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Student Guide
Analytics and Data Science (minor)
Basic information
Code:
SCI3073
Extent:
20–25
ECTS
Curriculum:
2020–2022
2022–2024
2024–2026
Previous curricula
Level:
Advanced studies
Language of learning:
English
Theme:
Global business dynamics
ICT and digitalisation
Target group:
All Aalto students
Teacher in charge:
Harri Lähdesmäki (SCI)
Pekka Malo (BIZ)
Administrative contact:
Anu Kuusela
Organising department:
-
School:
School of Science
Prerequisites:
No prerequisites for the minor as a whole, but some courses may have their own prerequisites. The prerequisites can be checked in the course descriptions; if in doubt, please consult the teacher of the course.
Quotas and restrictions:
No quotas for the minor; courses may have space for only a limited number of students. If you are not admitted to a course, you will have to choose another course.
Application process:
All Aalto master level students can choose this minor. However, some courses have limited number of seats and students are selected according to the order of priority confirmed in the course description. You should pay attention to the course-specific prerequisites and make sure you meet them in order to succeed in the courses.
Content and structure of the minor
We live in the information age, where a deluge of data is being generated by human activity, scientific data collection processes, business transactions, and adoption of new technologies. Distilling the information contained in such big volumes of data has the potential to transform science, technology, business, and arts, and to revolutionise the organisation and functioning of society. Data science is a new discipline that has emerged and its objective is to provide the students with knowledge of the underlying theory and with the necessary tools to cope with the data revolution. The goal of the Analytics and Data Science minor in Aalto is to educate students on becoming proficient in making sense of such big data, and in applying data analysis skills to their domain of expertise.
The minor is structured in four subareas. Students need to complete courses from different subareas, as indicated in the course description below.
SF: Statistical foundations
CM: Computational methods
BA: Business analytics
AP: Applications
| Code | Course name | ECTS | Period |
|---|---|---|---|
Choose at least one (1) course from the Statistical Foundations subarea |
|||
| CS-E5710 | Bayesian Data Analysis D | 5 | |
| MS-C1620 | Statistical Inference | 5 | |
| MS-C2128 | Prediction and Time Series Analysis | 5 | |
| MS-E2112 | Multivariate Statistical Analysis D | 5 | |
Choose at least one (1) course from the Computational Methods subarea |
|||
| CS-E4715 | Supervised Machine Learning | 5 | |
| CS-E4650 | Methods of Data Mining D | 5 | |
| CS-E4840 | Information Visualization D | 5 | |
| CS-E4190 | Cloud Software and Systems D | 5 | |
| CS-E4580 | Programming Parallel Computers D | 5 | |
| CS-E4800 | Artificial Intelligence D | 5 | |
| ELEC-E5431 | Large Scale Data Analysis D | 5 | |
Choose at least one (1) course from Business Analytics and Applications |
|||
| BA subarea | |||
| MS-E2135 | Decision Analysis D | 5 | |
| MARK-E0047 | Digital Marketing | 6 | |
| 30E03000 | Data Science for Business I D | 6 | |
| ISM-C2002 | MySQL for Data Analytics | 6 | |
| 57E00500 | Capstone: Business Intelligence | 6 | |
| 31E40100 | History of Economic Growth and Crises D | 6 | |
| AP subarea | |||
| CS-E5740 | Complex Networks D | 5 | |
| MS-E2177 | Seminar on Case Studies in Operations Research (V) D (in Finnish only) | 5 | |
| ELEC-E5510 | Speech Recognition D | 5 | |
| ELEC-E5550 | Statistical Natural Language Processing D | 5 | |
| 30E03500 | Data Science for Business II D | 6 | |
| ELEC-E5410 | Signal Processing for Communication | 5 | |
| ELEC-E5440 | Statistical Signal Processing D | 5 | |
| AP/BA subarea | |||
| 31E00910 | Applied Microeconometrics I D | 6 | |
| ECON-E0710 | Labor Economics I | 6 | |
| 31E16000 | Development Economics II | 6 | |
Previous curricula
Code: SCI3073
Extent: 20–25 ECTS
Language of instruction: English
Level: Masters
Target group: All Aalto students
Theme: Global business dynamics ICT and digitalisation
Teacher in charge: Harri Lähdesmäki Pekka Malo
Administrative contact: Anu Kuusela
Organising department:
Prerequisites: No prerequisites for the minor as a whole, but some courses may have their own prerequisites. The prerequisites can be checked in the course descriptions; if in doubt, please consult the teacher of the course.
Quotas and restrictions: No quotas for the minor; courses may have space for only a limited number of students. If you are not admitted to a course, you will have to choose another course.
Application process: All Aalto master level students can choose this minor. However, some courses have limited number of seats and students are selected according to the order of priority confirmed in the course description. You should pay attention to the course-specific prerequisites and make sure you meet them in order to succeed in the courses.
Content and structure of the minor
We live in the information age, where a deluge of data is being generated by human activity, scientific data collection processes, business transactions, and adoption of new technologies. Distilling the information contained in such big volumes of data has the potential to transform science, technology, business, and arts, and to revolutionise the organisation and functioning of society. Data science is a new discipline that has emerged and its objective is to provide the students with knowledge of the underlying theory and with the necessary tools to cope with the data revolution. The goal of the Analytics and Data Science minor in Aalto is to educate students on becoming proficient in making sense of such big data, and in applying data analysis skills to their domain of expertise.
The minor is structured in four subareas. Students need to complete courses from different subareas, as indicated in the course description below.
Structure
The minor is composed of elective courses in four subareas:
SF: Statistical foundations
CM: Computational methods
BA: Business analytics
AP: Applications
| Subarea | Code | Course name | ECTS credits |
|---|---|---|---|
| 1) AT LEAST ONE COURSE FROM THE SF SUBAREA | |||
| SF | CS-E5710 | Bayesian Data Analysis D | 5 |
| SF | MS-C1620 | Statistical Inference | 5 |
| SF | MS-C2128 | Prediction and Time Series Analysis* | 5 |
| SF | ISM-E1003 | Predictive Analytics D* (teaching 2022-2024 TBA) | 6 |
| SF | MS-E2112 | Multivariate Statistical Analysis D | 5 |
| 2) AT LEAST ONE COURSE FROM THE CM SUBAREA | |||
| CM | CS-E4710 | Machine Learning: Supervised Methods D | 5 |
| CM | CS-E4650 | Methods of Data Mining D | 5 |
| CM | CS-E4840 | Information Visualization D | 5 |
| CM | CS-E4190 | Cloud Software and Systems D | 5 |
| CM | CS-E4580 | Programming Parallel Computers D | 5 |
| CM | CS-E4830 | Kernel Methods in Machine Learning D NO TEACHING 2023-2024! | 5 |
| CM | CS-E4800 | Artificial Intelligence D | 5 |
| CM | ELEC-E5431 | Large Scale Data Analysis D | 5 |
| 3) SELECT AT LEAST ONE OF THE FOLLOWING | |||
| BA | MS-E2135 | Decision Analysis D | 5 |
| BA | MARK-E0047 | Digital Marketing | 6 |
| BA | 30E03000 | Data Science for Business I D | 6 |
| BA | ISM-C2002 | MySQL for Data Analytics | 6 |
| BA | 57E00500 | Capstone: Business Intelligence | 6 |
| BA | 31E40100 | History of Economic Growth and Crises D | 6 |
| AP | CS-E5740 | Complex Networks D | 5 |
| AP | MS-E2177 | Seminar on Case Studies in Operations Research (V) D (in Finnish only) | 5 |
| AP | ELEC-E5510 | Speech Recognition D | 5 |
| AP | ELEC-E5550 | Statistical Natural Language Processing D | 5 |
| AP | 30E03500 | Data Science for Business II D | 6 |
| AP | ELEC-E5410 | Signal Processing for Communication | 5 |
| AP | ELEC-E5440 | Statistical Signal Processing D | 5 |
| AP/BA | 31E00910 | Applied Microeconometrics I D | 6 |
| AP/BA | ECON-E0710 | Labor Economics I | 6 |
| AP/BA | 31E16000 | Development Economics II | 6 |
* The courses MS-C2128 and ISM-E1003 are alternative, i.e. the student can include only one of them in the degree.
Code: SCI3073
Extent:
- 20-25 cr for students of the Aalto schools of technology
- 24 cr for students of the School of Art, Design and Architecture and the School of Business
Language: English
Professors in charge: Professor Harri Lähdesmäki (SCI) & Assistant Professor Pekka Malo (BIZ)
Administrative contact: Anu Kuusela
Target group: All Aalto master students who want to sharpen their data analysis skills and be educated on the application of data science methods to different domains.
Application procedure: All Aalto master level students can choose this minor. However, some courses have limited number of seats and students are selected according to the order of priority confirmed in the course description. You should pay attention to the course-specific prerequisites and make sure you meet them in order to succeed in the courses.
Quotas: No quotas for the minor; courses may have space for only a limited number of students. If you are not admitted to a course, you will have to choose another course.
Prerequisites: No prerequisites for the minor as a whole, but some courses may have their own prerequisites. The prerequisites can be checked in the course descriptions; if in doubt, please consult the teacher of the course.
Content and structure of the minor
We live in the information age, where a deluge of data is being generated by human activity, scientific data collection processes, business transactions, and adoption of new technologies. Distilling the information contained in such big volumes of data has the potential to transform science, technology, business, and arts, and to revolutionise the organisation and functioning of society. Data science is a new discipline that has emerged and its objective is to provide the students with knowledge of the underlying theory and with the necessary tools to cope with the data revolution. The goal of the Analytics and Data Science minor in Aalto is to educate students on becoming proficient in making sense of such big data, and in applying data analysis skills to their domain of expertise.
The minor is structured in four subareas. Students need to complete courses from different subareas, as indicated in the course description below.
Structure
The minor is composed of elective courses in four subareas:
SF: Statistical foundations
CM: Computational methods
BA: Business analytics
AP: Applications
| Subarea | Code | Course name | ECTS credits | Period |
|---|---|---|---|---|
| 1) AT LEAST ONE COURSE FROM THE SF SUBAREA | ||||
| SF | CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| SF | MS-C1620 | Statistical Inference | 5 | III-IV |
| SF | MS-C2128 | Prediction and Time Series Analysis* | 5 | II |
| SF | ISM-E1003 | Predictive Analytics* | 6 | IV |
| SF | MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| 2) AT LEAST ONE COURSE FROM THE CM SUBAREA | ||||
| CM | CS-E4710 | Machine Learning: Supervised Methods | 5 | I-II |
| CM | CS-E4650 | Methods of Data Mining | 5 | I-II |
| CM | CS-E4840 | Information Visualization | 5 | IV |
| CM | CS-E4190 | Cloud Software and Systems | 5 | I-II |
| CM | CS-E4580 | Programming Parallel Computers | 5 | V |
| CM | CS-E4830 | Kernel Methods in Machine Learning | 5 | III-IV |
| CM | CS-E4800 | Artificial Intelligence | 5 | III-IV |
| CM | ELEC-E5422 | Convex Optimization I | 5 | I-II |
| CM | ELEC-E5431 | Large Scale Data Analysis | 5 | III-IV |
| 3) SELECT AT LEAST ONE OF THE FOLLOWING | ||||
| BA | MS-E2134 | Decision Making and Problem Solving | 5 | III-IV |
| BA | 23E47000 | Digital Marketing | 6 | I, V |
| BA | 30E03000 | Data Science for Business | 6 | III |
| BA | ISM-C2002 | MySQL for Data Analytics | 6 | I |
| BA | 57E00500 | Capstone: Business Intelligence | 6 | |
| BA | 31E40100 | History of Economic Growth and Crisis | 6 | II |
| AP | CS-E5740 | Complex Networks | 5 | I-II |
| AP | MS-C2103 | Design of Experiments and Statistical Models (in Finnish only) | 5 | III |
| AP | MS-E2177 | Seminar on Case Studies in Operation Research (in Finnish only) | 5 | III-V |
| AP | ELEC-E5510 | Speech Recognition | 5 | II |
| AP | GIS-E4020 | Advanced Spatial Analytics L | 5 | III-IV |
| AP | ELEC-E5550 | Statistical Natural Language Processing L | 5 | III-IV |
| AP | 30E03500 | Data Science for Business II | 6 | IV |
| AP | CS-E4870 | Research Project in Machine Learning and Data Science | 5-10 | I-II |
| AP | ELEC-E5410 | Signal Processing for Communication | 5 | I-II |
| AP | ELEC-E5440 | Statistical Signal Processing | 5 | I-II |
| AP/BA | 31E00910 | Applied Microeconometrics I | 6 | I |
| AP/BA | ECON-E0710 | Labor Economics I | 6 | IV |
| AP/BA | 31E16000 | Development Economics II | 6 | IV |
* The courses MS-C2128 and ISM-E1003 are alternative, i.e. the student can include only one of them in the degree.
Code: SCI3073
Extent:
- 20-25 cr for students of the Aalto schools of technology
- 24 cr for students of the School of Art, Design and Architecture and the School of Business
Language: English
Professors in charge: Professor Harri Lähdesmäki (SCI) & Assistant Professor Pekka Malo (BIZ)
Administrative contact: Anu Kuusela
Target group: All Aalto master students who want to sharpen their data analysis skills and be educated on the application of data science methods to different domains.
Application procedure: If you are interested in taking the minor, please send your confirmed HOPS to Study Coordinator Anu Kuusela ([email protected]).
Quotas: No quotas for the minor; separate courses may have space for only a limited number of students. If you are not admitted to a course, you will have to choose another course.
Prerequisites: No prerequisites for the minor as a whole, but some courses may have their own prerequisites. The prerequisites can be checked in the course descriptions; if in doubt, please consult the teacher of the course.
Content and structure of the minor
We live in the information age, where a deluge of data is being generated by human activity, scientific data collection processes, business transactions, and adoption of new technologies. Distilling the information contained in such big volumes of data has the potential to transform science, technology, business, and arts, and to revolutionise the organisation and functioning of society. Data science is a new discipline that has emerged and its objective is to provide the students with knowledge of the underlying theory and with the necessary tools to cope with the data revolution. The goal of the Analytics and Data Science minor in Aalto is to educate students on becoming proficient in making sense of such big data, and in applying data analysis skills to their domain of expertise.
The minor is structured in four subareas. Students need to complete courses from different subareas, as indicated in the course description below.
Structure
The minor is composed of elective courses in four subareas:
SF: Statistical foundations
CM: Computational methods
BA: Business analytics
AP: Applications
| Subarea | Code | Course name | ECTS credits | Period |
|---|---|---|---|---|
| 1) COMPULSORY COURSE | ||||
| CS-E4620 | Introduction to Analytics and Data Science | 2 | I | |
| The course is removed from the course selection and it is no longer required as compulsory course of the Analytics and Data Science minor. | ||||
| 2) AT LEAST ONE COURSE FROM THE SF SUBAREA | ||||
| SF | CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| SF | MS-C1620 | Statistical Inference | 5 | III-IV |
| SF | MS-C2128 | Prediction and Time Series Analysis* | 5 | II |
| SF | 30E00800 | Time Series Analysis* | 6 | IV-V |
| SF | MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| 3) AT LEAST ONE COURSE FROM THE CM SUBAREA | ||||
| CM | CS-E3210 | Machine Learning: Basic Principles | 5 | I-II |
| CM | CS-E4600 | Algorithmic Methods of Data Mining | 5 | I-II |
| CM | CS-E4840 | Information Visualization | 5 | IV |
| CM | CS-E4640 | Big Data Platforms | 5 | I-II |
| CM | CS-E4100 | Mobile Cloud Computing | 5 | I-II |
| CM | CS-E4580 | Programming Parallel Computers | 5 | V |
| CM | CS-E4830 | Kernel Methods in Machine Learning | 5 | III-IV |
| CM | CS-E4800 | Artificial Intelligence | 5 | III-IV |
| CM | ELEC-E5422 | Convex Optimization I | 5 | I-II |
| CM | ELEC-E5431 | Large Scale Data Analysis | 5 | III-IV |
| 4) SELECT AT LEAST ONE OF THE FOLLOWING | ||||
| BA | MS-E2134 | Decision Making and Problem Solving | 5 | III-IV |
| BA | 23E47000 | Digital Marketing | 6 | I, V |
| BA | 30E03000 | Data Science for Business | 6 | III |
| BA | 37E01600 | Data Resources Management | 6 | I |
| BA | 57E00500 | Capstone: Business Intelligence | 6 | |
| BA | 31E00920 | Applied Microeconomics II | 6 | Not lectured 2018-2019 or 2019-2020 |
| BA | 31E40100 | History of Economic Growth and Crisis | 6 | II |
| AP | CS-E5740 | Complex Networks | 5 | II |
| AP | MS-C2103 | Design of Experiments and Statistical Models (in Finnish only) | 5 | III |
| AP | MS-E2177 | Seminar on Case Studies in Operation Research (in Finnish only) | 5 | III-V |
| AP | ELEC-E5510 | Speech Recognition | 5 | II |
| AP | GIS-E4020 | Advanced Spatial Analytics L | 5 | III-IV |
| AP | ELEC-E5550 | Statistical Natural Language Processing L | 5 | III-IV |
| AP | 30E03500 | Data Science for Business II | 6 | IV |
| AP | CS-E4870 | Research Project in Machine Learning and Data Science | 5-10 | I-II |
| AP | ELEC-E5410 | Signal Processing for Communication | 5 | I-II |
| AP | ELEC-E5440 | Statistical Signal Processing | 5 | I-II |
| AP/BA | 31E00910 | Applied Microeconometrics I | 6 | I |
| AP/BA | 31E00700 | Labor Economics | 6 | IV |
| AP/BA | 31E40200 | Economics of Science and Innovation | 6 | Not lectured 2018-2019 or 2019-2020 |
| AP/BA | 31E16000 | Development Economics II | 6 | IV |
* The courses MS-C2128 and 30E00800 are alternative, i.e. the student can include only one of them in the degree.
Basic information of the minor
Code: SCI3073
Extent:
- 20-25 cr for students of the Aalto schools of technology
- 24 cr for students of the School of Art, Design and Architecture and the School of Business
Language: English
Professors in charge: Associate Professor Aristides Gionis (SCI) & Assistant Professor Pekka Malo (BIZ)
Target group: All Aalto master students who want to sharpen their data analysis skills and be educated on the application of data science methods to different domains.
Application procedure: If you are interested in taking the minor, please send your confirmed HOPS to Study Coordinator Anu Kuusela ([email protected]).
Quotas: No quotas for the minor; separate courses may have space for only a limited number of students. If you are not admitted to a course, you will have to choose another course.
Prerequisites: No prerequisites for the minor as a whole, but some courses may have their own prerequisites. The prerequisites can be checked in the course descriptions; if in doubt, please consult the teacher of the course.
Content and structure of the minor
We live in the information age, where a deluge of data is being generated by human activity, scientific data collection processes, business transactions, and adoption of new technologies. Distilling the information contained in such big volumes of data has the potential to transform science, technology, business, and arts, and to revolutionise the organisation and functioning of society. Data science is a new discipline that has emerged and its objective is to provide the students with knowledge of the underlying theory and with the necessary tools to cope with the data revolution. The goal of the Analytics and Data Science minor in Aalto is to educate students on becoming proficient in making sense of such big data, and in applying data analysis skills to their domain of expertise.
The minor is structured in four subareas. Students need to complete courses from different subareas, as indicated in the course description below.
Structure
The minor is composed of elective courses in four subareas:
SF: Statistical foundations
CM: Computational methods
BA: Business analytics
AP: Applications
| Subarea | Code | Course name | ECTS credits | Period |
|---|---|---|---|---|
| 1) COMPULSORY COURSE | ||||
| CS-E4620 | Introduction to Analytics and Data Science | 2 | I | |
| 2) AT LEAST ONE COURSE FROM THE SF SUBAREA | ||||
| SF | CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| SF | MS-C2104 | Introduction to Statistical Inference | 5 | III-IV |
| SF | MS-C2128 | Prediction and Time Series Analysis (in Finnish only) * | 5 | II |
| SF | 30E00800 | Time Series Analysis* | 6 | IV-V |
| SF | MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| 3) AT LEAST ONE COURSE FROM THE CM SUBAREA | ||||
| CM | CS-E3210 | Machine Learning: Basic Principles | 5 | I-II |
| CM | CS-E4600 | Algorithmic Methods of Data Mining | 5 | I-II |
| CM | CS-E4840 | Information Visualization | 5 | IV |
| CM | CS-E4120 | Scalable Cloud Computing | 5 | I-II |
| CM | CS-E4100 | Mobile Cloud Computing | 5 | I-II |
| CM | CS-E4580 | Programming Parallel Computers | 5 | V |
| CM | CS-E4830 | Kernel Methods in Machine Learning | 5 | I-II |
| CM | CS-E4800 | Artificial Intelligence | 5 | III-IV |
| 4) SELECT AT LEAST ONE OF THE FOLLOWING | ||||
| BA | MS-E2134 | Decision Making and Problem Solving | 5 | I |
| BA | 23E47000 | Digital Marketing | 6 | I, V |
| BA | 30E03000 | Data science for Business | 6 | IV |
| BA | 37E01600 | Data Resources Management | 6 | III |
| BA | 37E00550 | Business Intelligence | 6 | IV |
| BA | 31E00920 | Applied Microeconomics II | 6 | IV |
| BA | 31E40100 | History of Economic Growth and Crisis | 6 | II |
| AP | CS-E5740 | Complex Networks | 5 | II |
| AP | MS-C2103 | Design of Experiments and Statistical Models (in Finnish only) | 5 | III |
| AP | MS-E2177 | Seminar on Case Studies in Operation Research (in Finnish only) | 5 | III-IV |
| AP | ELEC-E5510 | Speech Recognition | 5 | II |
| AP | GIS-E4020 | Advanced Spatial Analytics L | 5 | V |
| AP | ELEC-E5550 | Statistical Natural Language Processing L | 5 | III-IV |
| AP | 30E03500 | Data Science for Business II | 6 | IV |
| AP | CS-E4870 | Research Project in Machine Learning and Data Science | 5-10 | I-II |
| AP/BA | 31E00910 | Applied Microeconometrics I | 6 | II |
| AP/BA | 31E00700 | Labor Economics | 6 | V |
| AP/BA | 31E40200 | Economics of Science and Innovation | 6 | III |
| AP/BA | 31E16000 | Development Economics II | 6 | IV |
* The courses MS-C2128 and 30E00800 are alternative, i.e. the student can include only one of them in the degree.
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