Introduction to Data Science
Faculteit  Science and Engineering 
Jaar  2018/19 
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 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 indepth analysis of the specific topics. We will cover the following topics with respect to data mining:  multidimensional and multivariate data, sampling, preprocessing, quality, missingness and uncertainty  visualisation  clustering and classification  optimisation analysis  text analysis These topics give a broad overview of challenges with respect to data science and methodologies. Practical assignment work is done in groups which will 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. 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)  
Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE)
(Assignments are both of a theoretical nature, and some programmed in Matlab, R, or Python. 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.) 

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. 

Opmerkingen  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.  
Opgenomen in 
