Managing Data Science Projects
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My slides from the MASTODONS (big data) workshop of the CNRS. http://www.cnrs.fr/mi/spip.php?article631&lang=fr On defining and managing the data science ecosystem at Université Paris-Saclay. Challenges and tools.
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Managing Data Science Projects
- 1. 1 Center for Data Science Paris-Saclay DR / CNRS LAL & LRI CNRS & University Paris-Sud BALÁZS KÉGL Pr / UPSud LRI CÉCILE GERMAIN MdC / Telecom ParisTech LTCI ALEXANDRE GRAMFORT MdC / Mines ParisTech CGS AKIN KAZAKÇI Pr / ENSAE Laboratoire de Statistique ARNAK DALALYAN
- 2. 2 Meta
- 3. 3 I will not talk about science
- 4. 4 I will talk about management (of) (data) science
- 5. Center for Data Science Paris-Saclay • My eight-year of experience interfacing between high-energy physics and data science • Our one-year experience of running PSCDS • Extensive discussions with management scientist and the MI/Mastodons/MaDICS for the last year 5 WHERE DOES IT COME FROM?
- 6. Center for Data Science Paris-Saclay6 UNIVERSITÉ PARIS-SACLAY 19 founding partners
- 7. Center for Data Science Paris-Saclay UNIVERSITÉ PARIS-SACLAY 7 + horizontal multi-disciplinary and multi-partner initiatives (“lidex”) to create cohesion
- 8. Center for Data Science Paris-Saclay8 Biology & bioinformatics IBISC/UEvry LRI/UPSud Hepatinov CESP/UPSud-UVSQ-Inserm IGM-I2BC/UPSud MIA/Agro MIAj-MIG/INRA LMAS/Centrale Chemistry EA4041/UPSud Earth sciences LATMOS/UVSQ GEOPS/UPSud IPSL/UVSQ LSCE/UVSQ LMD/Polytechnique Economy LM/ENSAE RITM/UPSud LFA/ENSAE Neuroscience UNICOG/Inserm U1000/Inserm NeuroSpin/CEA Particle physics astrophysics & cosmology LPP/Polytechnique DMPH/ONERA CosmoStat/CEA IAS/UPSud AIM/CEA LAL/UPSud 250researchers in 35laboratories Machine learning LRI/UPSud LTCI/Telecom CMLA/Cachan LS/ENSAE LIX/Polytechnique MIA/Agro CMA/Polytechnique LSS/Supélec CVN/Centrale LMAS/Centrale DTIM/ONERA IBISC/UEvry Visualization INRIA LIMSI Signal processing LTCI/Telecom CMA/Polytechnique CVN/Centrale LSS/Supélec CMLA/Cachan LIMSI DTIM/ONERA Statistics LMO/UPSud LS/ENSAE LSS/Supélec CMA/Polytechnique LMAS/Centrale MIA/AgroParisTech machine learning information retrieval signal processing data visualization databases Domain science human society life brain earth universe Tool building software engineering clouds/grids high-performance computing optimization Domain scientistSoftware engineer datascience-paris-saclay.fr Center for Data Science Paris-Saclay A multi-disciplinary initiative to define, structure, and manage the data science ecosystem at the Université Paris-Saclay http://www.datascience-paris-saclay.fr/
- 9. Center for Data Science Paris-Saclay DATA SCIENCE 9 Design of automated methods to analyze massive and complex data to extract useful information
- 10. Center for Data Science Paris-Saclay10 CENTER FOR DATA SCIENCE = DATA CENTER We are focusing on inference: data knowledge Interfacing with HPC, cloud, storage, production
- 11. Center for Data Science Paris-Saclay11 PARAMETERS • 2 years:April 2014 - June 2016, 1.2M€ • +1 year, conditional on evaluation • Light management • executive committee of 17 members • work groups • management, strategy (around objectives) • thematic (around scientific themes), • open to everyone to propose and to participate
- 12. Center for Data Science Paris-Saclay Domain science energy and physical sciences health and life sciences Earth and environment economy and society brain 12 THE DATA SCIENCE LANDSCAPE Data scientist Data trainer Applied scientist Domain scientistSoftware engineer Data engineer Data science statistics machine learning information retrieval signal processing data visualization databases Tool building software engineering clouds/grids high-performance computing optimization
- 13. Center for Data Science Paris-Saclay • Manpower • especially at the interfaces • industrial brain-drain • Incentives • data scientists are not incentivized to work on domain science • scientists are not incentivized to work on tools • Access • no well-developed channels to identify the right experts for a given problem • Tools • few tools that can help domain scientists and data scientists to collaborate efficiently 13 CHALLENGES
- 14. Center for Data Science Paris-Saclay TOOLS 14 We are designing and learning to manage tools to accompany data science projects with different needs
- 15. Center for Data Science Paris-Saclay TOOLS: LANDSCAPE TO ECOSYSTEM 15 Data scientist Data trainer Applied scientist Domain expertSoftware engineer Data engineer Tool building Data domains Data science statistics machine learning information retrieval signal processing data visualization databases • interdisciplinary projects • matchmaking tool • design and innovation strategy workshops • data challenges • coding sprints • Open Software Initiative • code consolidator and engineering projects software engineering clouds/grids high-performance computing optimization energy and physical sciences health and life sciences Earth and environment economy and society brain • data science bootcamps • IT platform for linked data • annotation tool • SaaS data science platform
- 16. Center for Data Science Paris-Saclay16 POSTDOCS, THESES, SABBATICALS • Common selection criteria • scientific quality • expected results both in domain science and data science • relevance and feasibility • (real) scientific data, available at the start of the project • interdisciplinarity (PIs both from domain and data sciences, different LABEXs) • community building • organizing and participating in thematic days, bootcamps, workshops
- 17. Center for Data Science Paris-Saclay17 ENGINEERING AND CODE CONSOLIDATING PROJECTS • No research • implementation, maintenance, integration • 1 year engineering projects • 3-6 month code consolidating projects • a postdoc or PhD student drops research during the project, implements his/her research code in a professional software, or integrates it into a toolbox
- 18. Center for Data Science Paris-Saclay • A window to open data at Paris-Saclay • We are not storing or handling existing large data sets • Rather indexing, linking, and mapping, embedding in the worldwide linked data (RDF) ecosystem • Storing small data sets of small teams is possible • Subsets of large sets for prototyping • Or simply store metadata plus pointer 18 IT PLATFORM FOR LINKED DATA http://io.datascience-paris-saclay.fr/
- 19. Center for Data Science Paris-Saclay IT PLATFORM FOR LINKED DATA 19
- 20. Center for Data Science Paris-Saclay20 BOOTCAMPS • Single-day coding sessions • 20-30 participants • Goals • training PhD students, postdocs, engineers, senior researchers for hands-on data science (problem types, tools) • solving (prototyping) real data science problems • networking, knowing each other
- 21. 21 BOOTCAMPS
- 22. Center for Data Science Paris-Saclay22 BOOTCAMPS
- 23. 23 BOOTCAMPS
- 24. 24 BOOTCAMPS
- 25. Center for Data Science Paris-Saclay • A data challenge is a recently developed unconventional dissemination and communication tool • a scientific or industrial data producer arrives with a well-defined problem and a corresponding annotated data set • defines a quantitative goal • makes the problem and part of the data set (the training set) public on a dedicated site • data science experts then take the public training data and submit solutions for a test set with hidden annotations • submissions are evaluated numerically using the quantitative measure • contestants are listed on a leaderboard • after a predefined time, typically a couple of months, the final results are revealed and the winners are awarded 25 DATA CHALLENGES
- 26. Center for Data Science Paris-Saclay • The HiggsML challenge on Kaggle • https://www.kaggle.com/c/higgs-boson • 1785 teams, huge publicity • significant improvement on baseline • 18 month preparation, yet partially missing the target 26 DATA CHALLENGES
- 27. Center for Data Science Paris-Saclay • Challenges are useful for • generating visibility in the data science community about novel application domains • benchmarking in a fair way state-of-the-art techniques on well-defined problems • finding talented data scientists • Limitations • not necessary adapted to solving complex and open-ended data science problems in realistic environments • emphasizes competition 27 DATA CHALLENGES
- 28. 28 DESIGN AND INNOVATION STRATEGY WORKSHOPS
- 29. Center for Data Science Paris-Saclay • Putting domain scientists, data scientists, and management scientist in the same room • Getting them understand each other • Keeping them collectively creative • The goal: identifying and defining projects • low-hanging fruits • breakthrough projects • long-term vision 29 DESIGN AND INNOVATION STRATEGY WORKSHOPS
- 30. Center for Data Science Paris-Saclay innovative design = interaction and joint expansion of concepts and knowledge 30 DESIGN AND INNOVATION STRATEGY WORKSHOPS C/K design theory
- 31. Center for Data Science Paris-Saclay31 DESIGN AND INNOVATION STRATEGY WORKSHOPS [P]$Project$ building! Ini3alisa3on$ [K]$Knowledge$ sharing$ Workshops$ [C]$IFM?Design$ Workshops$ [RUN]! DKCP process: linearizing C-K dynamics
- 32. Center for Data Science Paris-Saclay32 TAKE HOME MESSAGE NO1 If you are interested in adapting any of these tools in your project/site, feel free to contact us, we would be happy to share our experience
- 33. Center for Data Science Paris-Saclay • We need data engineers and trainers to support research • ideally: 75% research scientists, 25% research engineers • We are lucky in France that the position exists in public research • Tasks • integrating research code into general-purpose professional software (e.g., scikit-learn,Torch) • providing an interface between computational infrastructure (e.g., clouds) and scientists • training scientists to use the tools 33 WHAT CNRS CAN DO
- 34. Center for Data Science Paris-Saclay34 WHAT CNRS CAN DO Incentives
- 35. Center for Data Science Paris-Saclay35 WHAT CNRS CAN DO Most data scientists, as other scientists, are trained and incentivized to do research on highly specialized domains. They search scientific visibility in their international community, which is equally highly specialized, because their carrier advancement is almost entirely based on peer-reviewed publications. Even when they would have the expertise, they have little incentive to venture into the tool builder (data engineer) role since software authorship has little value in their evaluation, and it can only serve them implicitly through the visibility they gain in the community of tool users. By the same token, they have little incentive to venture into domain sciences and to tackle economic or societal challenges. It is possible that a domain science or an industrial project requires new techniques which then can be published in data science venues, but this is not guaranteed at all. It usually takes heavy investment of time and effort to be able to understand domain problems, so excursions into domain sciences are highly risky. Even when such collaborations are established, the data scientist has a strong prior to use his/her favorite methodology which is not necessarily the best solution for a given problem. Finally, the data scientist has little incentive to bring the project to full fruition, and he/she often “runs away” with an abstract data science problem (and solution) extracted from the project. Symmetrically, domain scientist and industrial domain experts have no incentive to advance data science and to develop and publish new techniques, as long as their data science problems get solved. When they venture into tool development, they have little incentive in developing general purpose tools.
- 36. Center for Data Science Paris-Saclay Affirmative action 36 TAKE HOME MESSAGE NO2
- 37. • Overweight (e.g, double count) out-of-domain papers in every evaluation • Make software tool ownership count Affirmative action 37 TAKE HOME MESSAGE NO2
- 38. Center for Data Science Paris-Saclay38 TAKE HOME MESSAGE NO3 Co-locality is important. It would be desirable that data science for scientific data in France be grouped into 5-6 sites of similar size. Resources and experience can and should be shared.
- 39. Center for Data Science Paris-Saclay39 THANK YOU!