Financial Engineering (minor)
Basic information
Code:
Extent:
Curriculum:
Level:
Language of learning:
Theme:
Target group:
Teacher in charge:
Administrative contact:
Organising department:
School:
Prerequisites:
The minor requires basic knowledge in engineering mathematics and probability theory. Bachelor-level minor SCI3034 Systems Sciences is warmly recommended.
Quotas and restrictions:
No quotas for the minor, but some elective courses may have space for a limited number of students. The minor is available for master’s level students.
Application process:
Open for all students of Aalto University. It is recommended that the student discuss the choice of courses with Ruth Kaila.
Content and structure of the minor
N.B.! This minor is discontinued starting from the 2024-2026 curriculum. If you have already started completing the minor, you can finish it. Notice that due to the new curriculum, 2024-2026 course offerings may also change.
Financial engineering is the application of quantitative methods to solve problems arising in the practice of finance. As a discipline, it combines financial theory and applied mathematics with the use of computational methods and tools.
Finance is becoming increasingly demanding due to the continual development of new financial products, availability of big data sets, rapid technological advances, and changes in regulation. This has created a growing demand for financial risk management and opportunities for advanced quantitative analysis.
The minor in Financial Engineering provides students with a good understanding of financial mathematics and equips them with methodological and computational skills. Thus, the students are set to pursue successful careers in banking, insurance, risk management and consulting, or to become quantitative analysts for instance in the finance departments of manufacturing and service firms.
The minor consist of 10–15 credits of compulsory core courses and 5-15 credits of elective courses from three subject areas (i) systems and operations research, (ii) computational methods, (iii) advanced topics in finance.
Core courses (10–15 cr)
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mandatory: | |||
| TU-E2210 | Financial Engineering I D | 5 | I-II |
| One or two of the following courses: |
|||
| TU-E2220 | Financial Engineering II D | 5 | III-IV |
| MS-E2114 | Investment Science D | 5 | I-II |
Optional courses (5-15 cr)
The credit requirement is fullfilled by completing a sufficient number of courses from the following. Any courses except those from the subject area of the student's major can be selected.
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
|
Mathematical methods (Dept of Mathematics and Systems Analysis) |
|||
| MS-C2105 | Introduction to Optimization | 5 | IV |
| MS-C2111 | Stochastic Processes | 5 | II |
| MS-E2112 | Multivariate Statistical Analysis D | 5 | III-IV |
| MS-E2117 | Riskianalyysi D | 5 | III-IV |
| MS-E2177 | Seminar on Case Studies in Operation Research (V) D | 5-7 | III-V |
| Computational Methods (Dept of Computer Science) | |||
| Choose one of the following courses in Machine Learning: | |||
| CS-C3240 | Machine Learning D | 5 | I |
| OR | |||
| CS-E4710 | Machine Learning: Supervised Methods D | 5 | I-II |
| CS-E4650 | Methods of Data Mining D | 5 | I-II |
| CS-E4890 | Deep Learning D | 5 | IV-V |
| CS-E4830 | Kernel Methods in Machine Learning D | 5 | IV-V |
| CS-E5710 | Bayesian Data Analysis D | 5 | I-II |
| Any course in programming | |||
| Selected topics in Finance (Dept of Finance) and Financial Engineering (Dept of Industrial Engineering and Management) | |||
| TU-E2230 | Machine Learning in Financial Engineering D | 3-6 | III-IV |
| FIN-A0104 | Fundamantals of Investments | 6 | II |
| FIN-A0103 | Fundamentals in Corporate Finance | 6 | I |
| FIN-A0105 | Fundamentals in Financial Markets and Institutions | 6 | III |
| TU-EV | Course with varying content related to Financial Engineering** |
* If you have completed the course MS-E2114 Investment Science, you can't choose FIN-A0104 Fundamentals in Investments as these courses are largely overlapping.
** to be agreed in advance with coordinating teacher of the minor.
Previous curricula
Code: SCI3086
Extent: 20-25 cr
Language: English
Organizing department: Industrial Engineering and Management
Teachers in charge: Mikko Jääskeläinen, Ruth Kaila (coordinating teacher), Ahti Salo
Administrative contact: Tarja Timonen
Target group: All master's level students with sufficient prerequisite knowledge
Application procedure: Open for all students of Aalto University. It is recommended that the student discuss the choice of courses with Ruth Kaila.
Quotas and restrictions: No quotas for the minor, but some elective courses may have space for a limited number of students. The minor is available for master’s level students.
Prerequisites: The minor requires basic knowledge in engineering mathematics and probability theory. Bachelor-level minor SCI3034 Systems Sciences is warmly recommended.
Content and structure of the minor
Financial engineering is the application of quantitative methods to solve problems arising in the practice of finance. As a discipline, it combines financial theory and applied mathematics with the use of computational methods and tools.
Finance is becoming increasingly demanding due to the continual development of new financial products, availability of big data sets, rapid technological advances, and changes in regulation. This has created a growing demand for financial risk management and opportunities for advanced quantitative analysis.
The minor in Financial Engineering provides students with a good understanding of financial mathematics and equips them with methodological and computational skills. Thus, the students are set to pursue successful careers in banking, insurance, risk management and consulting, or to become quantitative analysts for instance in the finance departments of manufacturing and service firms.
The minor consist of 10-15 credits of compulsory core courses and 5-15 credits of elective courses from three subject areas (i) systems and operations research, (ii) computational methods, (iii) advanced topics in finance.
Core courses (10-15 cr)
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mandatory: | |||
| TU-E2210 | Financial Engineering I | 5 | I-II |
| One or two of the following courses: |
|||
| TU-E2220 | Financial Engineering II | 5 |
III 2021-2022: III-IV |
| MS-E2114 | Investment Science | 5 | I-II |
Elective courses (5-15 cr)
The credit requirement is fullfilled by completing sufficiently many courses from the following. Any courses except those from the subject area of the student's major can be selected.
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mathematical methods (Dept of Mathematics and Systems Analysis) | |||
| MS-C2105 | Introduction to Optimization | 5 | IV |
| MS-C2111 | Stochastic Processes | 5 | I |
| MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| MS-E2117 | Risk Analysis | 5 | III-IV |
| MS-E2177 | Seminar on Case Studies in Operation Research | 5-7 | III-IV |
| Computational Methods (Dept of Computer Science) | |||
| Choose of one the following: | |||
| CS-C3240 | Machine Learning | 5 | III-IV |
| OR | |||
| CS-E4710 | Machine Learning: Supervised Methods | 5 | I-II |
| CS-E4650 | Methods of Data Mining | 5 | I-II |
| CS-E4890 | Deep Learning | 5 | IV-V |
| CS-E4830 | Kernel Methods in Machine Learning | 5 | III-V |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| Any course in programming | |||
| Selected topics in Finance (Dept of Finance) | |||
| 28C00300 | Investment Management *, ** | 6 | III |
| 28E35100 | Corporate Financial Management** | 6 | I |
| 28C00800 | Financial Markets and Institutions** | 6 | II |
| TU-EV | Course with varying content |
*If you have completed the course MS-E2114 Investment Science, you can't choose 28C00300 Investment Management as these courses are largely overlapping
**Limited admission based on study success; applications should be sent to the coordinating teacher, ruth.kaila(at)aalto.fi, latest one week before the course starts.
Code: SCI3086
Extent: 20-25 cr
Language: English
Organizing department: Industrial Engineering and Management
Teachers in charge: Mikko Jääskeläinen, Ruth Kaila (coordinating teacher), Ahti Salo
Administrative contact: Tarja Timonen
Target group: All master's level students with sufficient prerequisite knowledge
Application procedure: Open for all students of Aalto University. It is recommended that the student discuss the choice of courses with Ruth Kaila.
Quotas and restrictions: No quotas for the minor, but some elective courses may have space for a limited number of students. The minor is available for master’s level students.
Prerequisites: The minor requires basic knowledge in engineering mathematics and probability theory. Bachelor-level minor SCI3034 Systems Sciences is warmly recommended.
Content and structure of the minor
Financial engineering is the application of quantitative methods to solve problems arising in the practice of finance. As a discipline, it combines financial theory and applied mathematics with the use of computational methods and tools.
Finance is becoming increasingly demanding due to the continual development of new financial products, availability of big data sets, rapid technological advances, and changes in regulation. This has created a growing demand for financial risk management and opportunities for advanced quantitative analysis.
The minor in Financial Engineering provides students with a good understanding of financial mathematics and equips them with methodological and computational skills. Thus, the students are set to pursue successful careers in banking, insurance, risk management and consulting, or to become quantitative analysts for instance in the finance departments of manufacturing and service firms.
The minor consist of 10-15 credits of compulsory core courses and 5-15 credits of elective courses from three subject areas (i) systems and operations research, (ii) computational methods, (iii) advanced topics in finance.
Core courses (10-15 cr)
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mandatory: | |||
| TU-E2210 | Financial Engineering I | 5 | I-II |
| One or two of the following courses: |
|||
| MS-E2114 | Investment Science* | 5 | I-II |
| TU-E2220 | Financial Engineering II | 5 | III |
Elective courses (5-15 cr)
The credit requirement is fullfilled by completing sufficiently many courses from the following. Any courses except those from the subject area of the student's major can be selected.
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mathematical methods (Dept of Mathematics and Systems Analysis) | |||
| MS-C2105 | Introduction to Optimization | 5 | IV |
| MS-C2111 | Stochastic Processes | 5 | I |
| MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| MS-E2117 | Risk Analysis | 5 | III-IV |
| MS-E2177 | Seminar on Case Studies in Operation Research | 5-7 | III-IV |
| Computational Methods (Dept of Computer Science) | |||
| CS-E3210 | Machine Learning: Basic Principles | 5 | I-II |
| CS-E4600 | Algorithmic Methods for Data Mining | 5 | I-II |
| CS-E4890 | Deep Learning | 5 | IV-V |
| CS-E4830 | Kernel Methods in Machine Learning | 5 | III-V |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| Any course in programming | |||
| Selected topics in Finance (Dept of Finance) | |||
| 28C00300 | Investment Management *, ** | 6 | III |
| 28E35100 | Corporate Financial Management** | 6 | I |
| 28C00800 | Financial Markets and Institutions** | 6 | II |
| TU-EV | Course with varying content |
*Choose either MS-E2114 or 28C00300 but not both as they are largely overlapping.
**Limited admission bassed on study success; applications should be sent to the coordinating teacher, ruth.kaila(at)aalto.fi, latest one week before the course starts.
Code: SCI3086
Extent: 20-25 cr
Language: English
Teachers in charge: Mikko Jääskeläinen, Ruth Kaila (coordinating teacher), Ahti Salo
Target group: All master's level students with sufficient prerequisite knowledge
Application procedure: Open for all students of Aalto University. It is recommended that the student discuss the choice of courses with Ruth Kaila.
Quotas and restrictions: No quotas for the minor, but some elective courses may have space for a limited number of students. The minor is available for master’s level students.
Prerequisites: The minor requires basic knowledge in engineering mathematics and probability theory. Bachelor-level minor SCI3034 Systems Sciences is warmly recommended.
Content and structure of the minor
Financial engineering is the application of quantitative methods to solve problems arising in the practice of finance. As a discipline, it combines financial theory and applied mathematics with the use of computational methods and tools.
Finance is becoming increasingly demanding due to the continual development of new financial products, availability of big data sets, rapid technological advances, and changes in regulation. This has created a growing demand for financial risk management and opportunities for advanced quantitative analysis.
The minor in Financial Engineering provides students with a good understanding of financial mathematics and equips them with methodological and computational skills. Thus, the students are set to pursue successful careers in banking, insurance, risk management and consulting, or to become quantitative analysts for instance in the finance departments of manufacturing and service firms.
The minor consist of 10-15 credits of compulsory core courses and 5-15 credits of elective courses from three subject areas (i) systems and operations research, (ii) computational methods, (iii) advanced topics in finance.
Core courses (10-15 cr)
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mandatory: | |||
| TU-E2210 | Financial Engineering I | 5 | I-II |
| One or two of the following courses: |
|||
| MS-E2114 | Investment Science | 5 | I-II |
| TU-E2220 | Financial Engineering II | 5 | III |
Elective courses (5-15 cr)
The credit requirement is fullfilled by completing sufficiently many courses from the following. Any courses except those from the subject area of the student's major can be selected.
| Code | Course name | ECTS credits | Period |
|---|---|---|---|
| Mathematical methods (Dept of Mathematics and Systems Analysis) | |||
| MS-C2105 | Optimoinnin perusteet | 5 | IV |
| MS-C2111 | Stokastiset prosessit | 5 | I |
| MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| MS-E2117 | Risk Analysis | 5 | III-IV |
| MS-E2177 | Seminar on Case Studies in Operation Research | 5 | III-IV |
| Computational Methods (Dept of Computer Science) | |||
| CS-E3210 | Machine Learning: Basic Principles | 5 | I-Ii |
| CS-E4600 | Algorithmic Methods for Data Mining | 5 | I-II |
| CS-E4890 | Deep Learning | 5 | II |
| CS-E4830 | Kernel Methods in Machine Learning | 5 | I-II |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| Any course in programming | |||
| Selected topics in Finance (Dept of Finance) | |||
| 28C00300 | Investment Management* | 6 | III |
| 28E35100 | Corporate Financial Management* | 6 | I |
| 28C00800 | Financial Markets and Institutions* | 6 | II |
| TU-EV | Course with varying content |
*Limited admission bassed on study success; applications should be sent to the coordinating teacher, ruth.kaila(at)aalto.fi, latest one week before the course starts.
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