|Kursusnavn (dansk):||Big Data Management (Technical) |
|Kursusnavn (engelsk):||Big Data Management (Technical) |
|Semester:||Efterår 2015 |
|Udbydes under:||cand.it., softwareudvikling og -teknologi (sdt) |
|Omfang i ECTS:||7,50 |
|Min. antal deltagere:||1 |
|Forventet antal deltagere:||50 |
|Maks. antal deltagere:||80 |
|Formelle forudsætninger:||This course assumes basic computer science and programming background. It requires that the participants have taken the introductory programming courses and a data modeling course (Introduction to Database Design) for the Software Development and Technology study programme.
This course is part of the SDT specializations "Databases" and "Business Analytics".
|Læringsmål:||After the course students should be able to:
- Analyze and discuss the characteristics of data exploration and analysis with large, fast-growing and diverse data sets.
- Analyze and discuss the complexity of the database system landscape
- Reflect upon the relative merits of SQL and NoSQL systems
- Reflect upon basic big data analysis techniques
- Reflect upon the societal issues linked to big data management
- Design, conduct and report result of experiments related to data exploration and analysis in the context of two specific data sets.
|Fagligt indhold:||Please note, that due to technical challenges, changes may occur before the start of the semester (week 35) – this applies to all sections of the course description.
Big data offers opportunities and risks, requiring deep technical knowledge as well as critical skills to analyse the quality and impact of any approach or solution. Just a few years ago most data could be extracted, transferred and loaded into data warehouses where it could be analyzed off-line. Traditional relational approaches to data management could fit all forms of problems. Today, large volumes of data are being captured online, through sensors, outside the scope of traditional database systems. Making sense, possibly in real-time, of this data is a key challenge for many organizations, institutions and governments so that they can understand and adapt quickly to changing conditions. For example, a hospital could incorporate GPS data about the actual location of its ambulances and helicopters with data about the mission these vehicles are involved in, as well as emergency calls and current status in various emergency rooms in order to take decisions in real-time when faced with an emergency call (also in the face of large-scale disasters). At the same time, availability of low-cost sensing and network connectivity technologies drives a myriad technical innovations affecting every aspect of life through data-based services.
There is no longer one approach that can fit all data management problems. For each problem, IT specialists have to decide on appropriate models and systems to handle the relevant data. Big data denotes the processes involved in making data from various data sources available for advanced analytics. In this course, we will address the technical issues that emerge in the course of collection, management, processing and analytics of large-scale data. We will introduce modern approaches to organizing and making sense of large, fast growing and diverse data sets. We will cover the principles of big data analysis, and illustrate a hands-on approach to big data modeling and management. Students will be introduced to technical skills necessary for assessment of current approaches to big data management and analytics as well as critical theoretical tools for identification and discussion of potential pitfalls, obstacles and opportunities that working with data and analytics may bring up.
|Læringsaktiviteter:||14 ugers undervisning bestående af forelæsninger og øvelser|
The course will be based on lectures and three mandatory practical assignments with a range of different datasets and data management systems. More specifically, the class relies on three mandatory projects where teams of DIM students will work together with SDT students from the technical big data management course.
See the schedule here:
link to the time table
The schedule will be available shortly before the beginning of the term.
|Obligatoriske aktivititer:||There are three mandatory practical assignments. |
|Eksamensform og -beskrivelse:||X. experimental examination form (7-scale; external exam)|
Hand-in the exam report in LearnIT.
|Litteratur udover forskningsartikler:||We will use the following textbook: Big Data Principles and best practices of scalable realtime data systems by Nathan Marz and James Warren, Manning Publications Co.
Optional/supplementary: (1) "Principles of Big Data: Preparing, sharing and analysing complex information", Jules Berman, Morgan Kaufmann, 2013, (2) "Joe Celko's Complete Guide to NoSQL: What Every SQL Professional Needs to Know about Non-Relational Databases", Joe Celko, Morgan Kaufmann, 2013.