University of Cambridge｜MPhil Data Intensive Science。项目库类目：数据科学 · 理科。
下面的学制、学费、入学时间、语言要求与课程设置均来自该校该项目本身，不与其他学校同一名称的项目共用。
QS 2027 · 第 6 位
学位 硕士 · 学制 1年 · 学费 41064英镑/年 · 入学 10月 · 15 门课程
📖 项目简介
The course responds to the growing:
demand for highly trained research scientists to design and implement data analysis pipelines for the increasingly large and complex data sets produced by the next generation of scientific experiments;
societal demand for data science and data analysis skills in the industry, especially when applied in strategic domains (science, health) and economic areas (finance, e-commerce);
need to train postgraduate students with a deep understanding of data science techniques and algorithm building for modern computer architectures and utilising industry best practices for software development;
importance of open science in research, specifically reproducibility of scientific results and the creation of public data analytic codes.
The course aims:
to give students deep knowledge of data science tools and techniques, including machine learning and AI, and the statistical frameworks underpinning them.
to give students knowledge and practical experience of modern software best practice and collaborative code development.
to give students an understanding and practical experience of the application of modern data-science methodologies in specific scientific domains utilising industry best practice.
to give students an awareness of the wide range of industrial applications of data science more broadly.
以上为剑桥大学该项目官网项目页原文（英文），本站未做改写或补写。
📋 项目参数
数据密集型科学哲学硕士
MPhil Data Intensive Science
硕士理科
学制1年
学费41064英镑/年
入学时间10月
语言要求雅思 7（W 7 / S 7 / L 7 / R 6.5） · 托福 100（W 25 / S 25 / L 25 / R 25）
就业方向教育机构、研究机构、出版业、文化机构、非营利组织
岗位分布研究员、教师、编辑、文化顾问、社区工作者
📚 课程设置（15 门，主修课程 6 / 辅修课程 9）
主修课程6 门
数据密集型科学的统计方法Statistical Methods for Data Intensive Science
数据密集型科学的机器学习Machine Learning for Data Intensive Science
数据密集型科学的研究计算Research Computing for Data Intensive Science
数据密集型科学的高级统计方法Advanced Statistical Methods for Data Intensive Science
数据密集型科学的深度学习Deep Learning for Data Intensive Science
数据密集型科学的高性能计算High Performance Computing for Data Intensive Science
辅修课程9 门
数据科学在宇宙学中的应用Applications of Data Science to Cosmology
SKA时代的数据驱动天文学Data Driven Astronomy in the SKA era
近场宇宙学的银河考古学Galactic Archaeology for Near-field Cosmology
数据科学在引力波中的应用Applications of Data Science to Gravitational Waves
数据科学在高能物理中的应用Applications of Data Science to High Energy Physics
图像分析Image analysis
数据科学在医学成像中的应用Applications of Data Science to Medical Imaging
多智能体系统和人工智能Multi-Agent Systems and Agentic AI
天气和气候科学的机器学习Machine Learning for Weather and Climate Science
✨ 项目特色
该项目官网「Unique Program Features」原文，说的是这个项目自身的特点（学制长短、资助、实践安排、认证等），不是学校层面的通用介绍。
The taught element comprises two module types; major modules which cover all essential aspects required for scientific data analysis, and minor modules which demonstrate the application of these data science skills to real-world scientific research areas.
Candidates will be required to take seven modules in total for assessment comprising 5 major and 2 minor modules.
These will be chosen from: • a selection of (minimum 6) Major modules • a selection of (minimum 4) Minor modules
The major modules will be taught in the Michaelmas and Lent terms. They will cover the main material that all students are expected to master, covering three topic areas: statistical data analysis, machine learning and AI , and research computing, high performance computing, and software development. These major modules will equip students with the essential skills for top-level scientific data analysis.
The minor modules will be taught in the Lent and Easter terms. The selection of minor modules offered will be updated annually to respond to changes in the research landscape. These will cover the application of the core techniques taught in the major modules to specific scientific research problems. Beyond illustrating useful data science methodology that may have broader applications, the minor modules are also intended to give insight into the goals and status of the relevant field and hence can help in preparing interested students for research in one of these areas.
🎯 入学要求
官网给出的录取条件原文，含学校统一要求与该项目的附加要求；语言成绩的具体分数线请到项目官网核对。
Applicants for this course should have achieved a Good II.i Honours Degree .
If your degree is not from the UK, please check International Qualifications to find the equivalent in your country.
Your first degree should be in science or a technology discipline. You are also expected to demonstrate strong mathematical aptitude and prior training in core areas of mathematics, including linear algebra, calculus and statistics. You should also demonstrate sufficient training to engage with the scientific content of at least 2 minor modules.
University Minimum Academic Requirements
Score : Grade A & B (overall score of 193, with no element lower than 185 plus a Language Centre assessment)
Score : Grade A, B, or C (with at least 200 with no element lower than 185).
该项目为硕士 · 数据科学，官网介绍：The course responds to the growing:
剑桥大学数据密集型科学哲学硕士的学制是多久？
该校官网公布为 1年（全日制）。
剑桥大学数据密集型科学哲学硕士的学费是多少？
41064英镑/年，为该校官网公布的项目学费。
剑桥大学数据密集型科学哲学硕士什么时候入学？
该校官网公布 10月 入学。
剑桥大学数据密集型科学哲学硕士的语言成绩要求是什么？
雅思 7（W 7 / S 7 / L 7 / R 6.5） · 托福 100（W 25 / S 25 / L 25 / R 25）。
剑桥大学数据密集型科学哲学硕士的入学要求是什么？
Applicants for this course should have achieved a Good II.i Honours Degree . If your degree is not from the UK, please check International Qualifications to find the equivalent in your country.