Goethe University Frankfurt

Web Business: Data Challenges SS 2018

(Summer Semester 2018)

B-WB, M-WB, M-PoE

Lecturers: Prof. Dott. Ing. Roberto V. ZicariDr. Karsten TolleTodor Ivanov, Naveed Mushtaq and Kim Hee.


Course start: Thursday 19. April. 2018 (kickoff)

Time and Location:

Thursday  14:00 – 16:00   Hörsaaltrakt Bockenheim – H III

Friday       10:00 – 12:00   Hörsaaltrakt Bockenheim – H III

Languages: The languages of the lecture are English and German.

Credit Points: Students can receive 6 CPs. Link in QIS/LFS

Course Description: Students will take part to two Data Challenges. One offered by Deutsche Bahn AG and one offered by Procter & Gamble (P&G).

Eligibility: Bachelor Students, Master Students, and PhD students across multiple disciplines are encouraged to attend the kickoff and to sign up for one Data Challenge.

Students in Computer Science,  Data Science, Information Systems, Business Computer Science, Mathematics, Economics, Marketing, Psychology, and other disciplines will form teams of two to explore the questions posed.  Team members are required to attend the kick-off lecture to sign-up for this project.

Course Registration:  You have to register for the Data Challenge WS 2018 in the form below. Registration is preferable in teams of 2 persons. Deadline for registration is Thursday, 05.04.2018.


Important Note: This project is in two phases, with Phase One and Phase Two taking place Summer Semester 2018.  


Data Challenges:

Deutsche Bahn and Frankfurt Big Data Lab Data Challenges 2018 – Smart Cities, Smart Life

Details of the DB Award „Smart Cities“ in collaboration with the Frankfurt BIG DATA LAB of the Goethe-Universität Frankfurt

The design of our future urban life will depend fundamentally on the constitution of our future mobility in ever faster growing or ever faster evacuating urban regions.

The Deutsche Bahn AG, as one of the few comprehensive mobility supplier, wants to shape the actual development towards networked, smart and sustainable mobility.

Against this background the development and the construction of digital and networked possibilities to offer comfortable, payable and simultaneously environmentally friendly solutions of urban mobility and logistics will gain great importance.

This means in our opinion the following:

  1. How will be organized smart and integrated mobility on different modes of transport in the future
  2. How will be shaped smart logistics and how could we offer new infrastructural services
  3. How will become reality a new smart mobility by the use of nodes and transfer points and the remodeling of third places
  4. Which concepts for a „mobility on demand” or certain possibilities of autonomous driving actually could be demonstrated prototypically

On the basis of the open Data pool of Deutsche Bahn AG, students of the Frankfurt BIG DATA LAB should create ideas and patterns of possible solutions that could accomplish to be regarded as possible indications for a smart mobility of the future.

For first insights on actual solutions look at the following reference implementations from Deutsche Bahn AG:

  1. Bahnhofsbox
  2. Cargo Bike
  3. Clever shuttle
  4. ioKi

 

Procter & Gamble (P&G) Data and Frankfurt Big Data Lab  Data Challenges 2018 – Smart Logistics, Smart Supply Chain

Challenges to be announced soon …

 


Among the teams that successfully complete both phases of the project, winners will be awarded a price:

 


Project Description:

The project consists of two phases: Phase I will be held during the Spring Semester 2018. Phase 2 will take place during Spring 2018. The proposed timeline and details of these stages are:

Phase 1:


-Teams will be asked to address one of the Data Challenges offered.

Specifics will be addressed at introductory lectures. Teams will then work independently to create a proposal of a novel idea that satisfies the data challenge chosen.

-Deliverable: A mid-term presentation of the project idea, where it is required that:

  1. teams clearly state objectives,
  2. general description of the way they intend to implement the idea using the data available for the challenge chosen.

Phase 2:

Teams that submitted a successful presentation at Phase I will be then asked to implement the idea and present it at the end of Phase II.  (Exact dates and detailed agenda to be reviewed at the kickoff)



Course Schedule (preliminary)

Date Topic Materials
19.04.2018 – 14:00-16:00 Kick Off Meeting Data ChallengesDeutsche Bahn and Procter & Gamble (P&G)
26.04.2018 – 14:00-16:00 Data Challenge presentation by Deutsche Bahn
27.04.2018 – 10:00-12:00  Data Challenge presentation by Procter & Gamble (P&G)
03.05.2018 – 14:00-16:00 How to do: business understanding, requirement analysis + Q & A
04.05.2018 – 10:00-12:00 How to handle data? – Data Tools + Q & A
11.05.2018 – 10:00-12:00  Q & A
17.05.2018 – 14:00-16:00  Student presentations – Deutsche Bahn
18.05.2018 – 10:00-12:00  Student presentations – Procter & Gamble (P&G)
24.05.2018 – 14:00-16:00
25.05.2018 – 10:00-12:00
01.06.2018 – 10:00-12:00
07.06.2018 – 14:00-16:00
08.06.2018 – 10:00-12:00
14.06.2018 – 14:00-16:00
15.06.2018 – 10:00-12:00
21.06.2018 – 14:00-16:00
22.06.2018 – 10:00-12:00
28.06.2018 – 14:00-16:00
29.06.2018 – 10:00-12:00
05.07.2018 – 14:00-16:00
06.07.2018 – 10:00-12:00
12.07.2018 – 14:00-16:00 Final presentations Deutsche Bahn
13.07.2018 – 10:00-12:00 Final presentations Procter & Gamble (P&G)
Award Ceremony at Deutsche Bahn
 Award Ceremony at Procter & Gamble (P&G)

 


Resources

Ethics and Data

Legal Implications of Data

Data Privacy

Elevator Pitch

Elevator Pitch- 5 minutes Presentation

Machine Learning

  • Machine Learning Course at Stanford by Andrew Ng, Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty.
  • Non technical 5-part series on introductory machine learning by Alex Castrounis, Product Leader and Technologist.
    • Part 1 – definition of machine learning and most widely used machine learning algorithms.
    • Part 2 – model performance, data selectionpre-processing, splittingfeature selection and feature engineering.
    • Part 3 – model variancebias, overfitting, model complexitydimensionality reduction, model evaluationperformance, tuningvalidationensemble learning, and resampling methods.
    • Part 4 - model performance and error analysis 
    • Part 5 – unsupervised learning, predictive analyticsartificial intelligencestatistical learning, and data mining.

Open Source Tools

  • Apache Hadoop is a project developing open-source software for reliable, scalable, distributed computing.
  • Apache Spark is a fast and general engine for large-scale data processing.
  • Apache Flink is an open-source platform for distributed stream and batch data processing.

Advanced AI Tools

  • TensorFlow  is an open source software library for numerical computation using data flow graphs.
  • The Microsoft Cognitive Toolkit: A free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain.

Making App

Chat Bot 

Enterpreneurship

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