Data Science
ProgrammeWhile the world holds its breath in anticipation of the Age of the Petabyte, we’re getting ready for the Age of the Exabyte. The Data Science programme is the first dedicated BSc programme in the world.
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.
- 1
- 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
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- 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
- 2
- 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.
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- 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
- 3
- 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.
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- 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.
- 1
- 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
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- 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
- 2
- 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
More
- 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
Faculty Data Science
We're delighted to introduce some of our excellent teachers. Keep track of more news about who's coming to share their expertise at Harbour.Space on our Twitter and Facebook.
Lecturer at Technopark@Mail.Ru
Founder of the Moscow Spark tech community
Co-author of “Big Data for Data Engineers” Coursera specialization
kikh Senior Software Engineer at Yandex
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.
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Junior Data ScientistThis 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.
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Data ScientistData 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.
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Senior Data ScientistA 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.
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Principal Data ScientistA 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.
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Chief Data OfficerA 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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140,000 – 190,000 more deep analytical talent positions needed.
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As a Data Scientist, you should be experienced with and passionate about using data to drive strategy and product
recommendations who is able to “crunch the numbers” one minute and critically think through strategic issues the next.
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At Alphabet, data drives the way we make decisions. We're able to transform insights into real-world
products.
Blog Data Science
Free Format Friday: Meet Shannon Bering
Humility and humour go hand-in-hand for Shannon Bering. "I feel reluctant calling what I make 'art' because I’ve seen real art and what I do is nowhere near it", she says, as she produces an illustration that leaves her peers in awe.
November 16 Pranav Joy, Content Officer, Harbour.Space University
Tech students stories
At the forefront of a new educational journey with Harbour.Space, three of our Technical students shared their experiences and thoughts on studying at our university.
August 04 Simge Su Toksoz, Interaction Design Student
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