Master Semester Ia
You learn some fundamental principles about data and discovery, analysis and visualization. We will look at big data analytics and advanced analytical theory and methods. Since it is an introduction course we will have a broad overview of general principles including specific examples and references to the Master courses available with in-depth analysis of the specific topics.
DSSC Newsletter March 2017
The course Introduction to Data Science: look ahead by looking back
The course Introduction to Data Science was inaugurated in November 2016 and was shared by the Data Science specialization in the Astronomy Master’s Programme and the Data Science and Systems Complexity specialization in the Computing Science Programme. The course brought together students with various backgrounds: Mathematics (9), Computer Science (36) and other disciplines including Astronomy, Molecular Biology and Biotechnology, Industrial Engineering & Management (21). Several of the students were part of an exchange program.
The instructors and DSSC Pioneers Dr. Kerstin Bunte and Dr. Mircea Lungu share with us their approach to the course:
“In order to simulate realistic data science situations, we assigned students to heterogeneous groups making sure that each group had an interdisciplinary background. The assignments were designed in such a way that the students of different backgrounds would have advantage in different parts of the practicals. The most successful groups eventually developed a team environment where the members were sharing their knowledge with the others by teaching the aspects that they had more experience with.
The students had the chance to practice a variety of skills that a data scientist must be familiar with: Collaborative project development including using distributed the git version control system, Reading, summarizing, and presenting relevant state-of-the-art research, Following the steps of the data analysis life cycle including (identifying data sources, collecting and combining various sources, cleaning up, exploring and analyzing the data, and finally discussing the outcomes of their analysis).
To ensure the attractiveness of the practicals we avoided toy examples and focused on offering the students a variety of realistic data science problems that span various domains: Descriptive analysis and visualization applied to data from open APIs, Music and movie recommendations based on association rule mining applied to hundreds of thousands of user preferences scraped from online services such as Last.fm and OMDB, Clustering of astronomical data representing simulated galaxy evolution with several hundred thousand stars, Text analysis and text summarization applied to literary texts, Classification applied to mixed marketing data.
We are already looking forward to the next iterations of the course whose interdisciplinary character we anticipate to be reinforced as other Master’s programmes have already manifested their intention of recommending Introduction to Data Science to their students.”