on a killer candidate: Klaas. You make an offer, he accepts, and he starts working at your location • Klaas gets paid 50% of his negotiated salary. If Klaas’ 60000€/year, that is a 5000€/mo cost, and produces 2500€ * 3 months = 7500€ savings, which covers your initial investment of 7000€.
- 54. Data Science Retreat Contact: Jose Quesada, PhD, Director, Data Science Retreat Berlin jose@datascienceretreat.com DO you want to be a sponsor?
- 55. Advice to anyone on their path to be a data scientist • Try to find a mentor • Spend as much time at the border of your ability • Practice communication • Having a culture that can integrate such individuals is as hard as finding them. Interview your companies • How do you move from being a junior person to being the 'CEO wisperer'? Spend time with people who are
- 56. Getting tailored courses at your location • We listen to the people you need to train before we design the course • We will start with a dataset that is important for your company. Lacking that, we’ll bring a public that is relevant • Enterprisey courses are supposed to be non-effective
- 57. Hire somebody who’s better at engineering and teach him data science or hire somebody who’s better with data and teach him engineering? • Is your culture ready? Because if you manage to attract someone senior enough, they will sense if it's not • The problems you have must be a good match for the data scientists. People are extremely specialized, more so after PhDs. If you have say graph theory/recommendation problems, and hire someone with a time series background, things will take a while no matter who good he is in his field
- 59. He’s optimistic about this new big-data deployment!
- 60. Thank you for your attention Jose Quesada: jose@datascienceretreat.com @quesada @datascienceret
- 61. Sponsors