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#acl petia:read,write,delete,revert,admin All:read | = IS5 - Data warehousing = == Requirements == 90 hp Computer and Systems Sciences with at least: * 7,5 hp database |
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= IS5 - Data warehousing = | == Short description == The course discusses theories about - and implementation of - databases structured for data analysis, so called data warehouses. Data warehouses are used within Business Intelligence (BI) to support organizations strategic decision making by utilizing Decision Support Systems. This course will help you to design and implement data warehouses. You will learn different techniques and principles based on which you can design an effective and efficient data warehouse. In addition, you will experience the full data warehouse life-cycle by implementing a system according to latest advances in this area. |
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Describe the pages in this category... | == Aim == The general goal with this course is to familiarize the students with a special kind of information systems called Data Warehouses – their role, utilization and benefits for organisations, as well as architectures and underlying technologies relevant for their development. |
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To add a page to this category, add a link to this page on the last line of the page. You can add multiple categories to a page. | After taking this course the student should have achieved the following objectives: 1. knows central terminology in the area. 2. is able to produce and document dimensional models for a data warehouse based on an informal domain description 3. from a given source, produce routines for data transfer into a data warehouse, and able to write queries to fetch data from a data warehouse using MDX. 4. is able to implement dimensional models in a given system, populate these models with data, and use a front-end systems for extracting and analyzing the data present in a data warehouse. Can use a given ETL system to extract data from different files and load it into relational tables 5. can summarize, present and assess results from research literature in the area |
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'''List of pages in this category:''' | == Syllabus == The course contains the following parts: 1. Introduction to decision support systems and Business Intelligence 2. History, state-of-the-art within data warehousing 3. Rationale and patterns for multi-dimensional modeling 4. The ETL process (Extract-Transform-Load) and data integration 5. The Data Warehouse Lifecycle (DW development and maintenance process) 6. OLAP (Online Analytical Processing) and a multi-dimensional query language for data cubes 7. Trends within data warehousing and current research |
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== Outline == The course contains a number of lectures, lessons, labs and tutorials. Course schedule with the different activities is provided in Daisy. Students have to do self studies (i.e. read the course literature) and carry out a number of practical assignments. The assignments are completed in groups. The lectures and lessons introduce the students to the theory needed for the completion of the assignments. Tutorials are offered to students working with the assignments. The assignments are reported and discussed during seminars or meetings. Note that this is not a distance course; hence students are strongly recommended to participate in the activities offered during the course. |
IS5 - Data warehousing
Requirements
90 hp Computer and Systems Sciences with at least:
- 7,5 hp database
Short description
The course discusses theories about - and implementation of - databases structured for data analysis, so called data warehouses. Data warehouses are used within Business Intelligence (BI) to support organizations strategic decision making by utilizing Decision Support Systems. This course will help you to design and implement data warehouses. You will learn different techniques and principles based on which you can design an effective and efficient data warehouse. In addition, you will experience the full data warehouse life-cycle by implementing a system according to latest advances in this area.
Aim
The general goal with this course is to familiarize the students with a special kind of information systems called Data Warehouses – their role, utilization and benefits for organisations, as well as architectures and underlying technologies relevant for their development.
After taking this course the student should have achieved the following objectives: 1. knows central terminology in the area. 2. is able to produce and document dimensional models for a data warehouse based on an informal domain description 3. from a given source, produce routines for data transfer into a data warehouse, and able to write queries to fetch data from a data warehouse using MDX. 4. is able to implement dimensional models in a given system, populate these models with data, and use a front-end systems for extracting and analyzing the data present in a data warehouse. Can use a given ETL system to extract data from different files and load it into relational tables 5. can summarize, present and assess results from research literature in the area
Syllabus
The course contains the following parts: 1. Introduction to decision support systems and Business Intelligence 2. History, state-of-the-art within data warehousing 3. Rationale and patterns for multi-dimensional modeling 4. The ETL process (Extract-Transform-Load) and data integration 5. The Data Warehouse Lifecycle (DW development and maintenance process) 6. OLAP (Online Analytical Processing) and a multi-dimensional query language for data cubes 7. Trends within data warehousing and current research
Outline
The course contains a number of lectures, lessons, labs and tutorials. Course schedule with the different activities is provided in Daisy. Students have to do self studies (i.e. read the course literature) and carry out a number of practical assignments. The assignments are completed in groups. The lectures and lessons introduce the students to the theory needed for the completion of the assignments. Tutorials are offered to students working with the assignments. The assignments are reported and discussed during seminars or meetings.
Note that this is not a distance course; hence students are strongly recommended to participate in the activities offered during the course.