Introduction to data science
Faculteit  Science and Engineering 
Jaar  2020/21 
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: 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 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)
(16 LC, 2S, 67 PRC, self study 55) 

Toetsvorm 
Opdracht (AST), Schriftelijk tentamen (WE)
(AST 60%, WE 40%) 

Vaksoort  master  
Coördinator  prof. dr. ir. B. Jayawardhana  
Docent(en)  drs. D.H. Chu , H.T. Kruitbosch, MSc.  
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  Assignments (both of a theoretical nature, and some programmed in Matlab 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). Mandatory presence: Yes , Some group presentations assume the presence of the entire group. Some of the lectures are mandatory. 

Opgenomen in 
