Responsible Professor: Aristides Gionis
School: School of Science

Curriculum by timing

Orange: basic studiesGreen: major

First year

* A defined exercise group for data science students.
** Suggested Aalto course for data science students.

Second year

Data structures and algorithmsDatabases
Linear AlgebraStatistical inference
Data scienceArtificial Intelligence
Elective / Minor courseTheoretical Computer Science
Elective / Minor courseMajor optional
Elective / Minor courseElective / Minor course

Third year

Data Science ProjectBachelor's Thesis and Seminar
Principles of Algorithmic TechniquesElective / Minor course
Major optionalElective / Minor course
Elective / Minor courseElective / Minor course
Elective / Minor courseElective / Minor course

Curriculum by modules

Basic studies

Code: SCI3031.A

Extent: 65 ECTS

CodeNameECTS creditsPeriod
Mathematics 25 cr:


Differential and integral calculus 15I
MS-A0011 Matrix algebra5II
MS-A0503First course in probability and statistics5III
MS-A0402Foundations of discrete mathematics5IV
MS-C2105Introduction to optimization5IV
Programming 25 cr:
CS-A1110Programming 15I-II
CS-A1120Programming 25IV-V
CS-C2120Programming Studio 2: Project5III-IV
CS-A1140Data structures and algorithms5I-II

General studies 10 cr:
SCI-A1010Introduction Course for Bachelor's students2I-V

Compulsory Language Course*3I-II
LC-5001/ LC-7001

National Language Requirement (Swedish/Finnish), writing test

LC-5002/ LC-7002National Language Requirement (Swedish/Finnish), oral test*1III

Aalto Course**

Industrial Engineering and Management 5 cr:

Introduction to Industrial Engineering and Management


* For more information on language requirements, please see Compulsory language studies.
** An Aalto course worth 3 cr is a compulsory part of basic studies. The aim is to provide all students with sufficient understanding of the opportunities offered by Aalto University. The aim is also to support interactions among students from different fields of study and to inspire mobility. Course MS-C1001 Shapes in action is recommended. More suitable courses can be found in Into


Code: SCI3095

Credits: 65 ECTS

CodeNameECTS creditsPeriod
Compulsory courses, 50 cr:
MS-C1343Linear algebra5I
MS-C1620Statistical inference5III-IV
CS-E3190Principles of Algorithmic Techniques5I-II
CS-C3160Data science5II
CS-C2150Theoretical Computer Science5III-IV
CS-E4800Artificial Intelligence5III-IV

Data science project/
ADDBasics Capstone Course

Bachelor's Thesis and Seminar10III-IV

Maturity Test0
Optional courses, choose 15 cr:
CS-C1000Introduction to Artificial Intelligence (suggested)3IV
MS-C2111Stochastic processes5I
MS-C2128Prediction and time-series analysis5II
MS-E1651Numerical matrix computations5II
MS-E2112Multivariate statistical analysis5III-IV
MS-A0211Differential and integral calculus 25IV
CS-C3100Computer Graphics5I-II
CS-C3120Human-Computer Interaction5I-II
CS-E3210Machine Learning: Basic Principles5I-II
CS-E4640Big Data Platforms5I-II
CS-E4600Algorithmic Methods of Data Mining5I-II
CS-E5710Bayesian Data Analysis5I-II
CS-E5795Computational Methods in Stochastics5I-II
CS-E4840Information visualization5IV
CS-E4580Programming Parallel Computers5V
ELEC-E5422Convex Optimization5I
ELEC-E5431Large Scale Data Analysis5III-IV
CS-E5120Introduction to Digital Business and Venturing3I

Learning outcomes of the Data Science major

 Expand to

Data science is a multidisciplinary field focusing on the study of scientific methods for extracting knowledge and insights from data. Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information sciences, and computer science.

During their studies in the Data Science BSc major, students will acquire skills for developing their problem-solving abilities using data-driven approaches. After completing their studies, students will be able to tackle real-world problems using powerful methods of machine learning, data mining, and artificial intelligence. Students will be able to manage efficiently large volumes of data and develop data-driven models for different applications. They will also be proficient in making sound inferences from data and making sense of big data.

The students will acquire the following skills related to the computational foundations of Data Science:
 programming, data structures, algorithms, machine learning and data mining, artificial intelligence, data management, visualization, network analysis, and more.

The students will acquire the following skills related to the mathematical foundations of Data Science:
 discrete mathematics, multivariate calculus, linear algebra, matrix analysis, optimization, mathematics of computation, and more.

The students will acquire the following skills related to the statistical foundations of Data Science:
 probability and statistics,
 statistical inference, empirical data analysis, statistical modeling, prediction and time-series, design of experiments, and more.

Emphasis will be given to studying the foundations of mathematics, statistics, and computation, and building strong background in Data Science, so that students can follow the developments in this rapidly-evolving domain in the years to come.

Additionally, the students will obtain extensive hands-on experience and they will participate in team projects, so as to develop their technical skills, learn to work in a team, and learn to bring theory into practice.