Introduction to Data Science
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Faculteit  Science and Engineering 
Jaar  2022/23 
Vakcode  WMME02705 
Vaknaam  Introduction to Data Science 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I a 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Introduction to Data Science  
Leerdoelen  At the end of the course, the student is able to:


Omschrijving  You learn some fundamental principles about data preprocessing and discovery, analysis and evaluation. We will look at big data analytics, handson practicals, theory and methods. Some practicals contain realworld data science problems (taken from Astronomy and Medicine) for which the full datasciencelifecycle is expected to be performed by each student team. Since it is an introduction course we will have a broad overview of general principles including specific examples and references to the other Master courses which provide more indepth analysis of the specific topics. We will cover the following aspects with respect to data mining:
These topics give a broad overview of challenges with respect to data science and methodologies. Practical assignment work is done in groups in which participants will be automatically assigned within the first 2 weeks of the course optimized for interdisciplinary aiming to simulate a realistic data science project team composition. Groups will have to present their work in front of the class for selected assignments. Attendance to some lectures might be mandatory (details will be announced in the first lecture). 

Uren per week  
Onderwijsvorm 
Bijeenkomst (S), Hoorcollege (LC), Practisch werk (PRC)
(Mandatory presence: some group presentations assume the presence of the entire group. Some of the lectures are mandatory.) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE)
(AST 60%, WE 40%. Both WE and AST grades must be above 5.5 (a total grade of 5.5 will be rounded up to 6).) 

Vaksoort  master  
Coördinator  Dr. E. Wilhelm  
Docent(en)  Dr. E. Wilhelm  
Verplichte literatuur 


Entreevoorwaarden  The course unit recommends prior knowledge acquired from the courses Algorithms & Data Structures in C, Statistics and Advanced Algorithms & Data Structures from the BSc degree programme in Computing Science or equivalent knowledge from other bachelor programmes. Programming knowledge in at least one of the following languages: C/C++, Matlab, R or Python is indispensable. 

Opmerkingen  
Opgenomen in 
