2nd Data Science Hackathon
This past Winter Semester have a few very interesting workshops ready for you. In October you are most welcome to join our „Applied Data Science“ workshop and in November we will be organising an „Introduction into Deep Learning“ seminar, both of which will be moderated by professionals, who work with Data Science every day.
Do not worry, all you need is genuine interest in the subject. Some knowledge of R or Python might come in handy. On the day of the Hackathon itself, an introductory workshop will be offered for beginners, so anyone can join!
Do you want us to keep you posted on everything that is going on with the Data Science Initiative, do you wish to secure a spot for the upcoming workshops or simply have some questions?
Don’t hesitate to contact us at: firstname.lastname@example.org.
Deep Learning is nowadays a crucial part of every modern product! The emphasis of our workshop will be on Deep Learning Methods, explained in the context of Simulations for modern companies, where the participants will be able to apply the newly learned concepts.
Because of the diversity of Data Science, it is difficult to maintain a proper overview. With the help of Data Science experts we are organising a 2-part workshop, taking place in October 2018, about challenges and tasks of a Data Scientist in business.
Don’t want to miss any upcoming events or workshops? Send us a short email to email@example.com and subscribe to our Data Science Initiative of Students of the University of Vienna Newsletter!
Over 2 days of our 1st Data Science Hackathon 6 teams attempted to build best possible prediction models, including Random Forrest and Boosted Forrest algorithms, all the way to Neural Networks.
Nick Brooking held an introductory workshop about Neural Networks on 8th and 15th of May 2018. In the 2-part workshop Nick presented the principle of Neural Networks and the mathematics behind. He also covered possible implementations of Neural Networks in R.
Our 3 part Python Introduction workshop focused on visualising and analysing data with the help of Numpy, Pandas and Matplotlib.