Introduction to Data Science
Faculteit  Science and Engineering 
Jaar  2019/20 
Vakcode  WMCS16002 
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: 1) Describe several fundamental methods that play a central role in Data Science (in natural language and pseudo code) 2) Implement methods in a programming language 3) Reason about data science and individual methods concerning appropriateness, correctness and efficiency 4) Analyse data by means of experiments 5) Report orally and in writing about activities that involve the knowledge and skills listed above 

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:  multidimensional and multivariate data, sampling, preprocessing, quality and missingness  clustering and classification  optimization  text analysis 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 interdisciplinarity 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). Please note: Due to the popularity of the course the enrolment is now restricted to 120 places given on a firstcomefirstserve basis (see Remarks section). We run a very strict procedure in case of 'ghost enrolments' of people never showing up, which results in an NA grade if not disenrolled. This is necessary due to the automatic optimized group assignment in the first 2 weeks to limit the negative impact of personal choices on the groups. We strongly appeal to your consciousness when you take one of the restricted places if you are not sure if you are committed to finish the course requiring 140 hours of your time. 

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

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE)
(Assignments (both of a theoretical nature, and some programmed in Matlab, R, or Python) and an exam. The assignments (the A grade) are worth together 60% of the grade; the exam (the E grade) is worth 40% of the grade. Both A and E grades must be above 5.0 (a total grade of 5.5 will be rounded up to 6).) 

Vaksoort  master  
Coördinator  K. Bunte, PhD.  
Docent(en)  dr. G. Azzopardi , K. Bunte, PhD.  
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. Computers in the lab are in Linux, so familiarity will help. For group work, we require the use of git and GitHub. If you are not familiar with these, it would be good to read up on it upfront. 

Opmerkingen  This course has LIMITED CAPACITY for students outside of the CS programme:


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