Data Science and Machine Learning

Online sessions Tuesdays at 14:30-16:00 / 16:10-17:40 / 17:50-19:20 local time Germany (GMT+2) on:  

 April 13th, April 20th, April 27th  

 May 4th, May 11th  

 June 1st, June 8th, June 15th, June 22nd, June 29th  

 July 6th  

The course is addressed at Bachelor students. Main focus of this course is on collaborative case study work in international student teams. Students will get to know people from other countries and continents and work together with them on realistic Data Science tasks.  

Topics covered (preliminary) 

 

Part 1: Introduction to Data Science with Python  

 Programming Basics  

 Data Visualization  

 Data Cleaning and Manipulation  

 Importing and Exporting Data  

 Image Processing  

 Anaconda, Jupyter Notebook and Spyder  

 Python Packages: e.g. NumPy, Pandas, Mathplotlib 

 

Part 2: Machine Learning with Python  

 Model Validation  

 Regression  

 Classification  

 Principial Component Analysis  

 Clustering  

 Text Analytics  

 Python Packages: e.g. Scikit-Learn, SciPy 

 

Use cases  

 Market Basket Analysis  

 Customer Churn Prediction  

 Social Irresponsibility Survey  

 Insurance Case  

 Fraud Detection  

The online sessions will be very interactive including break-out rooms in small groups.  

As a preparation for the online sessions the students have to work through free self-learning online courses on DataCamp.com and/or selected chapters in text books (details will be provided). The case studies will be in self-organized international student teams outside the class room.  

Language  

The course will be held in English.  

 Prerequisites  

This course is designed for Bachelor students in the field of Business and Management. However, students from other disciplines like Engineering and Computer Science are welcome to participate. 

Participants are expected to have basic knowledge in Mathematics, Statistics and spreadsheet tools like Microsoft Excel.  

Workload  

Total expected workload: 10 ECTS / 300 hours  

 Part 1: Introduction to Data Science with Python (5 ECTS / 150 hours)  

 Part 2: Machine Learning with Python (5 ECTS / 150 hours)  

Crediting of the course work 

Students from Dortmund University of Applied Sciences and Arts can earn ECTS for their “Wahlpflichtmodul”. 

Students from InduTwin partner universities will receive a certificate of participation. Further recognition to be discussed directly with their home institution. 

Lecturers  

Prof. Dr. Stephan Weyers is professor for Mathematics, Statistics and Supply Chain Management at  

Dortmund University of Applied Sciences and Arts since March 2019. From 2014-2019 he was professor for Mathematics and Didactics at Technische Hochschule Mittelhessen in Gießen and has also worked as a Senior Analytic Specialist at McKinsey & Company between 2007-2014. Stephan Weyers is the Project Director of InduTwin.  

Guest lecturers to be confirmed. 

Registration Deadline 

The registration is already closed.