项目背景
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更新时间:2024-10-28
开学时间9月
课程学制1年
学费33900.00/GBP
专业介绍
Become a proficient data scientist with a master’s in Data Science at the University of Southampton.
You’ll study the latest techniques and technologies, including data mining, machine learning, and data visualisation. By the end of your studies, you’ll be able to develop original ideas and solve problems using advanced data science methods. You can apply what you have learned in fields such as data journalism, open government and social media.
Data scientists help organisations handle large amounts of data produced by digital technologies. Southampton is recognised around the world as a leader in many of the topics that form our data science curriculum. We offer one of the only data science master's programmes in the UK that covers every subject needed to become a skilled data scientist in industry, government or academia.
Our module teachers use their latest research to inform their teaching in emerging topics like:
intelligent agents
computational finance
open data innovation
the science of online social networks
deep learning
Module options are flexible, so you can pursue your interests and adapt the programme to prepare you for your ideal career. We’ll also give you access to dedicated project labs, computer workstations, and infrastructure for large-scale analysis.
推荐顾问
世界排名
语言要求
Write
6
21
64
总分
6.5
92
68
学术要求
计算机科学,计算机工程,软件工程,数学/统计学+含编程科目(如MATLAB, Python, Java, C, C++, C#)+数学科目(最好是统计与概率)
课程设置
Mandatory 必修
Data Visualisation:数据可视化
Foundations of Data Science:数据科学基础
Foundations of Machine Learning (MSc):机器学习基础 (MSc)
MSc Project:MSc项目
Machine Learning Technologies (MSc):机器学习技术 (MSc)
Research Methods and Project Preparation:研究方法与项目准备
Optional 选修
Data Visualisation:数据可视化
Foundations of Data Science:数据科学基础
Foundations of Machine Learning (MSc):机器学习基础 (MSc)
MSc Project:MSc项目
Machine Learning Technologies (MSc):机器学习技术 (MSc)
Research Methods and Project Preparation:研究方法与项目准备
Advanced Databases:高级数据库
Advanced Machine Learning:高级机器学习
Algorithmic Game Theory:算法博弈论
Bayesian, Active & Reinforcement Learning:贝叶斯、主动和强化学习
Computational Finance:计算金融
Data Mining:数据挖掘
Deep Learning Technologies:深度学习技术
Differentiable Programming and Deep Learning:可区分编程和深度学习
Evolution of Complexity:复杂性的演变
Knowledge Graphs for AI Systems:人工智能系统的知识图
Mobile Applications Development:移动应用开发
Natural Language Processing (MSc):自然语言处理 (MSc)
Open Data Innovation:开放数据创新
Simulation Modelling for Computer Science:计算机科学的仿真建模
Social Media and Network Science:社交媒体和网络科学