Presentation download

Open Analytics Day – 14 October

Plenary

  • Shipping Data Science Products! – Ian Ozsvald, modellinsight.io PDF
  • Ibis: Productive Python Analytics at Scale – Wes McKinney, Cloudera PDF
  • Machine learning for particle physics using R – Andrew Lowe, Wigner Research Centre for Physics, Hungarian Academy of Sciences LINK
  • Heroes, Warcraft and Spark – Vincent Warmerdam, Go Data Driven

Section track room I.

  • Luigi Task Orchestrator – three aspects – Arash Rouhani, Spotify LINK
  • Machine Learning Under Test – Valerio Maggio, University of Salerno PDF
  • Composing testable and robust machine learning pipelines – Holger Peters, Blue Yonder GmbH PDF
  • Recommender systems in anomaly detection – Windhager-Pokol Eszter, Balabit PDF
  • Analyzing Networks in Python – Johannes Wachs, CEU Center for Network Science PDF
  • Going Functional: Three Programming Languages what every Data Scientist should learn Földi Tamás, Starchema LINK
  • How to “start mining” in KNIME from sketch – Kuna Péter, Infomatix Ltd. PDF

Section track room II.

  • News from dplyr and future plans – Romai Francois, R-enthusiasts PDF
  • Forecasting the structure of natural gas consumers pool with R – Ondřej Konár, The Czech Academy of Sciences: Institute of Computer Science PDF
  • Finding the right customer – an uplift model case study – Wolfgang Körbitz, WU Wien PDF
  • Looking for Something Special — Outlier Detection in R – Salánki Ágnes, Budapest University of Technology and Economics PDF
  • Shiny as a platform for collaborative data exploration – Kocsis Imre, Quanopt Ltd.
  • R Service Bus Willem Ligtenberg,Open Analytics PDF
  • Teaching R in heterogeneous settings: Lessons learned – Matthias Gehrke, FOM University of Applied Sciences PDF
  • archivist: Managing Data Analysis Results – Marcin Kosiński, Grupa Wirtualna Polska PDF

Innovative BI Day – 15 October

Plenary

  • Holistic Event Analytics at SoundCloud – Cory Levinson, Soundcloud PDF
  • Watson Analytics – Vedran Travica, IBM
  • Scaling up Business Intelligence from the scratch and to 14 countries worldwide – Sergii Khomenko, Stylight GmbH PDF
  • Approachable Analytics – putting Analytics in Action – Anand Chitale, SAS Global Technology Practice PDF

Section track room I.

  • Adatvizualizációs esettanulmányok – Szűcs Kriszta, krisztinaszucs.com PDF
  • Szöveges jelentések és riportok kezelése – Kiss Máté, IBM
  • How to build a data-driven culture – Attila Petróczi, Prezi LINK
  • Multichannel personalization in retailing – Németh Bottyán, Gravity R&D PDF
  • How to Design a Performance Management Dashboard for Real Business Value? – Kovács Vera Lucia, Vortess Ltd PDF
  • Online analitika kihívásai – Mészáros Csaba PDF, Iván László, Holló Krisztián, Kántor Gergely

Section track room II.

  • Self-service BI System for Downstream Supply Chain Optimization – Kenesei Tamás és Parázs Dávid, MOL Group
  • Adatvizualizáció és analitika mindenkinek – Portik Imre, SAS Magyarország PDF
  • Daily sales at your fingertips – eliminate organisational roadblocks – Ollai János, Partner in Pet Food Hungária Kft. PDF
  • A modern BI 15 percben – bemutatkozik a Yellowfin – Fekszi Csaba, Omnit Solutions kft. PDF
  • ADAM a K&H-nál – Dévényi Edit, K&H Group (edit.devenyi@kh.hu)
  • Transactional Data Mining at Lloyds Banking Group – Főző Csaba, Lloyds PDF
  • Adatbányászat a nagyvállalatoknál kerekasztal – Gáspár Csaba, Főző Csaba, Dévényi Edit, Gozlán Illés

Tutorial day – 13 October

  • Innováció a BI-ban, Arató Bence, BI Consulting Kft. PDF1, PDF2
  • R bevezető, Tóth Dénes, LINK
  • Python és Pydata alapozó, Kazi Sándor, LINK1, LINK2, LINK3
  • Gyors sikerek adatbányászati módszerekkel, Petrócziné Huczman Zsuzsanna, PDF
  • Shiny tutorial, Vincent Warmerdam, GoDataDriven