|Website:||website met extra informatie|
|Periode:||periode 2 (week 46 t/m 5, d.w.z. 13-11-2017 t/m 2-2-2018; herkansing week 16)|
|Deelnemers:||tot nu toe 135 inschrijvingen|
|Rooster:||De officiële roosters staan ook in Osiris|
|Inhoud:||Data Analytics is a level-3 bachelor course which assumes you have completed the Scientific Research Methods (INFOWO) and Imperative or Mobile Programming (INFOB1MOP), or similar external courses. If you do not have elementary experience on statistics or programming yet, be aware that you will need to put in significantly more time than 20 hours per week in order to be able to complete this course. We therefore advise you not to enroll, then, even though it is formally not prohibited.
In the data analytics course, you will learn to:
|Werkvorm:||Throughout the course, you are given a number of individual assignments. The answers to the assignments are to be submitted to the appropriate section of the Slack infob3da forum.|
|Toetsvorm:||The final grade will be determined based on the following course components:
|Inspanningsverplichting voor aanvullende toets:||Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.|
Data analytics is a unique field where you will learn insights needed to make sense of data, research, and observations from everyday life. You will learn how to apply a data-driven approach to problem solving, but will not only learn about tools, methods, and techniques, or the latest trends, but also more generic insights: why do certain approaches work, why the field is so popular, what common mistakes are made, and so on. You will also learn that data analytics is part science and part `art', since in applying methods and searching for findings there is a creative component.
Throughout the workshops you will work on several individual DA assignments, on predefined problems/datasets, using R tools, with a focus on the Life Sciences & Health domain. However, many of these assignments allow for freedom for your own individual approach. Most assignments involve real-world and relevant data sets, often connected to active research.
The lectures will provide the theoretical background of how a DA process should be performed according to industry standards. Furthermore, we discuss an overview of popular DA techniques to help match techniques with information needs, including applications of text mining and data enrichment.
The course will be taught in English.