2020年兰卡斯特大学数据科学硕士入学条件及兰卡斯特大学数据科学硕士实习就业是申请留学的同学十分关心的话题,下面指南者留学详细整理2020年兰卡斯特大学数据科学硕士入学条件及实习就业相关信息供大家参考,其中包括2020年兰卡斯特大学数据科学硕士专业中文名、专业英文名、学制、学费、入学时间、授课语言、地理位置、课程领域、申请费、课程介绍、申请材料、课程设置、就业等。
专业介绍
英国兰卡斯特大学数据科学硕士数据科学硕士教授给学生数据科学领域的专业知识与实用技能。数据科学硕士适合那些对数据分析,计算机和应用程序交叉学习感兴趣的学生。拥有理科学士学位且学习内容是科学,技术,工程和数学的学生是很受欢迎的。
英国兰卡斯特大学数据科学硕士数据科学硕士的学制为一年。数据科学硕士包含两个研究方向,学生可以专攻与统计相关的领域,或者是计算机领域。除了课堂授课与论文研究外,还包括企业实习或机构研究。
What Will You Study
This MSc programme is aimed at students who are interested in the cross-over between data analysis, computing and application. Students could, for example, hold a BSc in Mathematics and Statistics, a BSc in Ecology or Environmental Sciences, or a BSc in Computing, but backgrounds in other STEM (science, technology, engineering or maths) subjects are welcome to apply.
Within the programme, two specialism routes are available, statistical inference and Computing.
Statistical Inference Specialism
A data scientist is a highly skilled individual with the ability to: articulate a research question; gather, process and model data at large-scale (developing scalable algorithms for performing inference and modelling complex and heterogeneous data structures) and to disseminate research findings in context.
This specialism provides a thorough training in data science fundamentals but with added focus upon statistical modelling and inference. Students will gain thorough training in computing, data science technologies, statistical inference, statistical modelling and applied data analysis. The MSc will involve a mixture of taught modules and a research dissertation with a placement in industry or with a research organisation.
Computing Specialism
Underpinning the data scientist role are the technologies that enable the processing of data at large-scale, often using parallel processing paradigms.
This specialism provides the training to understand how these technologies function and how they are implemented within both enterprise and research environments. Students will get hands-on experience of building, from scratch, large-scale systems that enable data science questions to be answered, using techn