Three days of keynotes, workshops, talks and case studies provide an opportunity for experts to share their specialist knowledge. From October 13 to 15, 2015, the Hotel Mercure Buda will host the Budapest BI Forum, which focuses on business intelligence, analytics and applications at leading companies. One of the participants is Holger Peters, data scientist and software developer at Blue Yonder, whose talk at Europython in Bilbao in July was so well received that he has been invited to speak in Budapest.
The Budapest BI Forum, a BI and analytics conference has made a name for itself among data scientists, software developers, and up-and-coming young specialists. The speakers include software developer Wes McKinney from big data platform Cloudera and Romain Francois, expert on the programming language R. Arash Rouhani, data scientist at the music streaming service Spotify, will give some insights into dealing with large volumes of data. Attila Petróczi, data analytics manager at Prezi, which provides presentation software, will discuss how to successfully establish a data-driven culture. Sergii Khomenko, data scientist at the fashion community Stylight will explain how his company has developed business intelligence from the ground up and implemented it in 14 countries. And
Cory Levinson, a data analyst with the online music service SoundCloud, will discuss holistic event Analysis.
The focal points of the individual conference days:
- October 13 is Tutorial Day, with a variety of full and half-day sessions on the programming languages R and Python, data mining, and BI trends for both beginners and experts.
- The first "real" conference day, October 14, is dedicated to the Open Source Analytics ecosystem.
- On October 15, the Innovative BI Day, the participants will discuss new application fields for business intelligence, such as machine learning and data mining.
On October 14, Blue Yonder’s Holger Peters will explain how to create tested and validated forecasting models. The more complex forecasting models become, the greater the danger that logical rules in the code will influence one another and it won’t be possible to separate them. The result: high development costs. Dividing machine learning models into small, combinable modules allows data scientists to test them in small units, ensuring they can be reliably used in production environments. He will also show how the scikit-learn programming library can simplfy model development, tests, and validation. Holger Peters will not only be a speaker, but also a keen participant. You can read all the tweets from the conference at the hashtag #BudapestBI or by following ByAnalytics_en in the coming weeks.