Introduction to data science

Faculteit Science and Engineering
Jaar 2020/21
Vakcode WMME027-05
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, hands-on practicals, theory and methods. Some practicals contain real-world data science problems (taken from
Astronomy and Medicine) for which the full data-science-life-cycle 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 in-depth 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
Titel Auteur ISBN Prijs
Genetic Algorithms in Search, Optimization and Machine Learning
(recommended)
David E. Goldberg 9780201157673
An Introduction to Introduction to Data Mining (recommended) Pang-Ning Tan, Michigan State University, Michael Steinbach, 9780321321367
An Introduction to Statistical Learning - with Applications in R
(recommended)
Gareth James, Daniela Witten 9781461471370
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
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