The Data Science MSc provides advanced technical and practical skills in the collection, collation, curation and analysis of data. This is an ideal study pathway for graduates with a background in quantitative subjects, or who possess relevant work experience, who want to gain experience in current methods and techniques of data science.
数据科学理学硕士在收集，整理，分类和分析数据方面提供先进的技术和实践技能。对于具有定量学科背景或具有相关工作经验，想获得当前数据科学方法和技术经验的毕业生来说，这是理想的学习途径。
You will gain an in-depth understanding of the general principles of the computational and statistical approaches and methods used in data science, as well as their underlying assumptions and limitations.
您将深入了解数据科学中使用的计算和统计方法和方法的一般原理，以及它们的基本假设和局限性。
Rooted in the renowned research strengths of the Department of Informatics, this course enables you to study a wide variety of topics in advanced computing while allowing you to specialise through your choice of modules. You will learn advanced practical techniques and implementation skills for solving complex computational problems.
本课程以信息学系著名的研究实力为根基，使您可以研究高级计算中的各种主题，同时可以通过选择模块来进行专业化研究。您将学习解决复杂的计算问题的高级实用技术和实施技巧。
2
课程安排
Required Modules必修课程
Computer Programming for Data Scientists (15 credits) or Big Data Technologies (15 credits)
数据科学家计算机编程（15个学分）或大数据技术（15个学分）
Databases, Data Warehousing & Information Retrieval (15 credits)
数据库，数据仓库和信息检索（15个学分）
Statistics for Data Analysis (15 credits) or Elements of Statistical Learning (15 credits) or Statistics in Finance (15 credits)
数据分析统计学（15学分）或统计学习要素（15学分）或金融统计学（15学分）
Data Mining (15 credits)
数据挖掘（15个学分）
Individual Project (60 credits)
个人项目（60学分）
Optional Modules选修课程
You are required to take 60 credits from a range of optional modules, which may typically include:您需要从一系列可选模块中获得60个学分，这些模块通常包括：
Elements of Statistical Learning (15 credits)
统计学习要素（15学分
Statistics in Finance (15 credits)
金融统计（15学分）
Agents & Multi-Agent Systems (15 credits) *
代理和多代理系统（15个学分）*
Nature-Inspired Learning Algorithms (15 credits) *
自然启发式学习算法（15个学分）*
Pattern Recognition, Neural Networks & Deep Learning (15 credits) *
模式识别，神经网络和深度学习（15个学分）*
Network Theory (15 credits)
网络理论（15学分）
Big Data Technologies (15 credits)
大数据技术（15学分）
Simulation & Data Visualisation (15 credits)
仿真和数据可视化（15个学分）
Computer Vision (15 credits)
计算机视觉（15学分）
Telling Stories with Data (15 credits)
用数据讲故事（15个学分）
You will also be able to select up to 30 credits form a range of level 6 modules, which may typically include:
您还将能够从一系列的6级模块中选择最多30个学分，这些模块通常包括：
Machine Learning (15 credits) *
机器学习（15个学分）*
Optimisation Methods (15 credits) *
优化方法（15个学分）*
Artificial Intelligence Reasoning and Planning (15 credits) *
人工智能推理与计划（15个学分）*
3
入学要求
A Bachelor’s degree with 2:1 (Upper Second Class) honours is required in one of the following subject areas:
必须在以下学儿科领域中具有2：1（高级二等）荣誉的学士学位：
Computer Science, or Computing计算机科学或计算
Mathematics, Pure Mathematics, Mathematical Statistics or Statistics数学，纯数学，数理统计学或统计学
Physics物理学
Natural Science自然科学
Electronic Engineering电子工程
Geographic Information Systems地理信息系统
Operations Research运筹学
In order to meet the academic entry requirements for this programme you should have a minimum 2:1 undergraduate degree with a final mark of at least 60% or above in the UK marking scheme. If you are still studying you should be achieving an average of at least 60% or above in the UK marking scheme.
为满足课程的学术入学要求，您应至少具有2：1本科学位，并且在英国成绩评定计划中的最终成绩至少达到60％或以上。如果您仍在学习，那么您在英国的评分标准中应该至少达到60％或以上。
语言要求
学术雅思6.5， 小分不低于6.0
PTE 62，各部分不低于59
托福总分92，写作不低于23，其他部分不低于20
如申请语言班，语言班最终成绩不得低于6.5
4
学费
Full time overseas fees: 26,550 per year (/21)
全日制国际生：每年26550英镑（约RMB 242032.455元）
When you receive an offer for this course you will be required to pay a non-refundable deposit to secure your place. The deposit will be credited towards your total fee payment.
当您收到此课程的录取通知书时，您将需要支付不予退还的押金以确保您的名额。押金将记入您的总费用中。
The INTERNATIONAL deposit is 2,000.
国际生押金为2000英镑。
