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| Semester |
F2020
|
| Subject |
Computer Science * / Informatics * / Mathematical Computer Modelling *
|
| Activitytype |
master course
|
| Teaching language |
English
|
| Registration |
Tilmelding sker via STADS-Selvbetjening indenfor annonceret tilmeldingsperiode, som du kan se på Studieadministrationens hjemmeside Når du tilmelder dig kurset, skal du være opmærksom på, om der er sammenfald i tidspunktet for kursusafholdelse og eksamen med andre kurser, du har valgt. Uddannelsesplanlægningen tager udgangspunkt i, at det er muligt at gennemføre et anbefalet studieforløb uden overlap. Men omkring valgfrie elementer og studieplaner som går ud over de anbefalede studieforløb, kan der forekomme overlap, alt efter hvilke kurser du vælger. Registration through STADS-Selvbetjeningwithin the announced registration period, as you can see on the Studyadministration homepage. When registering for courses, please be aware of the potential conflicts between courses or exam dates on courses. The planning of course activities at Roskilde University is based on the recommended study programs which do not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programs, an overlap of lectures or exam dates may occur depending on which courses you choose. |
| Detailed description of content |
Data is being collected, analyzed, and used in intelligent applications as never before. This applies across all areas of business, science and life. We live in an age not only of a digital revolution, but also of a data revolution, where businesses, in order to succeed, need to be ever more data-driven. But what does it mean to be data-driven and what are Big Data, Machine Learning, Artificial Intelligence, and Data Science in a business context? In this course, we will answer these questions by giving a hands-on introduction to Data Science (using the tool R) looking at how data can be utilized especially within businesses and organizations. We will discuss classical Business Intelligence and how data can be utilized in solving business problems. We will look at the entire data analysis cycle: from asking questions, collecting, preprocessing, and analyzing data, to building (machine learning) models and communicating the results. Furthermore, we will look at the particular challenges of storage and computation in the context of Big Data. |
| Expected work effort (ECTS-declaration) |
The course will have a total workload of 135 hours with 40 hours of lectures and exercises, 70 hours of preparation over an 11 week course period and 25 hours for the exam and preparation before the course |
| Course material and Reading list |
Articles and extracts from books and web. |
| Evaluation- and feedback forms |
There will be feedback on exercises which are set during the course. An evaluation will take place at the end of the course |
| Administration of exams |
IMT Studieadministration (imt-studieadministration@ruc.dk)
|
| Responsible for the activity |
Henrik Bulskov (bulskov@ruc.dk)
Jesper Schmidt Hansen (jschmidt@ruc.dk) Jens Ulrik Hansen (jensuh@ruc.dk) |
| Type of examination |
Individual oral examination based on a set assignment. The examination is conducted as a dialogue. During the examination, questions can be asked regarding the entire syllabus. The written product must be between 4,800 - 48,000 characters in length, including spaces. The size specifications include the cover, table of contents, bibliography, figures and other illustrations, but exclude any appendices. Time allowed for examination including time used for assessment 20 minutes. The assessment is an overall evaluation of the written product and the oral examination. Permitted support and preparation materials during the examination: All. Assessment: 7-point grading scale. Moderation: Internal co-assessor. Reexam Same as ordinary |
| ECTS |
5
|
| Learning outcomes and assessment criteria |
|
| Overall content |
With an elective course, the student has the opportunity to specialise in a specific subject area where the student acquires knowledge, skills and competences in order to translate theories, methods and solutions ideas into their own practice in relation to software development. Examples of elective courses: Robotics, AI, internet technologies, programming language, parallel calculation, mobile computers, etc.The specific contents are listed on study.ruc.dk. |
| Teaching and working methods |
Normal class instruction, i.e. a mix of lecturer presentations, student presentations and practical work on specific tasks. Lecture with exercises. Is stated in the description on study.ruc.dk. |
| Type of course |
Elective course |
| Exam code(s) | |
| Last changed | 14/02/2020 |
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