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Data Science (minor)
Basic information
Code:
SCI3096
Extent:
20–25
ECTS
Curriculum:
2024–2026
2022–2024
2020–2022
Previous curricula
Level:
Intermediate studies
Language of learning:
English
Theme:
ICT and digitalisation
Mathematics and research methods
Target group:
All Aalto students
Teacher in charge:
Aki Vehtari
Administrative contact:
Paavo Nisula
Organising department:
-
School:
School of Science
Prerequisites:
Basic courses in engineering mathematics (or equivalent knowledge) and either CS-A1110 Programming 1 (follow-up course CS-A1120) or CS-A1111/CS-A1113 Basics in Programming Y1 (follow-up course CS-A1121/ CS-A1123). Students are requested to check the prerequisites of the courses before signing up. Some of the optional courses for the minor subject are quite demanding.
Quotas and restrictions:
-
Application process:
-
Content and structure of the minor
Content
| Code | Course name | ECTS | Period / Year |
|---|---|---|---|
Choose one of the following (see the instructions above) 5 ECTS |
|||
| CS-A1120 | Programming 2* | 5 | IV-V |
| CS-A1121/ CS-A1123 |
Basics in Programming Y2* | 5 | IV-V |
Compulsory courses 10 ECTS |
|||
| CS-C3240 | Machine Learning | 5 | I |
| CS-A1155 | Databases for Data Science | 5 | IV-V |
Optional courses 5-10 ECTS |
|||
| CS-E5480 | Digital Ethics | 3-5 | V |
| MS-C1620 | Statistical Inference | 5 | III-IV |
| CS-C3120 | Human-Computer Interaction | 5 | I-II |
| MS-C2128 | Prediction and Time Series Analysis | 5 | II |
| CS-E3190 | Principles of Algorithmic Techniques | 5 | I-II |
| CS-E4650 | Methods of Data Mining | 5 | I-II |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II |
| CS-E4800 | Artificial Intelligence | 5 | III-IV |
| CS-E4840 | Information Visualization | 5 | IV |
* Students who have Programming 2 in their basic studies, cannot include Basics in Programming Y2 in the minor. If Programming 2 or Basics in Programming Y2 are placed elsewhere in the degree, students should choose one more optional course instead.
Previous curricula
Code: SCI3096
Extent: 20–25 ECTS
Language of instruction: English
Level: Bachelor
Target group: All Aalto students
Theme: ICT and digitalisation Mathematics and research methods
Teacher in charge: Professor Aki Vehtari
Administrative contact: Paavo Nisula
Organising department:
Prerequisites: Basic courses in engineering mathematics (or equivalent knowledge) and either CS-A1110 Programming 1 (follow-up course CS-A1120) or CS-A1111/CS-A1113 Basics in Programming Y1 (follow-up course CS-A1121/ CS-A1123). Students are requested to check the prerequisites of the courses before signing up. Some of the optional courses for the minor subject are quite demanding.
Quotas and restrictions: No quotas.
Application process: Open for all students of Aalto University.
Content and structure of the minor
| Code | Course name | ECTS credits | Period | Year |
|---|---|---|---|---|
| Choose one of the following (see the instructions above) | 5 | |||
| CS-A1120 | Programming 2* | 5 | IV-V | year 1 |
| CS-A1121/ CS-A1123 |
Basics in Programming Y2* | 5 | IV-V | year 1 |
| Compulsory courses | 10 | |||
| CS-C3240 | Machine Learning D | 5 | I | year 2 |
| MS-C1342 | Linear Algebra | 5 | V | year 2 |
| Optional courses | 5-10 | |||
| MS-C1620 | Statistical Inference | 5 | III-IV | |
| CS-C3120 | Human-Computer Interaction | 5 | I-II | |
| MS-C2128 | Prediction and Time Series Analysis | 5 | II | |
| CS-E3190 | Principles of Algorithmic Techniques D | 5 | I-II | |
| CS-E4650 | Methods of Data Mining D | 5 | I-II | |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II | |
| CS-E4800 | Artificial Intelligence D | 5 | III-IV | |
| CS-E4840 | Information Visualization D | 5 | IV |
* Students who have Programming 2 in their basic studies, cannot include Basics in Programming Y2 in the minor. If Programming 2 or Basics in Programming Y2 are placed elsewhere in the degree, students should choose one more optional course instead.
Code: SCI3096
Extent: 20-25 credits
Language: English
Teacher in charge: Prof. Aki Vehtari
Administrative contact: Planning Officer Paavo Nisula
Target group: All Aalto students, with sufficient prerequisite knowledge. Not for Data Science major students. M.Sc. students should check with their own study programme that this minor can be included in the degree.
Application procedure: Open for all students of Aalto University.
Quotas and restrictions: No quotas.
Prerequisites: Basic courses in engineering mathematics (or equivalent knowledge) and either CS-A1110 Programming 1 (follow-up course CS-A1120) or CS-A1111/CS-A1113 Basics in Programming Y1 (follow-up course CS-A1121/ CS-A1123). Students are requested to check the prerequisites of the courses before signing up. Some of the optional courses for the minor subject are quite demanding.
Content and structure of the minor
| Code | Course name | ECTS credits | Period | Year |
|---|---|---|---|---|
| Choose one of the following (see the instructions above), 5 cr: | ||||
| CS-A1120 | Programming 2* | 5 | IV-V | year 1 |
| CS-A1121 (in Finnish)/ CS-A1123 (in English) |
Basics in Programming Y2* | 5 | IV-V | year 1 |
| Compulsory courses, 10 cr: | ||||
| CS-C3240 | Machine Learning | 5 | III-IV | year 2 |
| MS-C1342 | Linear algebra | 5 | V | year 2 |
| Optional courses. Choose 5-10 cr: | ||||
| MS-C1620 | Statistical Inference | 5 | III-IV | |
| CS-C3120 | Human-Computer Interaction | 5 | I-II | |
| MS-C2128 | Prediction and time-series analysis | 5 | II | |
| CS-E3190 | Principles of Algorithmic Techniques | 5 | I-II | |
| CS-E4650 | Methods of Data Mining | 5 | I-II | |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II | |
| CS-E4800 | Artificial Intelligence | 5 | III-IV | |
| CS-E4840 | Information Visualization | 5 | IV |
*Students who have Programming 2 in their basic studies, cannot include Basics in Programming Y2 in the minor. If Programming 2 or Basics in Programming Y2 are placed elsewhere in the degree, students should choose one more optional course instead.
Code: SCI3096
Extent: 20-25 credits
Language: English
Teacher in charge: Prof. Aki Vehtari
Administrative contact: Planning Officer Paavo Nisula
Target group: All Aalto students, with sufficient prerequisite knowledge. Not for Data Science major students. M.Sc. students should check with their own study programme that this minor can be included in the degree.
Application procedure: Open for all students of Aalto University.
Quotas and restrictions: No quotas.
Prerequisites: Basic courses in engineering mathematics (or equivalent knowledge) and either CS-A1110 Programming 1 (follow-up course CS-A1120) or CS-A1111/CS-A1113 Basics in Programming Y1 (follow-up course CS-A1121/ CS-A1123). Students are requested to check the prerequisites of the courses before signing up. Some of the optional courses for the minor subject are quite demanding.
Content and structure of the minor
| Code | Course name | ECTS credits | Period | Year |
|---|---|---|---|---|
| Compulsory programming course, 5 cr: | ||||
| CS-A1120 | Programming 2* | 5 | IV-V | year 1 |
| CS-A1121/ CS-A1123 |
Basics in Programming Y2* | 5 | IV-V | year 1 |
| Compulsory courses, 10 cr: | ||||
| CS-C3160 | Data science | 5 | II | year 2 |
| MS-C1343 | Linear algebra | 5 | I | year 2 |
| Optional courses. Choose 5-10 cr: | ||||
| MS-C1620 | Statistical Inference | 5 | III-IV | |
| MS-C2111 | Stochastic processes | 5 | I | |
| MS-C2128 | Prediction and time-series analysis | 5 | II | |
| CS-E3190 | Principles of Algorithmic Techniques | 5 | I-II | |
| CS-E3210 | Machine Learning: Basic Principles | 5 | I-II | |
| CS-E4640 | Big Data Platforms | 5 | I-II | |
| CS-E4600 | Algorithmic Methods of Data Mining | 5 | I-II | |
| CS-E5710 | Bayesian Data Analysis | 5 | I-II | |
| CS-E4800 | Artificial Intelligence | 5 | III-IV | |
| CS-E4840 | Information Visualization | 5 | IV | |
| CS-E4580 | Programming Parallel Computers | 5 | V |
*Students who have Programming 2 in their basic studies, cannot include Basics in Programming Y2 in the minor. If Programming 2 or Basics in Programming Y2 are placed elsewhere in the degree, students should choose one more optional course instead.