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.
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.
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.
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.
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.
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.
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.
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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.
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.
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