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Analytics and Data Science

Basic information of the minor

Code: SCI3073

Extent:

  • 22 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 module, please contact Study Coordinator Anu Kuusela (This email address is being protected from spambots. You need JavaScript enabled to view it.). The student needs to have confirmed her/his personal study plan (HOPS) before registering for the module.

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 module is composed of elective courses in four subareas:

SF: Statistical foundations
CM: Computational methods
BA: Business analytics
AP: Applications

Subarea

Code

Name

Credits

Period

1) COMPULSORY COURSE

 

ICS-E4010

Introduction to Analytics and Data Science

2

I

2) AT LEAST ONE COURSE FROM THE SF SUBAREA

 SF BECS-E2601

Bayesian Data Analysis

5 I-II

SF

MS-C2104

Introduction to Statistical Inference (in Finnish only)

5

III-IV

SF

MS-C2128

Prediction and Time Series Analysis (in Finnish only) *

5

II

SF

30E00800

Time Series Analysis*

6

IV-V

3) AT LEAST ONE COURSE FROM THE CM SUBAREA

CM

T-61.3050

Machine Learning: Basic Principles

5

I-II

CM

T-61.5060

Algorithmic Methods of Data Mining

5

I-II

CM

T-61.5010

Information Visualization

5

IV

CM

CSE-E5430

Scalable Cloud Computing

5

I-II

 CM

T-110.5121

Mobile Cloud Computing

5

I-II

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

57E00500

Capstone: Business Intelligence

6

I

BA

31E00920

Applied Microeconomics II

 6  IV

BA

31E40100

History of Growth and Economic Crisis

 6  IV

AP

Becs-114.4150

Complex Networks

5

II

AP

Becs-E4101

Mathematical Modeling of Social Dynamics

5

II (2015)

AP

Maa-123.3585

Spatial Data Mining

3-5

V

AP

Maa-123.3530

Visual Analysis

4

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

T-61.5910

Science: Research project in analytics and data science

 5-10  I-II

* 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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