Dutchdatabusters connects Master students and Industry in the field of Data Science and Data transformation. Under the guidance of experienced project supervisors in both Academia and Industry our students can tackle the most complicated modern day challenges. This enables students to kickstart their career while Industry gets the opportunity to have their data transformation challenges solved in a rapid and intelligent way.
Dutchdatabusters brengt Master studenten op gebied van ‘Data transformation’ en het bedrijfsleven bij elkaar. Onder begeleiding van ervaren project begeleiders van CodeNext21 BV kunnen onze studenten oplossingen bieden voor de meest ingewikkelde uitdagingen. Studenten krijgen een kickstart in hun carrière, en bedrijven krijgen zo de kans om hun data transformatie uitdagingen versneld te verwezenlijken.
This what we did in the last two years!
Your data transformation project is in good hands. Our master students are eager to perform and get the best results for your project!
Reference cases
Don’t take our word for it – this is what we did!
Unica excels in providing integrated services. Our students created a Data-driven model to bring cost calculation to the next level.
Based on the P.O.C presented, UNICA is able to do cost price calculation of complicated buildings with high accuracy within seconds.
BERCO is Specialized in the development and assembly of truck interiors and car carpets. Our graduation students performed a process-mining model for the whole production facility of more than 100 machines, 150 operators 1200 distinct products, optimizing the manufacturing to increase throughput by over 20%
Tribe is a UK Business-to-business Telecoms Provider with more than 35mlj phone calls per month. Our students were challenged to provide machine learning (ML) model to prevent telecom fraud. Based on CodeNext21’s ML21 platform a fraud prevention & interception solution was provided with a efficiency higher than 80% than the excising solution in place.
In the gynaecologie department of a hospital, too many “urgent” treatments were being scheduled, interfering with regular scheduled operations. Data At first glance there were no clear common factors. Out students performed a data-discovery and a created a process-mining model to find the root cause of these of these “urgent” treatments.
