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Department of Engineering
Dean:	Dr. Ármann Gylfason
Email:	ru@ru.is
Website:	http://www.ru.is/tvd
Teachers	View

MSc in Financial Engineering
Semesters:	4
Years:	2
ETCS:	120
About major	Fjármálaverkfræði er þverfagleg grein sem samþættir fjármálafræði við verkfræðilegar aðferðir, stærðfræðileg líkön, tölfræði, aðgerðagreiningu og hagnýta tölvunarfræði. Nám í fjármálaverkfræði hentar þeim sem hafa áhuga á t.d. áhættustýringu, afleiðuviðskiptum eða fjárstýringu. MSc námið er mótað að kröfum Verkfræðingafélags Íslands um fullnaðarmenntun í verkfræði.
Learning Outcomes	View
Education cycle	2

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	Mandatory course on major		Teaching language
	Optional course on major		Prerequisites for course

Vorönn/Spring 2024

Energy Financial Assessment

SE-833-FA2

ECTS 6

Engineering Optimization

T-423-ENOP

ECTS 6

Sleep

T-424-SLEE

ECTS 6

Decision Analysis for Management

T-603-AKVA

ECTS 6

Internship in Engineering I

T-706-INT1

ECTS 6

Internship in Engineering II

T-706-INT2

ECTS 6

Systems Biology

T-765-SYBI

ECTS 8

Project Management and Strategic Planning

T-803-VERK

ECTS 8

Derivatives and risk management

T-814-DERI

ECTS 8

Creating a Complete Business Plan for a Technical Idea - Entrepreneurship and the Innovation Process

T-814-INNO

ECTS 8

Modern Trends in Financial Engineering

T-816-MTFE

ECTS 8

Tissue Engineering and Biomaterials

T-828-TISS

ECTS 8

Graduate Research Opportunities I

T-829-GRO1

ECTS 6

Finite Element Analysis in Engineering

T-844-FEMM

ECTS 8

Neural Engineering

T-863-NEUR

ECTS 8

Wind Power

T-863-WIND

ECTS 8

Power System Operation

T-867-POSY

ECTS 8

Stability and Control in Electric Power Systems

T-867-STAB

ECTS 8

Protection Philosophy for Smart-Grids

T-868-PROT

ECTS 8

MSc Thesis

T-899-MEIS

ECTS 30

MSc Thesis

T-900-MEIS

ECTS 30

MSc thesis II

T-901-MEI2

ECTS 30

Business Process Management

V-716-BPMA

ECTS 7,5

Entrepreneurial Finance

V-733-ENTR

ECTS 7,5

Business Intelligence and Analytics

V-784-REK5

ECTS 7,5

Year

1. year

Semester

Spring 2024

Level of course

N/A

Type of course

Elective

Prerequisites

V-715-ABIN, Advanced Business Informatics

Schedule

No schedule found.

Lecturer

Jón Bjarki Gunnarsson

Content

In the modern business world, decisions can no longer be solely based on gut feelings and personal data. Rapid changes in customer preferences, unlimited access to information, mounting competitor pressures and evolving technology, make decision support for managers a critical function in every company. The field of business intelligence (BI), analytics and decision support systems has evolved from personal support tools to company wide applications, technology solutions, functions and competencies.
This course deals with the technologies, competencies and solutions that support managerial work and decision making. The course provides a theoretical basis of decision making, describes how information technology can support and improve such decision making, what types of decision support systems there are, the nature and evolution of business intelligence, the tools and techniques of managerial decision support and describes some of the emerging technologies and trends in the business.The course discusses the following parts:· Managerial and decision support – Theoretical discussions and basis· Discussions about data assets, ethical concerns of managing and controlling information· Managing and measuring the quality of data· Data Warehousing, fundamentals of data modeling and the handling of master data· Fundamental concepts of datamining, artificial intelligence, machine learning and data predictions· Business performance management, analytics and reporting and its relation to data management and decision making· Various emerging trends in the BI business.The course should provide the student with a framework to understand decision support and business intelligence solutions, -work and projects. Based on textbook readings, cases and projects the student learns to apply this framework. The course will focus on generic approaches to decision support and business intelligence but will also include discussions on real life examples from Icelandic as well as foreign companies. Furthermore, there are planned sessions with companies which will describe their experiences with implementing decision support, business intelligence and performance management solutions.Please note that this course contains fair amount of technical details and -discussion. Experience has shown that the material is better suited for students with some prior knowledge and preparation from IT related courses or similar experience from work.

Learning outcome - Objectives

Collection of facts, concepts, theories and techniques acquired by students.
The student should be able to:
• Identify the major frameworks of computerized decision support, decision support systems (DSS), data analytics and business intelligence (BI).
• Describe the nature of information and data, explain their basic difference and its use in managerial decision making
• Explain the context and importance of gathering and managing data in the society and economy.
• Describe the core concepts of decision making and discuss how IT systems can support effective decision making.
• Discuss and know the main building blocks of data management systems as well as List the definitions, concepts, and architectures of data warehousing
• Describe the basic concepts of advanced analytics methods and understand their application
• Identify the major ethical and legal issues of analytics and data management
Skills:
During the course, student will have acquired the skills to perform various tasks of a BI and Information management specialists. The student should:
• Be able to critically evaluate strengths and weaknesses of BI projects
• Know the basic building blocks of a good BI program and how to apply them correctly
• Gathered knowhow on basic BI tools and evaluate their fit for purpose
• Know BI project best practices for BI projects and know how to apply them
Competences:
The student will have acquired the ability to apply knowledge and skills in Business Intelligence projects and regular business settings. The student should be able to:
• Effectively communicate course work in writing and oral presentation.
• Participate actively in BI projects and be ready to apply best practices BI work
• Make judgement on quality of data and assess the its impact on decision making.
• Create and participate in data quality projects through various roles
• Participate in gathering information on and create high quality KPIs
• Act as an active member BI teams focusing on information management

Course assessment

Participation and attendance – 20%Mid term project – 30%-Final exam – 50%

Reading material

No reading material found.

Teaching and learning activities

Teaching will be combined of various approaches:· Lectures from teacher. Presentations will consists on PPT slides, some videos and discussion points.· Lectures from guest lecturers-practitioners. At least two guest lecturers will pay a visit.· Case discussions based on cases from within the textbook or from outside resources and exercises. Students will be asked to present their findings both as individuals and in groups.· Class presentations of group project and “Teach Back” where students will have the opportunity to teach/study each other’s delivery of a case studyA big emphasize is put on students’ participation and good preparation.

Language of instruction

English

Entrepreneurship and Starting New Ventures

X-204-STOF

ECTS 6

Exchange Studies

X-699-EXCH

ECTS 30

Sumar/Summer 2024

Graduate Research Opportunities I

T-829-GRO1

ECTS 6