Master's Degree in Big Data in Espoo in Finland

See Masters Programs in Big Data 2017 in Espoo in Finland

Big Data

A masters refers to the completion of a graduate study program that prepares students to further their knowledge of a specific subject or advance their careers. The majority of masters are granted by state or public universities.

A student who does well in advanced mathematics and computer science may choose to study big data. This field deals with large data sets that must be analyzed as a unit instead of as individual points. There are applications for both the public and private sectors.

The formal higher education system of Finland is the 7-3-2-2 pattern. The university takes two years in vocational or technical education and finally two-year pre-tertiary education. Finland also has several public as well as private universities that have enabled accessibility of higher education to students.

Espoo is the 2nd largest city in Finland. It is an urban municipality with over 200,000 residents. It is home to Aalto University and a branch campus of Helsinki University of Technology.

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Master of Science in Computer, Communication and Information Sciences - Signal, Speech and Language Processing

Aalto University
Campus Full time 2 years August 2017 Finland Espoo

The purpose of the major is to provide the students with basics of either signal processing or speech and language processing and the ability to apply those in various fields of science and technology. [+]

Masters in Big Data in Espoo in Finland. Study programme The purpose of the major is to provide the students with basics of either signal processing or speech and language processing and the ability to apply those in various fields of science and technology. Students focusing in Signal Processing are given a strong theoretical background of modern signal processing. This means a toolbox of knowledge on signals and systems modelling, representation through transforms, systems optimization and implementation. Some emphasis is on the most recent research priorities in the field of signal processing in domains of data analysis, compression and storage, communications as well as in representation of signals. In addition, students can obtain even deeper understanding of signal processing and adjacent sciences, or apply signal processing in other fields. Interesting applications include radar systems and networks, data transmission, sensing and tracking of objects and spaces, as well as analysis of technical (machine based) and social (human based) networks. The cyber level of the smart power grid is increasingly important for efficient energy distribution and utilization, offering a platform for applying signal processing methodology for solving essential problems of great societal impact. Students focusing in Speech and Language Processing are provided basics of that field and the ability to apply those in various fields of science and technology. Speech and language processing utilizes signal processing, mathematical modeling and machine learning for statistical language modeling, information retrieval and speech analysis, synthesis, recognition and coding. Applications and research priorities have recently been, for example, speech recognition and synthesis, dictation, subtitling, machine translation, language learning, large-scale video data indexing and retrieval, speech coding and quality improvement in mobile phones and networks as well as in medical research of the human voice. Tuition fees and scholarships Non-EU/EEA students selected in the application round 2017 will be charged tuition fees. Admission requirements General admission requirements The general eligibility requirements are the same for all Master's programmes in the field of science and technology. Please see also the requirements concerning language skills. Eligible applications are then sent to the academic evaluation described in more detail below. Programme-specific admission requirements Applicants to the programme must first meet the general eligibility and language requirements that are common to all Master’s programmes in Science and Technology. Admission criteria to the CCIS programme is a high quality Bachelor’s degree in computer science, software engineering, communications engineering, or electrical engineering. Excellent candidates with degrees in other fields such as information systems, engineering, natural sciences, mathematics or physics will be considered if they have sufficient studies and proven skills and knowledge in the required areas. Required background: mathematics (linear algebra, calculus, probability theory, statistics) programming skills algorithms and data structures software engineering signal processing Knowledge of the following areas is considered an advantage: speech and language processing pattern recognition machine learning project and team work Application documents In addition to the compulsory application documents, the applicants are requested to provide the following, additional documents: at least one original recommendation letter (preferably academic) course descriptions of courses taken in relevant subject areas (see the subject list above) work certificates and certificates of other relevant achievements copies of any publications official transcript of records for other university studies which are not included in the mandatory part of the application GRE or GMAT test results, if available The application should explain full educational history of the applicant. Applicant’s motivation letter (compulsory part of the online application form) should be written in English. For purposes of study guidance, the applicants are asked to indicate already on the motivation letter which track they wish to follow. Also additional application documents described above (recommendations letter(s), course descriptions, work or other certificates, and publications) should preferably be submitted in English. If some other language than English, Finnish or Swedish is used in them, the applicant must provide precise, word-for-word translations of them. [-]

Master in Data Science (DSC)

EIT Digital Master School
Campus Full time 2 years September 2017 Finland Espoo

Data abounds: social media, manufacturing systems, medical devices, logistic services, and countless others generate petabytes of data on a daily basis. With a wealth of data available, we are at a point in history, where we can conduct analyses to detect, discover, and, ultimately, better understand the world around us. [+]

Why study Data Science? Data abounds: social media, manufacturing systems, medical devices, logistic services, and countless others generate petabytes of data on a daily basis. With a wealth of data available, we are at a point in history, where we can conduct analyses to detect, discover, and, ultimately, better understand the world around us. What are the carier opportunities for Data Science graduates? Become a professional for a career in a highly innovative area: data science. The profession in Data Science is hailed as the “… Sexiest Job of the 21st Century,” by Harvard Business Review in October 2012. The Data Scientist is a professional who simultaneously possesses breadth and depth in scalable data management, data analysis, and domain area expertise, and who is capable of solving real-world problems. This is an opportune time to pursue training in both a challenging and rewarding new field. Join us and embark on a journey of a lifetime! Why Data Science at EIT Digital? Getting value, meaning and answering big questions, are the ultimate goals of Learning Data Science at EIT Digital. Mazen Aly Data Science Master student Eindhoven University of Technology Study Data Science at EIT Digital, where education is provided by renowned universities and where entrepreneurial data scientists of the future are come to being! Farideh Heidari Data Science Coordinator What is Data Science Master at EIT Digital all about? The newly established Data Science Master’s offers a unique academic programme, whereby students can study data science, innovation, and entrepreneurship at leading European universities. In this programme, students will learn about scalable data collection techniques, data analysis methods, and a suite of tools and technologies that address data capture, processing, storage, transfer, analysis, and visualization, and related concepts (e.g., data access, pricing data, and data privacy). How is the programme structured? The first year will be similar at all three DSc entry point universities: Universidad Politecnica de Madrid (UPM), Eindhoven University of Technology (TU/e), and Universite Nice Sophia Antipolis (UNS) with foundations courses, such as data handling, data analysis, advanced data analysis and data management, visualization, and applications. The second year will enable students to concentrate one of six technical specialisation areas of their own choosing. See: specialisations below. An important part of the programme are the Innovation and Entrepreneurship (I&E) courses. The I&E basics course provides an introduction to business & management. Students participating in the DSc track are offered an internship with an industry partner or research centre of the EIT Digital to work on their thesis project. Directly linked to the master thesis is the I&E minor thesis that specifies the requirements, strategy and business plan for the selected thesis project. Students participating in the DSc track are offered an internship with an industry partner or research centre of the EIT-Digital to work on their thesis project. Directly linked to the master thesis is the I&E minor thesis that specifies the requirements, strategy and business plan for the selected thesis project. Where can I study if I choose Data Science? Entry - 1st year Eindhoven University of Technology (TU/e) Universidad Politecnica de Madrid (UPM) Universite Nice Sophia Antipolis (UNS) Exit - 2nd year, specialisation Internet of Things (IoT) at UPM Multimedia and Web Science for Big Data at UNS Process Mining at High Tech Systems, Healthcare, Visual Analytics or Big software at TU/e Distributed Systems and Data Mining for Really Big Data at KTH Design, Implementation, and Usage of Data Science Instruments at TUB Specific Admission Requirements A B.Sc. degree in electrical engineering/ electronics, computer engineering, computer science or information technology is required. The studies should include at least 60 ECTS courses in computer science, computer architecture, or programming, and mathematics including calculus, algebra and mathematical statistics. [-]