Data Science

Programme overview

3 years
Full-time Duration
19 900 EUR Tuition Fee/Year
180 ECTS ECTS
English Language of Instruction
All Year Round Application Period

The Data Science BSc programme sets out to develop the skills needed to cut through the deluge of data we’re dealing with on a global scale. Students learn to cut through the noise and employ automated analytical tools to create useful knowledge out of big data.

See full curriculum Programme structure

year

In the first year, students obtain the foundational theoretical knowledge they need to become data scientists. The programme builds the mathematical basis upon which students will develop understanding of programming, statistics, machine learning and data management during following years. The courses are mostly given in a form of lectures and takeaway coursework.

Topics

Combinatorics & Graphs - 1
Foundations of Programming: C/C++
Calculus – 1
Foundations of Mathematical Logic
Linear Algebra -1
Algorithms and Data Structures – 1
Combinatorics and Graphs - 2
Object Oriented Programming: Python
Calculus – 2
Algorithms and Data Structures – 2
Linear Algebra -2
Computer Organisation and Systems
Combinatorics and Graphs - 3
Operating Systems
Calculus - 3
Capstone Project - 1
Seminars & Workshops - 1

year

In the second year, students learn programming, statistics and machine learning in addition to courses that will further establish the mathematical foundations they need in data science.

The second year also contains courses that start covering tremendously useful data science tools as well as technical writing instruments. Most courses require practical coursework and a course project enabling students to get a feel for the challenges and approaches used in this field. The students will also begin developing software for the Capstone project.

By the end of this year, students will be able to write programmes, use primary data science tools and conduct data analysis and will be ready to study applied courses during the final year of the programme.

Topics

Probability Theory
Introduction to Statistics
Java Programming
Practical Unix
Parallel and Distributed Computing
Introduction to Optimization
Machine Learning – 1
R, Matlab, SPSS
Stochastic Processes
Introduction to Computer Networking
Database Systems Principles
Computational Complexity Theory
Introduction to Cryptography
Convex Optimization
Python for Massive Data Analysis
Capstone Project - 2
Seminars & Workshops - 2

year

During the third, the final year, students will complete their studies of programming and data analysis and will primarily focus on applications of data science. The programme offers many of practical and interdisciplinary courses. The courses are taught by researchers and professionals who practice the courses they teach either academically or by sharing their professional experiences in their field.

Topics

Information Theory
MapReduce
Parallel and Disrtibuted Computing
Machine Learning – 2
Stochastic and Hugescale Optimization
Bioinformatics
Big Data & Emerging Technologies
Performance Oriented Computing
Text Mining
Software Development Process
Computational Genomics
Image Analysis
Technical Project Management
Web-graphs
Data Visualization
Neural Networks
Leadership and Group Dynamics
Writing, Documentation, TeX, JavaDoc, Academic
Introduction to Interaction Design
Capstone Project - 3
Seminars & Workshops - 3

2 years
Full-time Duration
22 900 EUR Tuition Fee/Year
120 ECTS ECTS
English Language of Instruction
All Year Round Application Period

The data science is a new frontier of human knowledge and a new domain of discovery. Data scientists have the analytical and programming skills needed to extract valuable knowledge out of data. The burgeoning technology sector is quickly becoming the epicentre for data science.

The MSc programme is designed for those who desire to deepen their comprehension of all aspects of the data science. Applicants could be graduates from other degrees with a strong mathematical core, or those continuing their academic pursuit after achieving a BSc in data science.

See full curriculum Programme structure

year

Students begin the programme with foundational knowledge of programming and mathematics, including data structures and algorithms, statistics and machine learning. During the first year their knowledge of mathematics, programming and data analysis will be significantly extended. The programme also offers the opportunity to obtain key soft skills for the professional world including technical project management, writing and presenting. Finally, students are expected to attend a substantial amount of talks and workshops offered by the university, as well as working on the Capstone project.

Topics

Combinatorics And Graphs
Object-Oriented Programming (C++)
Data Structures and Algorithms
Databases
Theory of Probability and Statistics
Practical Unix
Introduction to Interaction Design
Discrete Optimization
Master's Machine Learning
Python
Networks
Java Programming
Big Data Analysis/Machine Learning - 2
R
Convex Optimization
Leadership and Group Dynamics
Technical Writing and Presenting
Сomplexity Theory
Technical Project Management
Nonlinear Optimization
Statistical Data Analysis
Capstone Project - 1
Seminars & Workshops - 1

year

During the second year of the programme students will primarily focus on learning key applications of the data science as well as advanced methods in mathematics and data analysis. A significant part of the year will be allocated to the completion of the Capstone project. Through completion of the programme, students will learn to conduct data analysis on any scale, develop the software necessary for analysis and present the results in a professional and efficient ways.

Topics

Parallel and Distributed Computing
Statistical Data Analysis - 2
Software Design
Stochastic and Huge-scale Optimization
Foundations of Cryptography
Map Reduce
Distributed Databases
Text Mining
Game Theory
Neural Networks and Deep Learning
Social Network Analysis
Time Series
Robust Optimization
Image and Video Analysis - 1
Information Retrieval
Auctions
Statistical Data Analysis - 3
Information Theory
Image and Video Analysis - 2
Machine Translation
Data Visualization
Algorithms in Bioinformatics
Spectral Graph Analysis and Data Science Applications
Web Graphs
Capstone Project - 2
Seminars & Workshops - 2

Programme leadership

KONSTANTIN MERTSALOV

Faculty Leader Data Science

Konstantin Mertsalov is European Director of Development at Rational Enterprise, a globally leading software development company specialising in enterprise information management.

Originally from Russia, he moved to New York in 1998 to study Computer Science and Applied Mathematics , and continued his academic career with a Rensselaer Polytechnic Institute PhD on large dynamic social networks. He's an expert on machine learning, information diffusion in social network, semantic web search, unstructured data, big data and data analytics in general. He developed U Rank, a search engine that allows people to organise, edit and annotate search results as well as share information. Konstantin aims to lead the Harbour.Space Data Science programme with unbridled enthusiasm about the relatively new field, and he’s determined to use his industry knowledge to share, teach and create for the future with his students.

ANDREI RAIGORODSKII

Faculty Leader Data Science

DSci of Physics and Mathematics Andrei Raigorodskii is a professor of Department of Mathematical Statistics and Stochastic Processes, Faculty of Mechanics and Mathematics at the Lomonosov Moscow State University, Chair of Department of Discrete Mathematics and Chair of the Data Science Bachelor Programme at the Moscow Institute of Physics and Technology Faculty of Innovations and Advanced Technology, professor of the joint Bachelor Programme of the New Economic School and Higher School of Economics, and professor of Discrete Analysis, Probability Theory, and Graphs at the Yandex Data Analysis School alongside his faculty leadership at Harbour.Space.

He is editor-in-Chief of the Moscow Journal of Combinatorics and Number Theory. He was awarded the prize for breakthroughs in a number of fields in discrete mathematics and their practical applications in 2011. Andrei published more than 100 scientific papers, articles and books. He also founded a summer school of Combinatorics and Algorithms for senior undergraduate students. Andrei has been working with Yandex (4th largest search engine globally), dedicating himself to the practical application of methods he developed in modelling problems in the internet and other complex networks. His research at Yandex is focused on information retrieval, relevance of the retrieved information in relation to search parameters and the structure of spam documents. These results have greatly improved the quality of the Yandex search engine. As Data Science Faculty Leader at Harbour.Space, Andrei aspires to breed next generation of internationally recognised data scientists who are capable to meet every single possible challenge in the digital era.

Career path

Every career looks different: it depends on individual aspirations as a well as changes in the industry. We've selected some stories to illustrate and celebrate the diversity of the various career paths.

Junior Data Scientist

This is a typical starter role for data science BSc graduates. The position requires strong knowledge of mathematics with an emphasis on statistics and machine learning as well as on concepts related to programming and databases. As part of a team, junior data scientists begin gaining first hand experience with industrial data analysis while learning from more senior colleagues.
Data Scientist

Data scientists combine a mathematical background and practical experience with domain expertise which enables them to conduct advanced data analysis necessary to make predictions that facilitate good business decisions. The job requires an understanding of both mathematics and business objectives. Data scientists need to be technical experts with the competencies necessary to handle large volumes of diverse data as well as possess strong programming skills.
Senior Data Scientist

A senior data scientist becomes a link between the core of the business management and the entire analysis team. The role requires the ability to communicate effectively, in order to both grasp business goals and to communicate the insights brought to them by the analysts. Senior data scientists also direct relevant programming and application-building projects, as well as the formulation of experiment design and they have a leading role within a data science team.
Principal Data Scientist

A principal data scientist is a data visionary who finds ways to answer business questions by analysing available data within the enterprise and on the internet. The job often requires the creation of new methods of analysis and working at the intersection between industry and academia.
Chief Data Officer

A chief data officer is a business leader who handles responsibilities at the intersection of data analysis, information technology and business strategy. The CDO determines the long- term direction for controlling risks and generating value from information governance as well as data management and analysis. Most importantly, the CDO is an agent of change uncovering new levels of efficiency by reporting in-depth insights based on knowledge harvested from data.

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Data Science

Programme overview

Faculty Data Science

Programme leadership

Career path

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Blog Data Science

Data Science

Programme overview

Faculty Data Science

Programme leadership

Career path

Blog Data Science

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