Program Data Science MSc School of Informatics The University of Edinburgh

Tentang jurusan ini

Tentang jurusan ini
- Ikhtisar
- Departemen
Pilihan kuliah
Persyaratan masuk
- Internasional

Apa yang akan saya pelajari?

Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data and the application and use of those principles. Large data sets are now generated by almost every activity in science, society, and commerce - ranging from molecular biology to social media, from sustainable energy to health care.As an MSc Data Science student, you will explore how to efficiently find patterns in these vast streams of data. Many research areas have tackled parts of this problem:Machine learning focuses on finding patterns and making predictions from data.Ideas from algorithms and databases are required to build systems that scale to big data streams.Separate research areas have grown around different types of unstructured data such as text, images, sensor data, video, and speech.

Saya termasuk departemen apa?

School of Informatics

Pilihan kuliah

Purna Waktu (1 tahun)

Biaya kuliah

£45.410,00 (Rp 918.949.429) per tahun

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Tanggal mulai

September 2027

Tempat

Main Campus

Old College, South Bridge,

Edinburgh,

EH8 9YL, Scotland, United Kingdom

Persyaratan masuk

Untuk mahasiswa internasional

A UK 2:1 honours degree, or its international equivalent, in informatics, artificial intelligence, cognitive science, computer science, econometrics, electrical engineering, linguistics, mathematics, philosophy, physics, psychology or another quantitative discipline.

Entry to this programme is competitive. A typical offer will normally require a UK first class honours degree.

You should have experience of computer programming equivalent to an introductory programming course and have completed the equivalent of 60 SCQF credits or 30 ECTS credits of mathematics during your degree that have typically covered the following subjects/topics: calculus (differentiation and integration), linear algebra (vectors and multi-dimensional matrices), discrete mathematics and mathematical reasoning (e.g. induction and reasoning, graph theoretic models, proofs), and probability (concepts in discrete and continuous probabilities, Markov chains etc.)

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