Introduction to data science

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. Mandatory presence: Yes , 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 5.0 (a total grade of 5.5 will be rounded up to 6).)
Vaksoort	master
Coördinator	Dr. E. Wilhelm
Docent(en)	drs. D.H. Chu , H.T. Kruitbosch, MSc. ,Dr. E. Wilhelm
Verplichte literatuur	Titel Auteur ISBN Prijs Python Machine Learning Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2. 3rd edition, 2019 S. Raschka, and V. Mirjalili Deep Learning, 2016 I. Goodfellow, Y. Bengio, and A. Courville
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	Opleiding Jaar Periode Type MSc Courses for Exchange Students: Engineering: Biomedical-Industrial-Mechanical - semester I a verplicht MSc Mechanical Engineering: Advanced Instrumentation  (Compulsory Courses) 1 semester I a verplicht MSc Mechanical Engineering: Materials for Mechanical Engineering  (Compulsory Courses) 1 semester I a verplicht MSc Mechanical Engineering: Process Design for Energy Systems  (Compulsory Courses) 1 semester I a verplicht MSc Mechanical Engineering: Smart Factories  ( Compulsory courses) 1 semester I a verplicht